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.

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