In this article we examine factors behind the low detection rate of fraud using analytics, and ways that companies can build greater confidence in the effective use of analytics to combat fraud. In looking at the analytics-related aspects explored in KPMG’s Global profiles of the fraudster, the article offers perspective on the positive implications for businesses when trust is carefully managed in an anti-fraud analytics program.
Fraud is a very damaging facet of business life that companies often suffer from. To detect fraudsters, companies often deploy data & analytics to search for suspicious transactions. If a detection program is going to succeed, it must have access to reliable data and be trusted to perform according to the company’s expectations. Executives must have confidence the analytics will work as intended, and they may lose trust in the anti-fraud program if it does not successfully detect cases of wrongdoing in the early phases.
KPMG’s report “Using analytics successfully to detect fraud” explains the challenges of managing an analytics-driven program and examines the steps companies should take to improve the chances of delivering such benefits as protecting the reputation of the organization. In this latest article in the Trusted Analytics Series, we find that very few companies are successfully employing analytics for the detection of fraud. This lack of adoption reflects a ‘trust deficit’; there’s a lack of trust that the underlying data, the analysis and the business interpretation of the outcomes will be able to distinguish between legitimate transactions and fraudulent activity in an efficient and cost-effective manner.
People must be confident that the analytics algorithms are working as intended and must trust each other to use them properly. Find out more about the elements of an effective, anti-fraud analytics process and how companies may benefit from a carefully managed program of fraud detection.
A new heightened focus on trust is emerging and is quickly becoming a defining factor of Data and Analytics (D&A).