Technology and Analytics in Fraud Detection

Technology and Analytics in Fraud Detection

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Fraud is defined as the intentional decision to act or to not act in order to obtain personal gains. The most loyal employee may commit financial fraud and steal from the company if the opportunity arises and the temptation becomes too great or if the employee finds himself caught up in a serious personal financial dilemma and needs fast cash. Most fraud is ongoing, once it starts it does not stop by itself, and as it continues, it grows. Many are surprised to find out that most fraud is perpetrated by well-educated people in senior executive positions.

Fraud in the workplace is exhibited in many forms and these include but are not limited to the following: Embezzlement, also called larceny, 


which is the illegal use of funds by a person who controls those funds. Internal theft, which is the stealing of company assets by employees, such as taking office supplies or products the company sells without paying for them. Internal theft is often the culprit behind inventory shrinkage. Payoffs and kickbacks, which are situations in which employees accept cash or other benefits in exchange for access to the company’s business, often creating a scenario where the company that the employee works for pays more for the goods or products than necessary. That extra money finds its way into the employee’s pocket who helped facilitate the access. Skimming, which occurs when employees take money from receipts and don’t record the revenue on the books.

Many business owners will contend that there are sufficient internal controls in place to deter, or even eliminate fraudulent actions. However, experience has shown that internal controls do not entirely prevent fraud. Similarly, the previous year’s internal controls may no longer be as effective as when they were developed. Businesses change, and as they do more or different employees are hired for old and newly created positions. Rarely are internal controls considered in these circumstances. Nevertheless, it is believed that new technologies and analytics could improve existing fraud detection methods.


The battle against fraud is evolving, and technology is providing new and important tools to detect and prevent fraud. Companies are using a variety of techniques, including continuous monitoring, email monitoring, anomaly detection, pattern recognition, and artificial intelligence. Data mining and statistical analysis can also be helpful in detecting fraud. By using sophisticated data mining tools, companies can search millions of transactions to spot patterns and detect fraudulent transactions. These tools include decision trees, machine learning, cluster analysis as well as association rules and can generate models to predict fraud.


Data analysis is a straightforward strategy for detecting fraud. The objective is to analyze the entire set of data (for example transactional data, master vendor data and application control settings) to identify indicators of fraud. Data analysis techniques can vary from statistical analysis for transactions outside the norm to analytic tests for identifying specific circumstances indicative of fraud. The statistical analysis identifies transactions for closer examination. Another type of statistical test is to look for the presence of certain matches (for example employees and suppliers' identities, addresses, and bank accounts).


Fraudsters are adept at taking advantage of weaknesses or gaps in a company’s internal controls especially when business systems do not share or verify the information. Specific tests for matches of database fields can be an effective way to uncover potential anomalies. Some analytic procedures are fairly simple like looking for duplicate payments of an invoice. Data analytic tests, however, have to be carefully designed to avoid an excessive number of exceptions that may overwhelm fraud detectives. Data analysis software is available for audit, fraud detection, and control testing. These tools usually include pre-established analytic tests, such as classification stratification, duplicate testing, aging, and match-and-compare. In implementing a software solution, a company has to ensure that the software logs all procedures performed and audit trails to support fraud investigations.


After establishing a roster of effective data tests, companies should employ such testing on a continuous or regular basis, depending on the nature of the transactions. They should be continuous for daily payments and periodic for regularly scheduled payments. Continuous monitoring detection should generate a dashboard and reports. Most companies maintain fraud detection in business processes like in payrolls, travel and entertainment or areas that are high risk.


Ifeoma is a Business Analytics and Research Consultant at Industrial Psychology Consultants (Pvt) Ltd, a business management and human resources consulting firm.


Phone: +263 242 481946-48/481950

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Ifeoma Obi
This article was written by Ifeoma a Guest at Industrial Psychology Consultants (Pvt) Ltd

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