Fraudulent activities are of critical concern for many entities including financial organizations, insurance companies, governmental institutions, and individual investors. Both public and private projects have dark corners where corrupt actors can hide and thrive. Many contemporary organizations have been stepping up their efforts to detect and prevent fraud. However, spotting fraud patterns and clues can be quite complex most of the time. Thus, many entities still see fraud as a needle in the haystack.
What is Data Analytics in the Context of Fraud?
Data analytics, as it applies to fraud examination, refers to the use of analytics software to identify trends, patterns, anomalies, and exceptions within data. It is especially useful when fraud is hidden in large data volumes and manual checks are insufficient and can be used both reactively and proactively.
What is the Cost of Fraud?
2016 Global Survey of over 40,000 Certified Fraud Examiners revealed that fraud accounted for USD 6.3 Billion in losses, with a typical organization losing 5% of its revenues annually to fraud. Undoubtedly fraud schemes have become more sophisticated. Fraudsters are constantly finding new ways to manipulate technology. Thus, these costs are only set to rise unless we have appropriate mitigation mechanisms in hand.
How Wide is the Application of Data Analytics in Fraud Detection?
The advent of the digital age has created new challenges and opportunities for fraud and corruption risk assessment and management. Throughout recent decade, various techniques from different areas such as data mining, machine learning, and statistics have been proposed to deal with fraudulent activities.
Although the analytical market suggests a wide spectrum of specialized tools that are capable to support and enhance the antifraud activity, the evidence suggests that the majority of organizations are unable to take advantage of them. Based on a global survey of KPMG professionals who investigated 750 fraudsters between March 2013 and August 2015, only 3% were detected using proactive, fraud-focused analytics, compared with 44% that were found by means of whistle-blower mechanisms and other forms of tip-off.
What limits the Scope?
There are several underlying reasons that could limit the scope of data analytics’ application in fraud risk management. First and foremost is that many companies do not realize the power of analytical tools, while others might flinch from expenses. The lack of data analytics adoption may also imply the lack of trust towards such tools and the processes in general.
What is the impact of Data Analytics on Fraud Detection Practices?
Organizations that implement proactive data monitoring detect frauds 58% faster and experience losses that are 52% lower than organizations that don’t. (ACFE 2018) Data-driven corruption risk assessments can help managers to identify the riskiest transactions and adapt control activities across the project cycle, including predicting high-risk transactions before spending. Linking data analytics to management objectives can help drive broader improvements in data governance, infrastructure, and the institutionalization of an analytics function. Although data analytics cannot replace human judgment and skepticism, it can complement qualitative methodologies and enhance the effectiveness of risk assessment.
Where can Data Analytics Help?
- Systematic Fraud Pattern Detection – Computerized programs can scan through large sets of data and scrutinize it to detect malicious activities and set them for inspection. Moreover, such a program can successfully trace the system to find thefts holding a similar pattern. It could follow a similar route or originate from the same source.
- Tracking Anomalies – Apart from detecting a pattern, data analytics can be applied to find anomalies in data by performing rigorous testing. The latter allows for the identification of suspicious acts within the organization.
What are the Ways for Detecting Fraud?
Fraud can be effectively detected through testing. There are 3 major types of testing:
- Ad-Hoc Testing – is done on a need basis and is performed once a certain problem arises. Once the test has been executed the report is generated and threats are proposed.
- Repetitive Testing
- Continuous Testing – is an alternative for ad-hoc testing. During the process, automated scripts run through the system in a continuum and detect threats. The system is being scanned repeatedly followed by detailed monitoring, to ensure that no anomaly gets missed.
Analytical Techniques for Detecting Fraud
- Association analysis – while using this technique one can identify suspicious relationships by quantifying the odds of a combination of data points occurring together.
- Outlier analysis. Outliers are data points outside the norm for a given data set. Outliers can come in handy while performing fraud analysis.
- Cluster analysis – when using this technique similar data points are grouped into a set and then subdivided into smaller, more homogeneous clusters. Data points within a cluster are similar to each other but differ from those in other clusters. By observing these similarities and differences one can develop rules that apply to one cluster but not the others. Cluster analysis can be very beneficial for fraud detection, particularly when combined with outlier analysis.
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