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Data Analytics for Claims Fraud Detection

Posted by cogitateus on July 15th, 2019

Advanced analytics go beyond transforming customer experience and marketing functions and are increasingly being implemented for fraud detection. Cogitate Technology Solutions examines some methods of using predictive analytics to combat fraud.

Often purported to be a ‘victimless’ crime, claims fraud costs insurance companies, and ultimately policyholders, billions and drives up the insurance costs for everyone. While insurance companies have established effective fraud departments, yet undetected claim frauds are on the rise. Claims fraud statistics tracked by several organizations show a consistent increase over the years.

Traditionally, claims fraud detectionis performed by special investigators, insurance agents, claims adjustors and assessors. Armed with limited data about past frauds,heuristics based on a set of fraud indicators, their experience and their instincts, they would personally verify the genuineness of a claim. In reality, there aren’t enough trained eyes to examine all the claims and fraudulent claims slip through. The increase in available data comes with its own challenges as fraud detection becomes onerous and exhausting due to data overload. As multiple data sources are linked, it becomes practically impossible for humans to spot emergent patterns and insurance solutions that can help stem claims fraud are the need of the hour.

Analytics for improved fraud detection
Insurance houses are now beginning to rely on analytics to identify fraud or potential for fraud. The seek to convert available data into actionable intelligence to detect fraud much earlier in the cycle and improve underwriting checks. Analytics helps insurers sift through gargantuan amounts of data to identify patters and data anomalies across multiple data sets. Predictive analytics uses a mix of regression models and advanced techniques to examine the complexity of a claim and to determine if it requires further examination. This helps in assigning appropriate staff based on the complexity of claims while improving the processing speed of legitimate claims and increasing customer satisfaction.

In combating fraud, three methods of advanced analytics are taking center stage:
Predictive modellingexamines data elements to reveal patterns that indicate a high proclivity for fraud. One of the primary ways of fighting fraud, especially in P&C Insurance, is a combination of predictive modelling and text mining. Text analytics are applied to search for keywords in unstructured data such as assessor notes, emails, accident descriptions etc. to identify fraud patterns. Predictive modelling allows insurance providers to move from pay-and-chase to prevention with these prophetic insights.

Social network analysis(SNA), or link analysis, reveals connections to expose collusive activities. SNA helps in identifying proximities and strength of relationships among people, organizations and groups. This, along with studying the flow of information between these entities provides P&C insurance providers valuable insights into any affiliations between insureds and fraudulent groups.

Link and geo-spatial analysis offer a context for a larger and more complete view of claims that might not appear false at the first glance. Link analysis allows investigators to inspect any possible connections between the parties involved. Geo-spatial analysis investigates the physical proximity of claimants and provides location-based information about the claim. Geo-spatial analysis also helps in identifying the exact area affected by a disaster and weed out fraudulent P&C insuranceclaims for surrounding areas that are not actually part of the affected area.

If companies cannot afford a custom enterprise fraudanalytics system, there are out-of-the-box analyticsfor insurance solutions available that can improve upon rule-based manual systems. For the power of analytics to be harnessed to its fullest potential, insurance companies must implement correct data-driven practices. They will need to break data silos, combine structured and unstructured data and cross-link multiple data sources to arrive at the full picture of the insureds across underwriting, Claims Management Software and policy management. To understand how Cogitate Technology Solutions can help you with implementing analytics for fraud detection, please visit www.cogitate.us.

Also See: Fraud Detection, Claims Fraud, Spatial Analysis, Multiple Data, Insurance, Fraud, Data

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