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Insurance

Insurance

Problem:

Fraud Detection

In the insurance sector, detecting fradulent or anomalous claims is a major challenge over the last few decades. While actuarial science came up with a number of solutions based primarily on statistical models, these relied on historical use cases and well documented features to identify anomalous claims or fraudulent profiles. With the advent of AI and Machine Learning, the power of analyzing a wide spectrum of features without explicitly engineering them to identify anomalies presents a practical and enahanced solution. In recent years Deep Learning algorithms were able to uncover hidden relationships and patterns that were unknown before using sampled data and with a restricted set of features.

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Risk Assessment

One of the critical factors that Insurance companies focus on is to identify which of their customers will potentially make a claim. Multitude of variables are analyzed using a host of different algorithms to determine. State of the art Algorithms can be used to detect relationship between claims, detection of the missing observations, etc. Consequently, an individual customer’s portfolio can be developed. Using this portfolio models that can forecast claims can be developed and hence the premiums can be dynamically estimated.

Claims Management

Data Science has a role in managing insurance claim and related complaints processes more efficiently that can benefit both the insurer and policyholder. Using Natural Language Processing, complaints can be better understood and addressed. This in turn improves the brand image of the insurance provider and more importantly the claims settlement ratio which is the most important performance metric of any insurance provider.

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