Shift Claims Fraud vs. CPA Fraud Detection Service

Shift Technology’s Claims Fraud platform revolutionizes insurance fraud detection by combining AI, data integration, and investigative tools into one solution. Designed specifically for insurers, it identifies fraudulent claims at first notice of loss (FNOL) and throughout the claims lifecycle, enabling carriers to act before damage escalates. Its advanced AI models continuously learn from millions of claims and cross-carrier data, uncovering hidden patterns and sophisticated fraud rings. With the Insurance Data Network, insurers benefit from industry-wide intelligence, gaining the ability to connect dots between people, providers, and claims in real time. This reduces false positives and ensures investigators focus on the highest-impact cases, ultimately accelerating resolution. Shift also integrates external data sources and offers eForm, workflow, and reporting features to enhance operational efficiency. Trusted by over 115 insurers, it has detected 13M+ suspicious claims and helped identify billions in fraudulent payouts. Global carriers like AXA, Assurant, and L’olivier report measurable benefits, from millions saved to improved fraud prosecution outcomes. Recognized by Celent as a luminary in fraud detection, Shift stands at the forefront of insurance technology innovation. By blending scale, speed, and intelligence, it equips claims teams, SIUs, and executives with a powerful weapon against fraud.

Benford's law is an effective method for revealing patterns that may indicate fraudulent disbursements. It requires the analysis of audit trail reports from accounting software such as QuickBooks to identify suspicious actions like voids and deletions. Moreover, it also includes the detection of multiple payments made for the same amount on a single day. A comprehensive examination of payroll runs is performed to find any discrepancies, particularly payments that exceed designated salary or hourly rates. Payments made on days when businesses are closed are given extra attention. By employing statistical analyses, outliers that could point to fraud are uncovered, and duplicate payments are verified for accuracy. Furthermore, vendor files in accounts payable are scrutinized for names that may appear strikingly similar, leading to investigations into potential fictitious vendors. Comparisons of vendor addresses and payroll information are assessed through Z-Scores and relative size factor tests. Although data monitoring and unexpected audits have proven to significantly mitigate fraud losses, merely 37% of organizations have implemented these vital controls. For smaller businesses with fewer than 100 employees, the average estimated loss attributed to fraud stands at $200,000, underscoring the fact that these enterprises typically lack sufficient resources to effectively identify and respond to fraudulent activities. Therefore, it becomes crucial for small businesses to embrace more robust fraud detection strategies to protect their financial health and ensure long-term sustainability. By doing so, they can not only detect irregularities but also foster a culture of transparency and accountability within their operations.