Analytics to detect Insurance Fraud
Insurance fraud has been the industry’s main focus. Insurance industry incurs a loss of millions of dollars due to fraudulent reports every year. Manual detection of fraud is almost impossible to find in an industry that is drowned in data. Industries have started using analytic techniques like big data, reporting analytic to combat fraudulent claims. Since analytics integrate data into diversified channels and combine data into third party data, effective fraud detection is easily possible. Insurance companies should consider the possibility that 10 to 20 percent of all claims may be fraudulent. The impact when a fraudulent claim occurs is enormous; it weakens an insurers’ financial position, and sabotage its ability to offer competing rates and indemnifies the respectable and potentially profitable business. Government have responded with new regulations and centralized fraud bureau, Insurers have come up with special investigation unit to combat the fraudsters. The first big thing is that it is difficult to detect fraud given the large amount of data in an insurance industry. It is impossible to predict future trends in fraudulent activities. Fraudsters are becoming more inventive and resourceful, if we push them hard in one area they move around the next.
Insurers also need to be more innovative and resourceful by using a blend of different approaches and by exploiting the advantages of those methods/technologies.
Thresholds are set with anomaly detection and key performance indicators (KPI’s) associated with a specific task and key guidelines. When it exceeds the threshold amount, the event is reported. These outliers or anomalies helps in detecting new or previously unknown fraud. In recent years, insurers have turned to predictive analytics modeling, the most accurate out of all the modeling techniques, reducing the need for tedious hands on account management. As fraudsters adopt new methods, models must be updated to reflect new patterns. Predictive modeling helps insurers to target potential clients, identify their approach of buying insurance product and determine their relevant product and pricing that helps in pro-actively identifying fraudulent claims.
The prime purpose of Operational Intelligence report is to monitor business activities and identifying the inefficiencies and threats to provide operational solutions. It helps in taking appropriate decisions based on timely information. Operational Analytics are built right into application systems or business processes such as underwriting, claims, marketing etc…
The operational report has the following benefits:
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