Category Archives: Data Analytics

The Know It All

As fraud examiners intimately concerned with the general on-going state of health of fraud management and response systems, we find ourselves constantly looking at the integrity of the data that’s truly the life blood of today’s client organizations.  We’re constantly evaluating the network of anti-fraud controls we hope will help keep those pesky, uncontrolled, random data vulnerabilities to a minimum.   Every little bit of critical information that gets mishandled or falls through the cracks, every transaction that doesn’t get recorded, every anti-fraud policy or procedure that’s misapplied has some effect on the client’s overall fraud management picture. 

When it comes to managing its client, financial and payment data, almost every organization has a Pauline.  Pauline’s the person everyone goes to get the answers about data, and the state of the system(s) that process it, that no one else in her unit ever seems to have.  That’s because Pauline is an exceptional employee with years of detailed hands-on-experience in daily financial system operations and maintenance.  Pauline is also an example of the extraordinary level of dependence that many organizations have today on a small handful of their key employees.   The great recession of past memory where enterprises relied on retaining the experienced employees they had rather than on traditional hiring and cross-training practices only exacerbated a still existing, ever growing trend.  The very real threat to the fraud management system that the Pauline’s of the corporate data world pose is not so much that they will commit fraud themselves (although that’s an ever present possibility) but that they will retire or get another job out of state, taking their vital knowledge of the company systems and data with them. 

The day after Pauline’s retirement party and, to an increasing degree thereafter, it will dawn on  Pauline’s unit management that it’s lost a large amount of valuable information about the true state of its data and financial processing system(s), of its total lack of a large amount of system critical data documentation that’s been carried around nowhere but in Jane’s head.  The point is that, for some organizations, their reliance on a few key employees for day to day, operationally related information on their data goes well beyond what’s appropriate and constitutes an unacceptable level of risk to their fraud prevention system.  Today’s newspapers and the internet are full of stories about data breeches, only reinforcing the importance of vulnerable data and of its documentation to the on-going operational viability of our client organizations. 

Anyone whose investigated frauds involving large scale financial systems (insurance claims, bank records, client payment information) is painfully aware that when the composition of data changes (field definitions or content) surprisingly little of that change related information is ever formally documented.  Most of the information is stored in the heads of some key employees, and those key employees aren’t necessarily the ones involved in everyday, routine data management projects.  There’s always a significant level of detail that’s gone undocumented, left out or to chance, and it becomes up to the analyst of the data (be s/he an auditor, a management scientist, a fraud examiner or other assurance professional) to find the anomalies and question them.  The anomalies might be in the form of missing data, changes in data field definitions, or change in the content of the fields; the possibilities are endless.  Without proper, formal documentation, the immediate or future significance of these types of anomalies for the fraud management systems and for the overall fraud risk assessment process itself become almost impossible to determine.   

If our auditor or fraud examiner, operating under today’s typical budget or time constraints,  is not very thorough and misses even finding some of these anomalies, they can end up never being addressed.   How many times as an analyst have you tried to explain something (like apparently duplicate transactions) about the financial system that just doesn’t look right only to be told, “Oh, yeah.  Pauline made that change back in February before she retired; we don’t have too many details on it.”  In other words, undocumented changes to transactions and data, details of which are now only existent in Pauline’s head.  When a data driven system is built on incomplete information, the system can be said to have failed in its role as a component of overall fraud management.  The cycle of incomplete information gets propagated to future decisions, and the cost of the missing or inadequately explained data can be high.  What can’t be seen, can’t ever be managed or even explained. 

It’s truly humbling for any practitioner to experience how much critical financial information resides in the fading (or absent) memories of past or present key employees.  As fraud examiners we should attempt to foster a culture among our clients supportive of the development of concurrent transaction related documentation and the sharing of knowledge on a consistent basis for all systems but especially in matters involving changes to critical financial systems.  One nice benefit of this approach, which I brought to the attention of one of my clients not too long ago, would be to free up the time of one of these key employees to work on more productive fraud control projects rather than constantly serving as the encyclopedia for the rest of the operational staff. 

Fraud Detection-Fraud Prevention

One of our CFE chapter members left us a contact comment asking whether concurrent fraud auditing might not be a good fraud prevention tool for use by a retailer client of hers that receives hundreds of credit card payments for services each day. The foundational concepts behind concurrent fraud auditing owe much to the idea of continuous assurance auditing (CAA) that internal auditors have applied for years; I personally applied the approach as an essential tool throughout by carrier as a chief audit executive (CAE). Basically, the heart of a system of concurrent fraud auditing (CFA) like that of CAA is the process of embedding control based software monitors in real time, automated financial or payment systems to alert reviewers of transactional anomalies in as close to their occurrence as possible. Today’s networked/cloud based processing environments have made the implementation and support of such real time review approaches operationally feasible in ways that the older, batch processing based environments couldn’t.

Our member’s client uses several on-line, cloud based services to process its customer payments; these services provide our member’s client with a large database full of payment history, tantamount to a data warehouse, all available for use on SQL server, by in-house client IT applications like Oracle and SAP. In such a data rich environment, CFE’s and other assurance professionals can readily test for the presence of transactional patterns characteristic of defined, common payment fraud scenarios such as those associated with identity theft and money laundering. The objective of the CFA program is not necessarily to recover the dollars associated with on-line frauds but to continuously (in as close to real time as possible) adjust the edits in the payment collection and processing system so that certain fraudulent transactions (those associated with known fraud scenarios) stand a greater chance of not even getting processed in the first place. Over time, the CFA process should get better and better at editing out or flagging the anomalies associated with your defined scenarios.

The central concept of any CFA system is that of an independent application monitoring for suspected fraud related activity through, for example (as with our Chapter member), periodic (or even real time) reviews of the cloud based files of an automated payment system. Depending upon the degree of criticality of the results of its observations, activity summaries of unusual items can be generated with any specified frequency and/or highlighted to an exception report folder and communicated to auditors via “red flag” e-mail notices. At the heart of the system lies a set of measurable, operational metrics or tags associated with defined fraud scenarios. The fraud prevention team would establish the metrics it wishes to monitor as well as supporting standards for those metrics. As a simple example, the U.S. has established anti-money-laundering banking rules specifying that all transactions over $10,000 must be reported to regulators. By experience, the $10,000 threshold is a fraud related metric investigators have found to be generic in the identification of many money-laundering fraud scenarios. Anti-fraud metric tags could be built into the cloud based financial system of our Chapter member’s client to monitor in real time all accounts payable and other cash transfer transactions with a rule that any over $10,000 would be flagged and reviewed by a member of the audit staff. This same process could have multiple levels of metrics and standards with exceptions fed up to a first level assurance process that could monitor the outliers and, in some instances, send back a correcting feedback transaction to the financial system itself (an adjusting or corrective edit or transaction flag). The warning notes that our e-mail systems send us that our mailboxes are full are another example of this type of real time flagging and editing.

Yet other types of discrepancies would flow up to a second level fraud monitoring or audit process. This level would produce pre-formatted reports to management or constitute emergency exception notices. Beyond just reports, this level could produce more significant anti-fraud or assurance actions like the referral of a transaction or group of transactions to an enterprise fraud management committee for consideration as documentation of the need for an actual future financial system fraud prevention edit. To continue the e-mail example, this is where the system would initiate a transaction to prevent future mailbox accesses to an offending e-mail user.

There is additionally yet a third level for our system which is to use the CFA to monitor the concurrent fraud auditing process itself. Control procedures can be built to report monitoring results to external auditors, governmental regulators, the audit committee and to corporate council as documented evidence of management’s performance of due diligence in its fight against fraud.

So I would encourage our member CFE to discuss the CFA approach with the management of her client. It isn’t the right tool for everyone since such systems can vary greatly in cost depending upon the existing processing environment and level of IT sophistication of the implementing organization. CFA’s are particularly useful for monitoring purchase and payment cycle applications with an emphasis on controls over customer and vendor related fraud. CFA is an especially useful tool for any financial application where large amounts of cash are either coming in or going out the door (think banking applications) and to control all aspects of the processing of insurance claims.

Analytic Reinforcements

Rumbi’s post of last week on ransomware got me thinking on a long drive back from Washington about what an excellent tool the AICPA’s new Cybersecurity Risk Management Reporting Framework is, not only for CPAs but for CFEs as well as for all our client organizations. As the seemingly relentless wave of cyberattacks continues with no sign of let up, organizations are under intense pressure from key stakeholders and regulators to implement and enhance their cyber security and fraud prevention programs to protect customers, employees and all the types of valuable information in their possession.

According to research from the ACFE, the average total cost per company, per event of a data breach is $3.62 million. Initial damage estimates of a single breach, while often staggering, may not take into account less obvious and often undetectable threats such as the theft of intellectual property, espionage, destruction of data, attacks on core operations or attempts to disable critical infrastructure. These effects can knock on for years and have devastating financial, operational and brand impact ramifications.

Given the present broad regulatory pressures to tighten cyber security controls and the visibility surrounding cyberrisk, a number of proposed regulations focused on improving cyber security risk management programs have been introduced in the United States over the past few years by our various governing bodies. One of the more prominent is a regulation by the New York Department of Financial Services (NYDFS) that prescribes certain minimum cyber security standards for those entities regulated by the NYDFS. Based on an entity’s risk assessment, the NYDFS law has specific requirements around data encryption and including data protection and retention, third-party information security, application security, incident response and breach notification, board reporting, and required annual re-certifications.

However, organizations continue to report to the ACFE regarding their struggle to systematically report to stakeholders on the overall effectiveness of their cyber security risk management programs. In response, the AICPA in April of last year released a new cyber security risk management reporting framework intended to help organizations expand cyberrisk reporting to a broad range of internal and external users, to include management and the board of directors. The AICPA’s new reporting framework is designed to address the need for greater stakeholder transparency by providing in-depth, easily consumable information about the state of an organization’s cyberrisk management program. The cyber security risk management examination uses an independent, objective reporting approach and employs broader and more flexible criteria. For example, it allows for the selection and utilization of any control framework considered suitable and available in establishing the entity’s basic cyber security objectives and in developing and maintaining controls within the entity’s cyber security risk management program irregardless of whether the standard is the US National Institute of Standards and Technology (NIST)’s Cybersecurity Framework, the International Organization for standardization (ISO)’s ISO 27001/2 and related frameworks, or even an internally developed framework based on a combination of sources. The examination is voluntary, and applies to all types of entities, but should be considered by CFEs as a leading practice that provides management, boards and other key stakeholders with clear insight into the current state of an organization’s cyber security program while identifying gaps or pitfalls that leave organizations vulnerable to cyber fraud and other intrusions.

What stakeholders might benefit from a client organization’s cyber security risk management examination report? Clearly, we CFEs as we go about our routine fraud risk assessments; but such a report, most importantly, can be vital in helping an organization’s board of directors establish appropriate oversight of a company’s cyber security risk program and credibly communicate its effectiveness to stakeholders, including investors, analysts, customers, business partners and regulators. By leveraging this information, boards can challenge management’s assertions around the effectiveness of their cyberrisk management and fraud prevention programs and drive more effective decision making. Active involvement and oversight from the board can help ensure that an organization is paying adequate attention to cyberrisk management and displaying due diligence. The board can help shape expectations for reporting on cyberthreats while also advocating for greater transparency and assurance around the effectiveness of the program.

The cyber security risk management report in its initial and follow-up iterations can be invaluable in providing overview guidance to CFEs and forensic accountants in targeting both fraud prevention and fraud detection/investigative analytics. We know from our ACFE training that data analytics need to be fully integrated into the investigative process. Ensuring that data analytics are embedded in the detection/investigative process requires support from all levels, starting with the managing CFE. It will be an easier, more coherent process for management to support such a process if management is already supporting cyber security risk management reporting. Management will also have an easier time reinforcing the use of analytics generally, although the data analytics function supporting fraud examination will still have to market its services, team leaders will still be challenged by management, and team members will still have to be trained to effectively employ the newer analytical tools.

The presence of a robust cyber security risk management reporting process should also prove of assistance to the lead CFE in establishing goals for the implementation and use of data analytics in every investigation, and these goals should be communicated to the entire investigative team. It should be made clear to every level of the client organization that data analytics will support the investigative planning process for every detected fraud. The identification of business processes, IT systems, data sources, and potential analytic routines should be discussed and considered not only during planning, but also throughout every stage of the entire investigative engagement. Key in obtaining the buy-in of all is to include investigative team members in identifying areas or tests that the analytics group will target in support of the field work. Initially, it will be important to highlight success stories and educate managers and team leaders about what is possible. Improving on the traditional investigative approach of document review, interviewing, transaction review, etc. investigators can benefit from the implementation of data analytics to allow for more precise identification of the control deficiencies, instances of noncompliance with policies and procedures, and mis-assessment of areas of high risk that contributed to the development of the fraud in the first place. These same analytics can then be used to ensure that appropriate post-fraud management follow-up has occurred by elevating the identified deficiencies to the cyber security risk management reporting process and by implementing enhanced fraud prevention procedures in areas of higher fraud risk. This process would be especially useful in responding to and following up data breaches.

Once patterns are gathered and centralized, analytics can be employed to measure the frequency of occurrence, the bit sizes, the quantity of files executed and average time of use. The math involved allows an examiner to grasp the big picture. Individuals, including examiners, are normally overwhelmed by the sheer volume of information, but automation of pattern recognizing techniques makes big data a tractable investigative resource. The larger the sample size, the easier it is to determine patterns of normal and abnormal behavior. Network haystacks are bombarded by algorithms that can notify the CFE information archeologist about the probes of an insider threat for example.

Without analytics, enterprise-level fraud examination and risk assessment is a diminished discipline, limited in scope and effectiveness. Without an educated investigative workforce, armed with a programing language for automation and an accompanying data-mining philosophy and skill set, the control needs of management leaders at the enterprise level will go unmet; leaders will not have the data needed for fraud prevention on a large scale nor a workforce that is capable of getting them that data in the emergency following a breach or penetration.

The beauty of analytics, from a security and fraud prevention perspective, is that it allows the investigative efforts of the CFE to align with the critical functions of corporate business. It can be used to discover recurring risks, incidents and common trends that might otherwise have been missed. Establishing numerical baselines on quantified data can supplement a normal investigator’s tasks and enhance the auditor’s ability to see beneath the surface of what is presented in an examination. Good communication of analyzed data gives decision makers a better view of their systems through a holistic approach, which can aid in the creation of enterprise-level goals. Analytics and data mining always add dimension and depth to the CFE’s examination process at the enterprise level and dovetail with and are supported beautifully by the AICPA’s cyber security risk management reporting initiative.

CFEs should encourage the staffs of client analytics support functions to possess …

–understanding of the employing enterprise’s data concepts (data elements, record types, database types, and data file formats).
–understanding of logical and physical database structures.
–the ability to communicate effectively with IT and related functions to achieve efficient data acquisition and analysis.
–the ability to perform ad hoc data analysis as required to meet specific fraud examiner and fraud prevention objectives.
–the ability to design, build, and maintain well-documented, ongoing automated data analysis routines.
–the ability to provide consultative assistance to others who are involved in the application of analytics.

Forensic Data Analysis

As a long term advocate of big data based solutions to investigative challenges, I have been interested to see the recent application of such approaches to the ever-growing problem of data beaches. More data is stored electronically than ever before, financial data, marketing data, customer data, vendor listings, sales transactions, email correspondence, and more, and evidence of fraud can be located anywhere within those mountains of data. Unfortunately, fraudulent data often looks like legitimate data when viewed in the raw. Taking a sample and testing it might not uncover fraudulent activity. Fortunately, today’s fraud examiners have the ability to sort through piles of information by using special software and data analysis techniques. These methods can identify future trends within a certain industry, and they can be configured to identify breaks in audit control programs and anomalies in accounting records.

In general, fraud examiners perform two primary functions to explore and analyze large amounts of data: data mining and data analysis. Data mining is the science of searching large volumes of data for patterns. Data analysis refers to any statistical process used to analyze data and draw conclusions from the findings. These terms are often used interchangeably. If properly used, data analysis processes and techniques are powerful resources. They can systematically identify red flags and perform predictive modeling, detecting a fraudulent situation long before many traditional fraud investigation techniques would be able to do so.

Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. Simply put, big data is information of extreme size, diversity, and complexity. In addition to thinking of big data as a single set of data, fraud investigators and forensic accountants are conceptualizing about the way data grow when different data sets are connected together that might not normally be connected. Big data represents the continuous expansion of data sets, the size, variety, and speed of generation of which makes it difficult for investigators and client managements to manage and analyze.

Big data can be instrumental to the evidence gathering phase of an investigation. Distilled down to its core, how do fraud examiners gather data in an investigation? They look at documents and financial or operational data, and they interview people. The challenge is that people often gravitate to the areas with which they are most comfortable. Attorneys will look at documents and email messages and then interview individuals. Forensic accounting professionals will look at the accounting and financial data (structured data). Some people are strong interviewers. The key is to consider all three data sources in unison.

Big data helps to make it all work together to bring the complete picture into focus. With the ever-increasing size of data sets, data analytics has never been more important or useful. Big data requires the use of creative and well-planned analytics due to its size and complexity. One of the main advantages of using data analytics in a big data environment is that it allows the investigator to analyze an entire population of data rather than having to choose a sample and risk drawing erroneous conclusions in the event of a sampling error.

To conduct an effective data analysis, a fraud examiner must take a comprehensive approach. Any direction can (and should) be taken when applying analytical tests to available data. The more creative fraudsters get in hiding their breach-related schemes, the more creative the fraud examiner must become in analyzing data to detect these schemes. For this reason, it is essential that fraud investigators consider both structured and unstructured data when planning their engagements.

Data are either structured or unstructured. Structured data is the type of data found in a database, consisting of recognizable and predictable structures. Examples of structured data include sales records, payment or expense details, and financial reports. Unstructured data, by contrast, is data not found in a traditional spreadsheet or database. Examples of unstructured data include vendor invoices, email and user documents, human resources files, social media activity, corporate document repositories, and news feeds. When using data analysis to conduct a fraud examination, the fraud examiner might use structured data, unstructured data, or a combination of the two. For example, conducting an analysis on email correspondence (unstructured data) among employees might turn up suspicious activity in the purchasing department. Upon closer inspection of the inventory records (structured data), the fraud examiner might uncover that an employee has been stealing inventory and covering her tracks in the record.

Recent reports of breach responses detailed in social media and the trade press indicate that those investigators deploying advanced forensic data analysis tools across larger data sets provided better insights into the penetration, which lead to more focused investigations, better root cause analysis and contributed to more effective fraud risk management. Advanced technologies that incorporate data visualization, statistical analysis and text-mining concepts, as compared to spreadsheets or relational database tools, can now be applied to massive data sets from disparate sources enhancing breach response at all organizational levels.

These technologies enable our client companies to ask new compliance questions of their data that they might not have been able to ask previously. Fraud examiners can establish important trends in business conduct or identify suspect transactions among millions of records rather than being forced to rely on smaller samplings that could miss important transactions.

Data breaches bring enhanced regulatory attention. It’s clear that data breaches have raised the bar on regulators’ expectations of the components of an effective compliance and anti-fraud program. Adopting big data/forensic data analysis procedures into the monitoring and testing of compliance can create a cycle of improved adherence to company policies and improved fraud prevention and detection, while providing additional comfort to key stakeholders.

CFEs and forensic accountants are increasingly being called upon to be members of teams implementing or expanding big data/forensic data analysis programs so as to more effectively manage data breaches and a host of other instances of internal and external fraud, waste and abuse. To build a successful big data/forensic data analysis program, your client companies would be well advised to:

— begin by focusing on the low-hanging fruit: the priority of the initial project(s) matters. The first and immediately subsequent projects, the low-hanging investigative fruit, normally incurs the largest cost associated with setting up the analytics infrastructure, so it’s important that the first few investigative projects yield tangible results/recoveries.

— go beyond usual the rule-based, descriptive analytics. One of the key goals of forensic data analysis is to increase the detection rate of internal control noncompliance while reducing the risk of false positives. From a technology perspective, client’s internal audit and other investigative groups need to move beyond rule-based spreadsheets and database applications and embrace both structured and unstructured data sources that include the use of data visualization, text-mining and statistical analysis tools.

— see that successes are communicated. Share information on early successes across divisional and departmental lines to gain broad business process support. Once validated, success stories will generate internal demand for the outputs of the forensic data analysis program. Try to construct a multi-disciplinary team, including information technology, business users (i.e., end-users of the analytics) and functional specialists (i.e., those involved in the design of the analytics and day-to-day operations of the forensic data analysis program). Communicate across multiple departments to keep key stakeholders assigned to the fraud prevention program updated on forensic data analysis progress under a defined governance program. Don’t just seek to report instances of noncompliance; seek to use the data to improve fraud prevention and response. Obtain investment incrementally based on success, and not by attempting to involve the entire client enterprise all at once.

—leadership support will gets the big data/forensic data analysis program funded, but regular interpretation of the results by experienced or trained professionals are what will make the program successful. Keep the analytics simple and intuitive; don’t try to cram too much information into any one report. Invest in new, updated versions of tools to make analytics sustainable. Develop and acquire staff professionals with the required skill sets to sustain and leverage the forensic data analysis effort over the long-term.
Finally, enterprise-wide deployment of forensic data analysis takes time; clients shouldn’t be lead to expect overnight adoption; an analytics integration is a journey, not a destination. Quick-hit projects might take four to six weeks, but the program and integration can take one to two years or more.

Our client companies need to look at a broader set of risks, incorporate more data sources, move away from lightweight, end-user, desktop tools and head toward real-time or near-real time analysis of increased data volumes. Organizations that embrace these potential areas for improvement can deliver more effective and efficient compliance programs that are highly focused on identifying and containing damage associated with hacker and other exploitation of key high fraud-risk business processes.

Needles & Haystacks

A long-time acquaintance of mine told me recently that, fresh out of the University of Virginia and new to forensic accounting, his first assignment consisted in searching, at the height of summer, through two unairconditioned trailers full of thousands of savings and loan records for what turned out to be just two documents critical to proving a loan fraud. He told me that he thought then that his job would always consist of finding needles in haystacks. Our profession and our tools have, thankfully, come a long way since then!

Today, digital analysis techniques afford the forensic investigator the ability to perform cost-effective financial forensic investigations. This is achieved through the following:

— The ability to test or analyze 100 percent of a data set, rather than merely sampling the data set.
–Massive amounts of data can be imported into working files, which allows for the processing of complex transactions and the profiling of certain case-specific characteristics.
–Anomalies within databases can be quickly identified, thereby reducing the number of transactions that require review and analysis.
–Digital analysis can be easily customized to address the scope of the engagement.

Overall, digital analysis can streamline investigations that involve a large number of transactions, often turning a needle-in-the-haystack search into a refined and efficient investigation. Digital analysis is not designed to replace the pick-and-shovel aspect of an investigation. However, the proper application of digital analysis will permit the forensic operator to efficiently identify those specific transactions that require further investigation or follow up.

As every CFE knows, there are an ever-growing number of software applications that can assist the forensic investigator with digital analysis. A few such examples are CaseWare International Inc.’s IDEA, ACL Services Ltd.’s ACL Desktop Edition, and the ActiveData plug-in, which can be added to Excel.

So, whether using the Internet in an investigation or using software to analyze data, fraud examiners can today rely heavily on technology to aid them in almost any investigation. More data is stored electronically than ever before; financial data, marketing data, customer data, vendor listings, sales transactions, email correspondence, and more, and evidence of fraud can be located within that data. Unfortunately, fraudulent data often looks like legitimate data when viewed in the raw. Taking a sample and testing it might or might not uncover evidence of fraudulent activity. Fortunately, fraud examiners now have the ability to sort through piles of information by using special software and data analysis techniques. These methods can identify future trends within a certain industry, and they can be configured to identify breaks in audit control programs and anomalies in accounting records.

In general, fraud examiners perform two primary functions to explore and analyze large amounts of data: data mining and data analysis. Data mining is the science of searching large volumes of data for patterns. Data analysis refers to any statistical process used to analyze data and draw conclusions from the findings. These terms are often used interchangeably.

If properly used, data analysis processes and techniques are powerful resources. They can systematically identify red flags and perform predictive modeling, detecting a fraudulent situation long before many traditional fraud investigation techniques would be able to do so.

Big data is now a buzzword in the worlds of business, audit, and fraud investigation. Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. Simply put, big data is information of extreme size, diversity, and complexity.

In addition to thinking of big data as a single set of data, fraud investigators should think about the way data grow when different data sets are connected together that might not normally be connected. Big data represents the continuous expansion of data sets, the size, variety, and speed of generation of which makes it difficult to manage and analyze.

Big data can be instrumental to fact gathering during an investigation. Distilled down to its core, how do fraud examiners gather data in an investigation? We look at documents and financial or operational data, and we interview people. The challenge is that people often gravitate to the areas with which they are most comfortable. Attorneys will look at documents and email messages and then interview individuals. Forensic accounting professionals will look at the accounting and financial data (structured data). Some people are strong interviewers. The key is to consider all three data sources in unison. Big data helps to make it all work together to tell the complete picture. With the ever-increasing size of data sets, data analytics has never been more important or useful. Big data requires the use of creative and well-planned analytics due to its size and complexity. One of the main advantages of using data analytics in a big data environment is, as indicated above, that it allows the investigator to analyze an entire population of data rather than having to choose a sample and risk drawing conclusions in the event of a sampling error.

To conduct an effective data analysis, a fraud examiner must take a comprehensive approach. Any direction can (and should) be taken when applying analytical tests to available data. The more creative fraudsters get in hiding their schemes, the more creative the fraud examiner must become in analyzing data to detect these schemes. For this reason, it is essential that fraud investigators consider both structured and unstructured data when planning their engagements.
Data are either structured or unstructured. Structured data is the type of data found in a database, consisting of recognizable and predictable structures. Examples of structured data include sales records, payment or expense details, and financial reports.

Unstructured data, by contrast, is data not found in a traditional spreadsheet or database. Examples of unstructured data include vendor invoices, email and user documents, human resources files, social media activity, corporate document repositories, and news feeds.

When using data analysis to conduct a fraud examination, the fraud examiner might use structured data, unstructured data, or a combination of the two. For example, conducting an analysis on email correspondence (unstructured data) among employees might turn up suspicious activity in the purchasing department. Upon closer inspection of the inventory records (structured data), the fraud examiner might uncover that an employee has been stealing inventory and covering her tracks in the records.

Data mining has roots in statistics, machine learning, data management and databases, pattern recognition, and artificial intelligence. All of these are concerned with certain aspects of data analysis, so they have much in common; yet they each have a distinct and individual flavor, emphasizing particular problems and types of solutions.

Although data mining technologies provide key advantages to marketing and business activities, they can also manipulate financial data that was previously hidden within a company’s database, enabling fraud examiners to detect potential fraud.

Data mining software provides an easy to use process that gives the fraud examiner the ability to get to data at a required level of detail. Data mining combines several different techniques essential to detecting fraud, including the streamlining of raw data into understandable patterns.

Data mining can also help prevent fraud before it happens. For example, computer manufacturers report that some of their customers use data mining tools and applications to develop anti-fraud models that score transactions in real-time. The scoring is customized for each business, involving factors such as locale and frequency of the order, and payment history, among others. Once a transaction is assigned a high-risk score, the merchant can decide whether to accept the transaction, deny it, or investigate further.

Often, companies use data warehouses to manage data for analysis. Data warehouses are repositories of a company’s electronic data designed to facilitate reporting and analysis. By storing data in a data warehouse, data users can query and analyze relevant data stored in a single location. Thus, a company with a data warehouse can perform various types of analytic operations (e.g., identifying red flags, transaction trends, patterns, or anomalies) to assist management with its decision making responsibilities.

In conclusion, after the fraud examiner has identified the data sources, s/he should identify how the information is stored by reviewing the database schema and technical documentation. Fraud examiners must be ready to face a number of pitfalls when attempting to identify how information is stored, from weak or nonexistent documentation to limited collaboration from the IT department.

Moreover, once collected, it’s critical to ensure that the data is complete and appropriate for the analysis to be performed. Depending on how the data was collected and processed, it could require some manual work to make it usable for analysis purposes; it might be necessary to modify certain field formats (e.g., date, time, or currency) to make the information usable.

Fraud Prevention Oriented Data Mining

One of the most useful components of our Chapter’s recently completed two-day seminar on Cyber Fraud & Data Breaches was our speaker, Cary Moore’s, observations on the fraud fighting potential of management’s creative use of data mining. For CFEs and forensic accountants, the benefits of data mining go much deeper than as just a tool to help our clients combat traditional fraud, waste and abuse. In its simplest form, data mining provides automated, continuous feedback to ensure that systems and anti-fraud related internal controls operate as intended and that transactions are processed in accordance with policies, laws and regulations. It can also provide our client managements with timely information that can permit a shift from traditional retrospective/detective activities to the proactive/preventive activities so important to today’s concept of what effective fraud prevention should be. Data mining can put the organization out front of potential fraud vulnerability problems, giving it an opportunity to act to avoid or mitigate the impact of negative events or financial irregularities.

Data mining tests can produce “red flags” that help identify the root cause of problems and allow actionable enhancements to systems, processes and internal controls that address systemic weaknesses. Applied appropriately, data mining tools enable organizations to realize important benefits, such as cost optimization, adoption of less costly business models, improved program, contract and payment management, and process hardening for fraud prevention.

In its most complex, modern form, data mining can be used to:

–Inform decision-making
–Provide predictive intelligence and trend analysis
–Support mission performance
–Improve governance capabilities, especially dynamic risk assessment
–Enhance oversight and transparency by targeting areas of highest value or fraud risk for increased scrutiny
–Reduce costs especially for areas that represent lower risk of irregularities
–Improve operating performance

Cary emphasized that leading, successful organizational implementers have tended to take a measured approach initially when embarking on a fraud prevention-oriented data mining initiative, starting small and focusing on particular “pain points” or areas of opportunity to tackle first, such as whether only eligible recipients are receiving program funds or targeting business processes that have previously experienced actual frauds. Through this approach, organizations can deliver quick wins to demonstrate an early return on investment and then build upon that success as they move to more sophisticated data mining applications.

So, according to ACFE guidance, what are the ingredients of a successful data mining program oriented toward fraud prevention? There are several steps, which should be helpful to any organization in setting up such an effort with fraud, waste, abuse identification/prevention in mind:

–Avoid problems by adopting commonly used data mining approaches and related tools.

This is essentially a cultural transformation for any organization that has either not understood the value these tools can bring or has viewed their implementation as someone else’s responsibility. Given the cyber fraud and breach related challenges faced by all types of organizations today, it should be easier for fraud examiners and forensic accountants to convince management of the need to use these tools to prevent problems and to improve the ability to focus on cost-effective means of better controlling fraud -related vulnerabilities.

–Understand the potential that data mining provides to the organization to support day to day management of fraud risk and strategic fraud prevention.

Understanding, both the value of data mining and how to use the results, is at the heart of effectively leveraging these tools. The CEO and corporate counsel can play an important educational and support role for a program that must ultimately be owned by line managers who have responsibility for their own programs and operations.

–Adopt a version of an enterprise risk management program (ERM) that includes a consideration of fraud risk.

An organization must thoroughly understand its risks and establish a risk appetite across the enterprise. In this way, it can focus on those area of highest value to the organization. An organization should take stock of its risks and ask itself fundamental questions, such as:

-What do we lose sleep over?
-What do we not want to hear about us on the evening news or read about in the print media or on a blog?
-What do we want to make sure happens and happens well?

Data mining can be an integral part of an overall program for enterprise risk management. Both are premised on establishing a risk appetite and incorporating a governance and reporting framework. This framework in turn helps ensure that day-to-day decisions are made in line with the risk appetite, and are supported by data needed to monitor, manage and alleviate risk to an acceptable level. The monitoring capabilities of data mining are fundamental to managing risk and focusing on issues of importance to the organization. The application of ERM concepts can provide a framework within which to anchor a fraud prevention program supported by effective data mining.

–Determine how your client is going to use the data mined information in managing the enterprise and safeguarding enterprise assets from fraud, waste and abuse.

Once an organization is on top of the data, using it effectively becomes paramount and should be considered as the information requirements are being developed. As Cary pointed out, getting the right data has been cited as being the top challenge by 20 percent of ACFE surveyed respondents, whereas 40 percent said the top challenge was the “lack of understanding of how to use analytics”. Developing a shared understanding so that everyone is on the same page is critical to success.

–Keep building and enhancing the application of data mining tools.

As indicated above, a tried and true approach is to begin with the lower hanging fruit, something that will get your client started and will provide an opportunity to learn on a smaller scale. The experience gained will help enable the expansion and the enhancement of data mining tools. While this may be done gradually, it should be a priority and not viewed as the “management reform initiative of the day. There should be a clear game plan for building data mining capabilities into the fiber of management’s fraud and breach prevention effort.

–Use data mining as a tool for accountability and compliance with the fraud prevention program.

It is important to hold managers accountable for not only helping institute robust data mining programs, but for the results of these programs. Has the client developed performance measures that clearly demonstrate the results of using these tools? Do they reward those managers who are in the forefront in implementing these tools? Do they make it clear to those who don’t that their resistance or hesitation are not acceptable?

–View this as a continuous process and not a “one and done” exercise.

Risks change over time. Fraudsters are always adjusting their targets and moving to exploit new and emerging weaknesses. They follow the money. Technology will continue to evolve, and it will both introduce new risks but also new opportunities and tools for management. This client management effort to protect against dangers and rectify errors is one that never ends, but also one that can pay benefits in preventing or managing cyber-attacks and breaches that far outweigh the costs if effectively and efficiently implemented.

In conclusion, the stark realities of today’s cyber related challenges at all levels of business, private and public, and the need to address ever rising service delivery expectations have raised the stakes for managing the cost of doing business and conducting the on-going war against fraud, waste and abuse. Today’s client-managers should want to be on top of problems before they become significant, and the strategic use of data mining tools can help them manage and protect their enterprises whilst saving money…a win/win opportunity for the client and for the CFE.

Finding the Words

I had lunch with a long-time colleague the other day and the topic of conversation having turned to our May training event next week, he commented that when conducting a fraud examination, he had always found it helpful to come up with a list of words specifically associated with the type of fraud scenario on which he was working.  He found the exercise useful when scanning through the piles of textual material he frequently had to plow through during complex examinations.

Data analysis in the traditional sense involves running rule-based queries on structured data, such as that contained in transactional databases or financial accounting systems. This type of analysis can yield valuable insight into potential frauds. But, a more complete analysis requires that fraud examiners (like my friend) also consider unstructured textual data. Data are either structured or unstructured. Structured data is the type of data found in a database, consisting of recognizable and predictable structures. Examples of structured data include sales records, payment or expense details, and financial reports. Unstructured data, by contrast, is data that would not be found in a traditional spreadsheet or database. It is typically text based.

Our client’s employees are sending and receiving more email messages each year, retaining ever more electronic source documents, and using more social media tools. Today, we can anticipate unstructured data to come from numerous sources, including:

• Social media posts
• Instant messages
• Videos
• Voice files
• User documents
• Mobile phone software applications
• News feeds
• Sales and marketing material
• Presentations

Textual analytics is a method of using software to extract usable information from unstructured text data. Through the application of linguistic technologies and statistical techniques, including weighted fraud indicators (e.g., my friend’s fraud keywords) and scoring algorithms, textual analytics software can categorize data to reveal patterns, sentiments, and relationships indicative of fraud. For example, an analysis of email communications might help a fraud examiner gauge the pressures/incentives, opportunities, and rationalizations to commit fraud that exist in a client organization.

According to my colleague, as a prelude to textual analytics (depending on the type of fraud risk present in a fraud examiner’s investigation), the examiner  will frequently profit by coming up with a list of fraud keywords that are likely to point to suspicious activity. This list will depend on the industry of the client, suspected fraud schemes, and the data set the fraud examiner has available. In other words, if s/he is running a search through journal entry detail, s/he will likely search for different fraud keywords than if s/he were running a search of emails. It might be helpful to look at the ACFE’s fraud triangle when coming up with a keyword list. The factors identified in the triangle are helpful when coming up with a fraud keyword list. Consider how someone in the entity under investigation might have the opportunity to commit fraud, be under pressure to commit fraud, or be able to rationalize the commission of fraud.

Many people commit fraud because of something that has happened in their life that motivates them to steal. Maybe they find themselves in debt, or perhaps they must meet a certain goal to qualify for a performance-based bonus. Keywords that might indicate pressure include deadline, quota, trouble, short, problem, and concern. Think of words that would indicate that someone has the opportunity or ability to commit fraud. Examples include override, write-off, recognize revenue, adjust, discount, and reserve/provision.

Since most fraudsters do not have a criminal background, justifying their actions is a key part of committing fraud. Some keywords that might indicate a fraudster is rationalizing his actions include reasonable, deserve, and temporary.

So, even though the concepts embodied in the fraud triangle are a good place to start when developing a keyword list, it’s also important to consider the nature of the client entity’s industry and the types of payments it makes or is suspected of making. Think about the fraud scenarios that are likely to have occurred. Does the entity do a significant amount of work overseas or have many contractors? If so, there might be an elevated risk of bribery. Focus on the payment text descriptions in journal entries or in work delated documentation, since no one calls it “bribe expense.” Some examples of word combinations in payment descriptions that might merit special attention include:

• Goodwill payment
• Consulting fee
• Processing fee
• Incentive payment
• Donation
• Special commission
• One-time payment
• Special payment
• Friend fee
• Volume contract incentive

Any payment descriptions bearing these, or similar terms warrant extra scrutiny to check for reasonableness. Also, examiners should always be wary of large cash disbursements that have a blank journal payment description.

Beyond key word lists, the ACFE tells us that another way to discover fraud clues hidden in text is to consider the emotional tone of employee correspondence. In emails and instant messages, for instance, a fraud examiner should identify derogatory, surprised, secretive, or worried communications. In one example, former Enron CEO Ken Lay’s emails were analyzed, revealing that as the company came closer to filing bankruptcy, his email correspondence grew increasingly derogatory, confused, and angry. This type of analysis provided powerful evidence that he knew something was wrong at the company.

While advanced textual analytics can be extremely revealing and can provide clues for potential frauds that might otherwise go unnoticed, the successful application of such analytics requires the use of sophisticated software, as well as a thorough understanding of the legal environment of employee rights and workplace searches. Consequently, fraud examiners who are considering adding textual analytics to their fraud detection arsenal should consult with technological and legal experts before undertaking such techniques.

Even with sophisticated data analysis techniques, some data are so vast or complex that they remain difficult to analyze using traditional means. Visually representing data via graphs,  link diagrams, time-series charts, and other illustrative representations can bring clarity to a fraud examination. The utility of visual representations is enhanced as data grow in volume and complexity. Visual analytics build on humans’ natural ability to absorb a greater volume of information in visual rather than numeric form and to perceive certain patterns, shapes, and shades more easily than others.

Link analysis software is used by fraud examiners to create visual representations (e.g., charts with lines showing connections) of data from multiple data sources to track the movement of money; demonstrate complex networks; and discover communications, patterns, trends, and relationships. Link analysis is very effective for identifying indirect relationships and relationships with several degrees of separation. For this reason, link analysis is particularly useful when conducting a money laundering investigation because it can track the placement, layering, and integration of money as it moves around unexpected sources. It could also be used to detect a fictitious vendor (shell company) scheme. For instance, the investigator could map visual connections between a variety of entities that share an address and bank account number to reveal a fictitious vendor created to embezzle funds from a company.  The following are some other examples of the analyses and actions fraud examiners can perform using link analysis software:

• Associate communications, such as email, instant messages, and internal phone records, with events and individuals to reveal connections.
• Uncover indirect relationships, including those that are connected through several intermediaries.
• Show connections between entities that share an address, bank account number, government identification number (e.g., Social Security number), or other characteristics.
• Demonstrate complex networks (including social networks).

Imagine a listing of vendors, customers, employees, or financial transactions of a global company. Most of the time, these records will contain a reference to a location, including country, state, city, and possibly specific street address. By visually analyzing the site or frequency of events in different geographical areas, a fraud investigator has yet another variable with which s/he can make inferences.

Finally, timeline analysis software aids fraud examiners in transforming their data into visual timelines. These visual timelines enable fraud examiners to:

• Highlight key times, dates, and facts.
• More readily determine a sequence of events.
• Analyze multiple or concurrent sequences of events.
• Track unaccounted for time.
• Identify inconsistencies or impossibilities in data.

Analytics Confronts the Normal

The Information Audit and Control Association (ISACA) tells us that we produce and store more data in a day now than mankind did altogether in the last 2,000 years. The data that is produced daily is estimated to be one exabyte, which is the computer storage equivalent of one quintillion bytes, which is the same as one million terabytes. Not too long ago, about 15 years, a terabyte of data was considered a huge amount of data; today the latest Swiss Army knife comes with a 1 terabyte flash drive.

When an interaction with a business is complete, the information from the interaction is only as good as the pieces of data that get captured during that interaction. A customer walks into a bank and withdraws cash. The transaction that just happened gets stored as a monetary withdrawal transaction with certain characteristics in the form of associated data. There might be information on the date and time when the withdrawal happened; there may be information on which customer made the withdrawal (if there are multiple customers who operate the same account). The amount of cash that was withdrawn, the account from which the money was extracted, the teller/ATM who facilitated the withdrawal, the balance on the account after the withdrawal, and so forth, are all typically recorded. But these are just a few of the data elements that can get captured in any withdrawal transaction. Just imagine all the different interactions possible on all the assorted products that a bank has to offer: checking accounts, savings accounts, credit cards, debit cards, mortgage loans, home equity lines of credit, brokerage, and so on. The data that gets captured during all these interactions goes through data-checking processes and gets stored somewhere internally or in the cloud.  The data that gets stored this way has been steadily growing over the past few decades, and, most importantly for fraud examiners, most of this data carries tons of information about the nuances of the individual customers’ normal behavior.

In addition to what the customer does, from the same data, by looking at a different dimension of the data, examiners can also understand what is normal for certain other related entities. For example, by looking at all the customer withdrawals at a single ARM, CFEs can gain a good understanding of what is normal for that particular ATM terminal.  Understanding the normal behavior of customers is very useful in detecting fraud since deviation from normal behavior is a such a primary indicator of fraud. Understanding non-fraud or normal behavior is not only important at the main account holder level but also at all the entity levels associated with that individual account. The same data presents completely different information when observed in the context of one entity versus another. In this sense, having all the data saved and then analyzed and understood is a key element in tackling the fraud threat to any organization.

Any systematic, numbers-based system of understanding of the phenomenon of fraud as a past occurring event is dependent on an accurate description of exactly what happened through the data stream that got accumulated before, during, and after the fraud scenario occurred. Allowing the data to speak is the key to the success of any model-based system. This data needs to be saved and interpreted very precisely for the examiner’s models to make sense. The first crucial step to building a model is to define, understand, and interpret fraud scenarios correctly. At first glance, this seems like a very easy problem to solve. In practical terms, it is a lot more complicated process than it seems.

The level of understanding of the fraud episode or scenario itself varies greatly among the different business processes involved with handling the various products and functions within an organization. Typically, fraud can have a significant impact on the bottom line of any organization. Looking at the level of specific information that is systematically stored and analyzed about fraud in financial institutions for example, one would arrive at the conclusion that such storage needs to be a lot more systematic and rigorous than it typically is today. There are several factors influencing this. Unlike some of the other types of risk involved in client organizations, fraud risk is a censored problem. For example, if we are looking at serious delinquency, bankruptcy, or charge-off risk in credit card portfolios, the actual dollars-at-risk quantity is very well understood. Based on past data, it is relatively straightforward to quantify precise credit dollars at risk by looking at how many customers defaulted on a loan or didn’t pay their monthly bill for three or more cycles or declared bankruptcy. Based on this, it is easy to quantify the amount at risk as far as credit risk goes. However, in fraud, it is virtually impossible to quantify the actual amount that would have gone out the door as the fraud is stopped immediately after detection. The problem is censored as soon as some intervention takes place, making it difficult to precisely quantify the potential risk.

Another challenge in the process of quantifying fraud is how well the fraud episode itself gets recorded. Consider the case of a credit card number getting stolen without the physical card getting stolen. During a certain period, both the legitimate cardholder and the fraudster are charging using the card. If the fraud detection system in the issuing institution doesn’t identify the fraudulent transactions as they were happening in real time, typically fraud is identified when the cardholder gets the monthly statement and figures out that some of the charges were not made by him/her. Then the cardholder calls the issuer to report the fraud.  In the not too distant past, all that used to get recorded by the bank was the cardholder’s estimate of when the fraud episode began, even though there were additional details about the fraudulent transactions that were likely shared by the cardholder. If all that gets recorded is the cardholder’s estimate of when the fraud episode began, ambiguity is introduced regarding the granularity of the actual fraud episode. The initial estimate of the fraud amount becomes a rough estimate at best.  In the case in which the bank’s fraud detection system was able to catch the fraud during the actual fraud episode, the fraudulent transactions tended to be recorded by a fraud analyst, and sometimes not too accurately. If the transaction was marked as fraud or non-fraud incorrectly, this problem was typically not corrected even after the correct information flowed in. When eventually the transactions that were actually fraudulent were identified using the actual postings of the transactions, relating this back to the authorization transactions was often not a straightforward process. Sometimes the amounts of the transactions may have varied slightly. For example, the authorization transaction of a restaurant charge is sometimes unlikely to include the tip that the customer added to the bill. The posted amount when this transaction gets reconciled would look slightly different from the authorized amount. All of this poses an interesting challenge when designing a data-driven analytical system to combat fraud.

The level of accuracy associated with recording fraud data also tends to be dependent on whether the fraud loss is a liability for the customer or to the financial institution. To a significant extent, the answer to the question, “Whose loss is it?” really drives how well past fraud data is recorded. In the case of unsecured lending such as credit cards, most of the liability lies with the banks, and the banks tend to care a lot more about this type of loss. Hence systems are put in place to capture this data on a historical basis reasonably accurately.

In the case of secured lending, ID theft, and so on, a significant portion of the liability is really on the customer, and it is up to the customer to prove to the bank that he or she has been defrauded. Interestingly, this shift of liability also tends to have an impact on the quality of the fraud data captured. In the case of fraud associated with automated clearing house (ACH) batches and domestic and international wires, the problem is twofold: The fraud instances are very infrequent, making it impossible for the banks to have a uniform method of recording frauds; and the liability shifts are dependent on the geography.  Most international locations put the onus on the customer, while in the United States there is legislation requiring banks to have fraud detection systems in place.  The extent to which our client organizations take responsibility also tends to depend on how much they care about the customer who has been defrauded. When a very valuable customer complains about fraud on her account, a bank is likely to pay attention.  Given that most such frauds are not large scale, there is less need to establish elaborate systems to focus on and collect the data and keep track of past irregularities. The past fraud information is also influenced heavily by whether the fraud is third-party or first-party fraud. Third-party fraud is where the fraud is committed clearly by a third party, not the two parties involved in a transaction. In first-party fraud, the perpetrator of the fraud is the one who has the relationship with the bank. The fraudster in this case goes to great lengths to prevent the banks from knowing that fraud is happening. In this case, there is no reporting of the fraud by the customer. Until the bank figures out that fraud is going on, there is no data that can be collected. Also, such fraud could go on for quite a while and some of it might never be identified. This poses some interesting problems. Internal fraud where the employee of the institution is committing fraud could also take significantly longer to find. Hence the data on this tends to be scarce as well.

In summary, one of the most significant challenges in fraud analytics is to build a sufficient database of normal client transactions.  The normal transactions of any organization constitute the baseline from which abnormal, fraudulent or irregular transactions, can be identified and analyzed.  The pinpointing of the irregular is thus foundational to the development of the transaction processing edits which prevent the irregular transactions embodying fraud from even being processed and paid on the front end; furnishing the key to modern, analytically based fraud prevention.

Threat Assessment & Cyber Security

One rainy Richmond evening last week I attended the monthly dinner meeting of one of the professional organizations of which I’m a member.  Our guest speaker’s presentation was outstanding and, in my opinion, well worth sharing with fellow CFE’s especially as we find more and more of our client’s grappling with the reality of  ever-evolving cyber threats.

Our speaker started by indicating that, according to a wide spectrum of current thinking, technology issues in isolation should be but one facet of the overall cyber defense strategy of any enterprise. A holistic view on people, process and technology is required in any organization that wants to make its chosen defense strategy successful and, to be most successful, that strategy needs to be supplemented with a good dose of common sense creative thinking. That creative thinking proved to be the main subject of her talk.

Ironically, the sheer size, complexity and geopolitical diversity of the modern-day enterprise can constitute an inherent obstacle for its goal of achieving business objectives in a secured environment.  The source of the problem is not simply the cyber threats themselves, but threat agents. The term “threat agent,” from the Open Web Application Security Project (OWASP), is used to indicate an individual or group that can manifest a threat. Threat agents are represented by the phenomena of:

–Hacktivism;
–Corporate Espionage;
–Government Actors;
–Terrorists;
–Common Criminals (individual and organized).

Irrespective of the type of threat, the threat agent takes advantage of an identified vulnerability and exploits it in the attempt to negatively impact the value the individual business has at risk. The attempt to execute the threat in combination with the vulnerability is called hacking. When this attempt is successful, and the threat agent can negatively impact the value at risk, it can be concluded that the vulnerability was successfully exploited. So, essentially, enterprises are trying to defend against hacking and, more importantly, against the threat agent that is the hacker in his or her many guises. The ACFE identifies hacking as the single activity that has resulted in the greatest number of cyber breaches in the past decade.

While there is no one-size-fits-all standard to build and run a sustainable security defense in a generic enterprise context, most companies currently deploy something resembling the individual components of the following general framework:

–Business Drivers and Objectives;
–A Risk Strategy;
–Policies and Standards;
–Risk Identification and Asset Profiling;
–People, Process, Technology;
–Security Operations and Capabilities;
–Compliance Monitoring and Reporting.

Most IT risk and security professionals would be able to identify this framework and agree with the assertion that it’s a sustainable approach to managing an enterprise’s security landscape. Our speaker pointed out, however, that in her opinion, if the current framework were indeed working as intended, the number of security incidents would be expected to show a downward trend as most threats would fail to manifest into full-blown incidents. They could then be routinely identified by enterprises as known security problems and dealt with by the procedures operative in day-to-day security operations. Unfortunately for the existing framework, however, recent security surveys conducted by numerous organizations and trade groups clearly show an upward trend of rising security incidents and breaches (as every reader of daily press reports well knows).

The rising tide of security incidents and breaches is not surprising since the trade press also reports an average of 35 new, major security failures on each and every day of the year.  Couple this fact with the ease of execution and ready availability of exploit kits on the Dark Web and the threat grows in both probability of exploitation and magnitude of impact. With speed and intensity, each threat strikes the security structure of an enterprise and whittles away at its management credibility to deal with the threat under the routine, daily operational regimen presently defined. Hence, most affected enterprises endure a growing trend of negative security incidents experienced and reported.

During the last several years, in response to all this, many firms have responded by experimenting with a new approach to the existing paradigm. These organizations have implemented emergency response teams to respond to cyber-threats and incidents. These teams are a novel addition to the existing control structure and have two main functions: real-time response to security incidents and the collection of concurrent internal and external security intelligence to feed predictive analysis. Being able to respond to security incidents via a dedicated response team boosts the capacity of the operational organization to contain and recover from attacks. Responding to incidents, however efficiently, is, in any case, a reactive approach to deal with cyber-threats but isn’t the whole story. This is where cyber-threat intelligence comes into play. Threat intelligence is a more proactive means of enabling an organization to predict incidents. However, this approach also has a downside. The influx of a great deal of intelligence information may limit the ability of the company to render it actionable on a timely basis.

Cyber threat assessments are an effective means to tame what can be this overwhelming influx of intelligence information. Cyber threat assessment is currently recognized in the industry as red teaming, which is the practice of viewing a problem from an adversary or competitor’s perspective. As part of an IT security strategy, enterprises can use red teams to test the effectiveness of the security structure as a whole and to provide a relevance factor to the intelligence feeds on cyber threats. This can help CEOs decide what threats are relevant and have higher exposure levels compared to others. The evolution of cyber threat response, cyber threat
intelligence and cyber threat assessment (red teams) in conjunction with the existing IT risk framework can be used as an effective strategy to counter the agility of evolving cyber threats. The cyber threat assessment process assesses and challenges the structure of existing enterprise security systems, including designs, operational-level controls and the overall cyber threat response and intelligence process to ensure they remain capable of defending against current relevant exploits.

Cyber threat assessment exercises can also be extremely helpful in highlighting the most relevant attacks and in quantifying their potential impacts. The word “adversary” in the definition of the term ‘red team’ is key in that it emphasizes the need to independently challenge the security structure from the view point of an attacker.  Red team exercises should be designed to be independent of the scope, asset profiling, security, IT operations and coverage of existing security policies. Only then can enterprises realistically apply the attacker’s perspective, measure the success of its risk strategy and see how it performs when challenged. It’s essential that red team exercises have the freedom to treat the complete security structure and to point to flaws in all components of the IT risk framework. It’s a common notion that a red team exercise is a penetration test. This is not the case. Use of penetration test techniques by red teams is a means to identify the information required to replicate cyber threats and to create a controlled security incident. The technical shortfalls that are identified during standard penetration testing are mere symptoms of gaps that may exist in the governance of people, processes and technology. Hence, to make the organization more resilient against cyber threats, red team focus should be kept on addressing the root cause and not merely on fixing the security flaws discovered during the exercise. Another key point is to include cyber threat response and threat monitoring in the scope of such assessments. This demands that red team exercises be executed, and partially announced, with CEO-level approval. This ensures that enterprises challenge the end-to-end capabilities of an enterprise to cope with a real-time security incident. Lessons learned from red teaming can be documented to improve the overall security posture of the organization and as an aid in dealing with future threats.

Our speaker concluded by saying that as cyber threats evolve, one-hundred percent security for an active business is impossible to achieve. Business is about making optimum use of existing resources to derive the desired value for stakeholders. Cyber-defense cannot be an exception to this rule. To achieve optimized use of their security investments, CEOs should ensure that security spending for their organization is mapped to the real emerging cyber threat landscape. Red teaming is an effective tool to challenge the status quo of an enterprise’s security framework and to make informed judgements about the actual condition of its actual security posture today. Not only can the judgements resulting from red team exercises be used to improve cyber threat defense, they can also prove an effective mechanism to guide a higher return on cyber-defense investment.

Bye-Bye Money

Miranda had responsibility for preparing personnel files for new hires, approval of wages, verification of time cards, and distribution of payroll checks. She “hired” fictitious employees, faked their records, and ordered checks through the payroll system. She deposited some checks in several personal bank accounts and cashed others, endorsing all of them with the names of the fictitious employees and her own. Her company’s payroll function created a large paper trail of transactions among which were individual earnings records, W-2 tax forms, payroll deductions for taxes and insurance, and Form 941 payroll tax reports. She mailed all the W-2 forms to the same post office box.

Miranda stole $160,000 by creating some “ghosts,” usually 3 to 5 out of 112 people on the payroll and paying them an average of $650 per week for three years. Sometimes the ghosts quit and were later replaced by others. But she stole “only” about 2 percent of the payroll funds during the period.

A tip from a fellow employee received by the company hotline resulted in the engagement of Tom Hudson, CFE.  Tom’s objective was to obtain evidence of the existence and validity of payroll transactions on the control premise that different people should be responsible for hiring (preparing personnel files), approving wages, and distributing payroll checks. “Thinking like a crook” lead Tom to readily see that Miranda could put people on the payroll and obtain their checks just as the hotline caller alleged. In his test of controls Tom audited for transaction authorization and validity. In this case random sampling was less likely to work because of the small number of alleged ghosts. So, Tom looked for the obvious. He selected several weeks’ check blocks, accounted for numerical sequence (to see whether any checks had been removed), and examined canceled checks for two endorsements.

Tom reasoned that there may be no “balance” to audit for existence/occurrence, other than the accumulated total of payroll transactions, and that the total might not appear out of line with history because the tipster had indicated that the fraud was small in relation to total payroll and had been going on for years.  He decided to conduct a surprise payroll distribution, then followed up by examining prior canceled checks for the missing employees and then scan personnel files for common addresses.

Both the surprise distribution and the scan for common addresses quickly provided the names of 2 or 3 exceptions. Both led to prior canceled checks (which Miranda had not removed and the bank reconciler had not noticed), which carried Miranda’s own name as endorser. Confronted, she confessed.

The major risks in any payroll business cycle are:

•Paying fictitious “employees” (invalid transactions, employees do not exist);

• Overpaying for time or production (inaccurate transactions, improper valuation);

•Incorrect accounting for costs and expenses (incorrect classification, improper or inconsistent presentation and disclosure).

The assessment of payroll system control risk normally takes on added importance because most companies have fairly elaborate and well-controlled personnel and payroll functions. The transactions in this cycle are numerous during the year yet result in lesser amounts in balance sheet accounts at year-end. Therefore, in most routine outside auditor engagements, the review of controls, test of controls and audit of transaction details constitute the major portion of the evidence gathered for these accounts. On most annual audits, the substantive audit procedures devoted to auditing the payroll-related account balances are very limited which enhances fraud risk.

Control procedures for proper segregation of responsibilities should be in place and operating. Proper segregation involves authorization (personnel department hiring and firing, pay rate and deduction authorizations) by persons who do not have payroll preparation, paycheck distribution, or reconciliation duties. Payroll distribution (custody) is in the hands of persons who do not authorize employees’ pay rates or time, nor prepare the payroll checks. Recordkeeping is performed by payroll and cost accounting personnel who do not make authorizations or distribute pay. Combinations of two or more of the duties of authorization, payroll preparation and recordkeeping, and payroll distribution in one person, one office, or one computerized system may open the door for errors and frauds. In addition, the control system should provide for detail control checking activities.  For example: (1) periodic comparison of the payroll register to the personnel department files to check hiring authorizations and for terminated employees not deleted, (2) periodic rechecking of wage rate and deduction authorizations, (3) reconciliation of time and production paid to cost accounting calculations, (4) quarterly reconciliation of YTD earnings records with tax returns, and (5) payroll bank account reconciliation.

Payroll can amount to 40 percent or more of an organization’s total annual expenditures. Payroll taxes, Social Security, Medicare, pensions, and health insurance can add several percentage points in variable costs on top of wages. So, for every payroll dollar saved through forensic identification, bonus savings arise automatically from the on-top costs calculated on base wages. Different industries will exhibit different payroll risk profiles. For example, firms whose culture involves salaried employees who work longer hours may have a lower risk of payroll fraud and may not warrant a full forensic approach. Organizations may present greater opportunity for payroll fraud if their workforce patterns entail night shift work, variable shifts or hours, 24/7 on-call coverage, and employees who are mobile, unsupervised, or work across multiple locations. Payroll-related risks include over-claimed allowances, overused extra pay for weekend or public holiday work, fictitious overtime, vacation and sick leave taken but not deducted from leave balances, continued payment of employees who have left the organization, ghost employees arising from poor segregation of duties, and the vulnerability of data output to the bank for electronic payment, and roster dysfunction. Yet the personnel assigned to administer the complexities of payroll are often qualified by experience than by formal finance, legal, or systems training, thereby creating a competency bias over how payroll is managed. On top of that, payroll is normally shrouded in secrecy because of the inherently private nature of employee and executive pay. Underpayment errors are less probable than overpayment errors because they are more likely to be corrected when the affected employees complain; they are less likely to be discovered when employees are overpaid. These systemic biases further increase the risk of unnoticed payroll error and fraud.

Payroll data analysis can reveal individuals or entire teams who are unusually well-remunerated because team supervisors turn a blind eye to payroll malpractice, as well as low-remunerated personnel who represent excellent value to the organization. For example, it can identify the night shift worker who is paid extra for weekend or holiday work plus overtime while actually working only half the contracted hours, or workers who claim higher duty or tool allowances to which they are not entitled. In addition to providing management with new insights into payroll behaviors, which may in turn become part of ongoing management reporting, the total payroll cost distribution analysis can point forensic accountants toward urgent payroll control improvements.

The detail inside payroll and personnel databases can reveal hidden information to the forensic examiner. Who are the highest earners of overtime pay and why? Which employees gained the most from weekend and public holiday pay? Who consistently starts late? Finishes early? Who has the most sick leave? Although most employees may perform a fair day’s work, the forensic analysis may point to those who work less, sometimes considerably less, than the time for which they are paid. Joined-up query combinations to search payroll and human resources data can generate powerful insights into the organization’s worst and best outliers, which may be overlooked by the data custodians. An example of a query combination would be: employees with high sick leave + high overtime + low performance appraisal scores + negative disciplinary records. Or, reviewers could invert those factors to find the unrecognized exemplary performers.

Where predication suggests fraud concerns about identified employees, CFEs can add value by triangulating time sheet claims against external data sources such as site access biometric data, company cell phone logs, phone number caller identification, GPS data, company email, Internet usage, company motor fleet vehicle tolls, and vehicle refueling data, most of which contain useful date and time-of-day parameters.  The data buried within these databases can reveal employee behavior, including what they were doing, where they were, and who they were interacting with throughout the work day.

Common findings include:

–Employees who leave work wrongfully during their shift;
–Employees who work fewer hours and take sick time during the week to shift the workload to weekends and public holidays to maximize pay;
–Employees who use company property excessively for personal purposes during working hours;
–Employees who visit vacation destinations while on sick leave;
–Employees who take leave but whose managers do not log the paperwork, thereby not deducting leave taken and overstating leave balances;
–Employees who moonlight in businesses on the side during normal working hours, sometimes using the organization’s equipment to do so.

Well-researched and documented forensic accounting fieldwork can support management action against those who may have defrauded the organization or work teams that may be taking inappropriate advantage of the payroll system. Simultaneously, CFEs and forensic accountants, working proactively, can partner with management to recover historic costs, quantify future savings, reduce reputational and political risk, improve the organization’s anti-fraud policies, and boost the productivity and morale of employees who knew of wrongdoing but felt powerless to stop it.