Tag Archives: analytical techniques

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.

New Rules for New Tools

I’ve been struck these last months by several articles in the trade press about CFE’s increasingly applying advanced analytical techniques in support of their work as full-time employees of private and public-sector enterprises.  This is gratifying to learn because CFE’s have been bombarded for some time now about the risks presented by cloud computing, social media, big data analytics, and mobile devices, and told they need to address those risk in their investigative practice.  Now there is mounting evidence of CFEs doing just that by using these new technologies to change the actual practice of fraud investigation and forensic accounting by using these innovative techniques to shape how they understand and monitor fraud risk, plan and manage their work, test transactions against fraud scenarios, and report the results of their assessments and investigations to management; demonstrating what we’ve all known, that CFEs, especially those dually certified as CPAs, CIAs, or CISA’s can bring a unique mix of leveraged skills to any employer’s fraud prevention or detection program.

Some examples …

Social Media — following a fraud involving several of the financial consultants who work in its branches and help customers select accounts and other investments, a large multi-state bank requested that a staff CFE determine ways of identifying disgruntled employees who might be prone to fraud. The effort was important to management not only because of fraud prevention but because when the bank lost an experienced financial consultant for any reason, it also lost the relationships that individual had established with the bank’s customers, affecting revenue adversely. The staff CFE suggested that the bank use social media analytics software to mine employees’ email and posts to its internal social media groups. That enabled the bank to identify accurately (reportedly about 33 percent) the financial consultants who were not currently satisfied with their jobs and were considering leaving. Management was able to talk individually with these employees and address their concerns, with the positive outcome of retaining many of them and rendering them less likely to express their frustration by ethically challenged behavior.  Our CFE’s awareness that many organizations use social media analytics to monitor what their customers say about them, their products, and their services (a technique often referred to as sentiment analysis or text analytics) allowed her to suggest an approach that rendered value. This text analytics effort helped the employer gain the experience to additionally develop routines to identify email and other employee and customer chatter that might be red flags for future fraud or intrusion attempts.

Analytics — A large international bank was concerned about potential money laundering, especially because regulators were not satisfied with the quality of their related internal controls. At a CFE employee’s recommendation, it invested in state-of-the-art business intelligence solutions that run “in-memory”, a new technique that enables analytics and other software to run up to 300,000 times faster, to monitor 100 percent of its transactions, looking for the presence of patterns and fraud scenarios indicating potential problems.

Mobile — In the wake of an identified fraud on which he worked, an employed CFE recommended that a global software company upgrade its enterprise fraud risk management system so senior managers could view real-time strategy and risk dashboards on their mobile devices (tablets and smartphones). The executives can monitor risks to both the corporate and to their personal objectives and strategies and take corrective actions as necessary. In addition, when a risk level rises above a defined target, the managers and the risk officer receive an alert.

Collaboration — The fraud prevention and information security team at a U.S. company wanted to increase the level of employee acceptance and compliance with its fraud prevention – information security policy. The CFE certified Security Officer decided to post a new policy draft to a collaboration area available to every employee and encouraged them to post comments and suggestions for upgrading it. Through this crowd-sourcing technique, the company received multiple comments and ideas, many of which were incorporated into the draft. When the completed policy was published, the company found that its level of acceptance increased significantly, its employees feeling that they had part ownership.

As these examples demonstrate, there is a wonderful opportunity for private and public sector employed CFE’s to join in the use of enterprise applications to enhance both their and their employer’s investigative efficiency and effectiveness.  Since their organizations are already investing heavily in a wide variety of innovative technologies to transform the way in which they deliver products to and communicate with customers, as well as how they operate, manage, and direct the business, there is no reason that CFE’s can’t use these same tools to transform each stage of their examination and fraud prevention work.

A risk-based fraud prevention approach requires staff CFEs to build and maintain the fraud prevention plan, so it addresses the risks that matter to the organization, and then update that plan as risks change. In these turbulent times, dominated by cyber, risks change frequently, and it’s essential that fraud prevention teams understand the changes and ensure their approach for addressing them is updated continuously. This requires monitoring to identify and assess both new risks and changes in previously identified risks.  Some of the recent technologies used by organizations’ financial and operational analysts, marketing and communications professionals, and others to understand both changes within and outside the business can also be used to great advantage by loss prevention staff for risk monitoring. The benefits of leveraging this same software are that the organization has existing experts in place to teach CFE’s how to use it, the IT department already is providing technical support, and the software is currently used against the very data enterprise fraud prevention professionals like staff CFEs want to analyze.  A range of enhanced analytics software such as business intelligence, analytics (including predictive and mobile analytics), visual intelligence, sentiment analysis, and text analytics enable fraud prevention to monitor and assess risk levels. In some cases, the software monitors transactions against predefined rules to identify potential concerns such as heightened fraud risks in any given business process or in a set of business processes (the inventory or financial cycles).  For example, a loss prevention team headed by a staff CFE can monitor credit memos in the first month of each quarter to detect potential revenue accounting fraud. Another use is to identify trends associated with known fraud scenarios, such as changes in profit margins or the level of employee turnover, that might indicate changes in risk levels. For example, the level of emergency changes to enterprise applications can be analyzed to identify a heightened risk of poor testing and implementation protocols associated with a higher vulnerability to cyber penetration.

Finally, innovative staff CFEs have used some interesting techniques to report fraud risk assessments and examination results to management and to boards. Some have adopted a more visually appealing representation in a one-page assessment report; others have moved to the more visual capabilities of PowerPoint from the traditional text presentation of Microsoft Word.  New visualization technology, sometimes called visual analytics when allied with analytics solutions, provides more options for fraud prevention managers seeking to enhance or replace formal reports with pictures, charts, and dashboards.  The executives and boards of their employing organizations are already managing their enterprise with dashboards and trend charts; effective loss prevention communications can make effective use of the same techniques. One CFE used charts and trend lines to illustrate how the time her employing company was taking to process small vendor contracts far exceeded acceptable levels, had contributed to fraud risk and was continuing to increase. The graphic, generated by a combination of a business intelligence analysis and a visual analytics tool to build the chart, was inserted into a standard monthly loss prevention report.

CFE headed loss prevention departments and their allied internal audit and IT departments have a rich selection of technologies that can be used by them individually or in combination to make them all more effective and efficient. It is questionable whether these three functions can remain relevant in an age of cyber, addressing and providing assurance on the risks that matter to the organization, without an ever wider use of modern technology. Technology can enable the an internal CFE to understand the changing business environment and the risks that can affect the organization’s ability to achieve its fraud prevention related objectives.

The world and its risks are evolving and changing all the time, and assurance professionals need to address the issues that matter now. CFEs need to review where the risk is going to be, not where it was when the anti-fraud plan was built. They increasingly need to have the ability to assess cyber fraud risk quickly and to share the results with the board and management in ways that communicate assurance and stimulate necessary change.

Technology must be part of the solution to that need. Technological tools currently utilized by CFEs will continue to improve and will be joined by others over time. For example, solutions for augmented or virtual reality, where a picture or view of the physical world is augmented by data about that picture or view enables loss prevention professionals to point their phones at a warehouse and immediately access operational, personnel, safety, and other useful information; representing that the future is a compound of both challenge and opportunity.

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.

Analytics & Fraud Prevention

During our Chapter’s live training event last year, ‘Investigating on the Internet’, our speaker Liseli Pennings, pointed out that, according to the ACFE’s 2014 Report to the Nations on Occupational Fraud and Abuse, organizations that have proactive, internet oriented, data analytics in place have a 60 percent lower median loss because of fraud, roughly $100,000 lower per incident, than organizations that don’t use such technology. Further, the report went on, use of proactive data analytics cuts the median duration of a fraud in half, from 24 months to 12 months.

This is important news for CFE’s who are daily confronting more sophisticated frauds and criminals who are increasingly cyber based.  It means that integrating more mature forensic data analytics capabilities into a fraud prevention and compliance monitoring program can improve risk assessment, detect potential misconduct earlier, and enhance investigative field work. Moreover, forensic data analytics is a key component of effective fraud risk management as described in The Committee of Sponsoring Organizations of the Treadway Commission’s most recent Fraud Risk Management Guide, issued in 2016, particularly around the areas of fraud risk assessment, prevention, and detection.  It also means that, according to Pennings, fraud prevention and detection is an ideal big data-related organizational initiative. With the growing speed at which they generate data, specifically around their financial reporting and sales business processes, our larger CFE client organizations need ways to prioritize risks and better synthesize information using big data technologies, enhanced visualizations, and statistical approaches to supplement traditional rules-based investigative techniques supported by spreadsheet or database applications.

But with this analytics and fraud prevention integration opportunity comes a caution.  As always, before jumping into any specific technology or advanced analytics technique, it’s crucial to first ask the right risk or control-related questions to ensure the analytics will produce meaningful output for the business objective or risk being addressed. What business processes pose a high fraud risk? High-risk business processes include the sales (order-to-cash) cycle and payment (procure-to-pay) cycle, as well as payroll, accounting reserves, travel and entertainment, and inventory processes. What high-risk accounts within the business process could identify unusual account pairings, such as a debit to depreciation and an offsetting credit to a payable, or accounts with vague or open-ended “catch all” descriptions such as a “miscellaneous,” “administrate,” or blank account names?  Who recorded or authorized the transaction? Posting analysis or approver reports could help detect unauthorized postings or inappropriate segregation of duties by looking at the number of payments by name, minimum or maximum accounts, sum totals, or statistical outliers. When did transactions take place? Analyzing transaction activities over time could identify spikes or dips in activity such as before and after period ends or weekend, holiday, or off-hours activities. Where does the CFE see geographic risks, based on previous events, the economic climate, cyber threats, recent growth, or perceived corruption? Further segmentation can be achieved by business units within regions and by the accounting systems on which the data resides.

The benefits of implementing a forensic data analytics program must be weighed against challenges such as obtaining the right tools or professional expertise, combining data (both internal and external) across multiple systems, and the overall quality of the analytics output. To mitigate these challenges and build a successful program, the CFE should consider that the priority of the initial project matters. Because the first project often is used as a pilot for success, it’s important that the project address meaningful business or audit risks that are tangible and visible to client management. Further, this initial project should be reasonably attainable, with minimal dollar investment and actionable results. It’s best to select a first project that has big demand, has data that resides in easily accessible sources, with a compelling, measurable return on investment. Areas such as insider threat, anti-fraud, anti-corruption, or third-party relationships make for good initial projects.

In the health care insurance industry where I worked for many years, one of the key goals of forensic data analytics is to increase the detection rate of health care provider billing non-compliance, while reducing the risk of false positives. From a capabilities perspective, organizations need to embrace both structured and unstructured data sources that consider the use of data visualization, text mining, and statistical analysis tools. Since the CFE will usually be working as a member of a team, the team should demonstrate the first success story, then leverage and communicate that success model widely throughout the organization. Results should be validated before successes are communicated to the broader organization. For best results and sustainability of the program, the fraud prevention team should be a multidisciplinary one that includes IT, business users, and functional specialists, such as management scientists, who are involved in the design of the analytics associated with the day-to-day operations of the organization and hence related to the objectives of  the fraud prevention program. It helps to communicate across multiple departments to update key stakeholders on the program’s progress under a defined governance regime. The team shouldn’t just report noncompliance; it should seek to improve the business by providing actionable results.

The forensic data analytics functional specialists should not operate in a vacuum; every project needs one or more business champions who coordinate with IT and the business process owners. Keep the analytics simple and intuitive, don’t include too much information in one report so that it isn’t easy to understand. Finally, invest time in automation, not manual refreshes, to make the analytics process sustainable and repeatable. The best trends, patterns, or anomalies often come when multiple months of vendor, customer, or employee data are analyzed over time, not just in the aggregate. Also, keep in mind that enterprise-wide deployment takes time. While quick projects may take four to six weeks, integrating the entire program can easily take more than one or two years. Programs need to be refreshed as new risks and business activities change, and staff need updates to training, collaboration, and modern technologies.

Research findings by the ACFE and others are providing more and more evidence of the benefits of integrating advanced forensic data analytics techniques into fraud prevention and detection programs. By helping increase their client organization’s maturity in this area, CFE’s can assist in delivering a robust fraud prevention program that is highly focused on preventing and detecting fraud risks.

On Auditors, Lawyers & Data

corp-counselWhen it comes to gaining access to sensitive, internal digital data during a forensic examination, the corporate council can be the fraud examiner’s best ally.  It, therefore, behooves us to fully understand the unifying role the client counsel holds in overseeing the entire review process.  As our guest blogger, Michael Hart, and other experienced practitioners have pointed out, data analysis becomes most effective when it’s integrated into the wider forensic accounting project.  If the end results are to cohere with findings from other sources, forensic data analysis should not be performed as a separate investigation, walled off from the other review efforts undertaken to benefit the client. Today, it’s a truism that data analysis can serve many functions within a forensic accounting project. On some occasions, it’s rightfully the main engine of an engagement. When such is the case, data analysis is used for highlighting potentially unusual items and trends. More often, however, in actual practice, data analysis is a complementary part of a wider forensic accounting investigation, a piece of a puzzle (and never the be all and end all of the investigation), that involves several other parallel methods of information analysis or evidence gathering, including document review, physical inspection, and investigative interviews.

The timing of the data analysis work depends on the extent to which the forensic accounting team needs to work with the results as defined by counsel. Frequently, once the method of a fraud has been established, data analysis is conducted to estimate the amount of damage. If the team knows that several components of an organization were affected by a fraud scheme, that team may be able to compare these results with those derived from analyses of unaffected branches and, after adjusting for other relevant factors, provide management with a broad estimate of the total effect on the financial statements. When such an approach is used, the comparison should be performed after the investigation has determined the characteristics of the fraud scheme. However, in most cases, as the ACFE tells us, the purpose of data analysis in an investigation is to identify suspicious activity on which the forensic accounting team can act.

Suspicious transactions can be identified in several ways: comparing different sources of evidence, such as accounting records and bank statements, to find discrepancies between them; searching digital records for duplicate transactions; or identifying sudden changes in the size, volume, or nature of transactions, which need to be explained. While data analysis often is a fast and effective way of highlighting potential areas of fraud, it will never capture every detail that an experienced fraud examiner can glean from reviewing an original document. If data analysis is performed to identify suspicious activity, it typically is performed before any manual review is carried out. This helps ensure that investigative resources are targeting suspicious areas and are concentrating on confirming fraudulent activity rather than concentrating on a search for such activity within a sea of legitimate transactions.

The first person to be contacted when there is a suspected fraud is typically in-house counsel. Depending on the apparent severity of the matter and its apparent location in the company, other internal resources to be alerted at an early stage, in addition to the board (typically through its audit committee), may include corporate security, internal audit, risk management, the controller’s office, and the public relations and investor relations groups. Investigations usually begin with extensive conversation about who should be involved, and the responsible executives may naturally wish to involve some or all the functions just mentioned.  Depending on the circumstances, the group of internal auditors (if there is one) can in fact be a tremendous asset to an independent forensic investigative team. As participants in the larger team, internal auditors’ knowledge of the company may improve both the efficiency with which evidence is gathered and the forensic team’s effectiveness in lining up interviews and analyzing findings. The ACFE advices client executives and in-house counsel to engage an external team but to consider making available to that team the company’s internal auditors, selected information systems staff and other internal resources for any investigation of substantial size.

The key to the success of all this from the forensic accountant’s point of view, especially in gaining access to critical digital data, can be the corporate counsel.  On one hand, the forensic accounting investigator may find that the attorney gives the forensic accounting investigator free rein to devise and execute a strategic investigative plan, subject to the attorney’s approval. That scenario is particularly likely in cases of asset misappropriation. On the other hand, some attorneys insist on being involved in all phases of the investigation. It’s the attorney’s call. When engaged by counsel, forensic accounting investigators take direction from counsel. You should advise per your best judgment, but in the end, you work at counsel’s direction.

When working with attorneys on projects involving sensitive digital data, forensic accounting investigators should specifically understand:

  • Their expected role and responsibilities vis-à-vis other team members;
  • Critical managers and players within the information systems shop and their various roles;
  • What other professionals are involved (current or contemplated);
  • The extent and source of any external scrutiny (SEC, IRS, DOJ, etc.);
  • Any legal considerations (extent of privilege, expectation that the company intends to waive privilege, expectation of criminal charges, and so on);
  • Anticipated timing issues, if any;
  • Expected form, timing, and audience of interim or final deliverables;
  • Specifics of the matters under investigation, as currently understood by counsel;
  • Any limitations on departments or personnel that can be involved, interviewed, or utilized in the investigation process.

Independent counsel, with the help of forensic accounting investigators, often takes the lead in setting up, organizing, and managing the entire investigative team. This process may include the selection and retention of other parties who make up the team. Independent counsel’s responsibilities typically encompass the following:

  • Preparing, maintaining, and disseminating a working-group list (very helpful in sorting out which law firms or experts represent whom);
  • Establishing the timetable in conjunction with the board of directors or management, disseminating the timetable to the investigating team, and tracking progress against it;
  • Compiling, submitting, and tracking the various document and personnel access requests that the investigating team members will generate;
  • Organizing client or team meetings and agendas;
  • Preparing the final report with or for the board or its special committee, or doing so in conjunction with other teams from which reports are forthcoming;
  • Establishing and maintaining communication channels with the board of directors and other interested parties, generally including internal general counsel, company management, regulatory personnel, law enforcement or tax authority personnel, and various other attorneys involved.

As fraud examiners, we’re frequently conversant in areas related to financial accounting and reporting such as valuation, tax, and the financial aspects of human resource management but conversant doesn’t necessarily indicate a sufficient level of knowledge to fully guide a complex organizational investigation.  What we can do, however, is to work closely with the corporate counsel to assist him or her in the building of a team on the back of which even the most complex examination can be brought to a successful conclusion.