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Fraudulent Claim Propensity

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Fraudulent Claim Propensity

Problem/Challenge – Insurance companies conservatively lose over $80 billion per year in insurance fraud, a cost that inevitably gets passed along to individuals and businesses in the form of higher premiums. Estimates vary, but generally, a staggering 10%-30% of all claims are believed to be fraudulent. Traditional manual review doesn’t scale across billions of claims per year and rules-based fraud detection systems are expensive and slow to adapt to new fraud techniques.

Resolution – Machine learning algorithms are able to detect, recognize and prioritize likely fraudulent activity. Fraud detection process using machine learning can be used to automate claims assessment and routing based on existing fraud patterns. Additionally, you optimize customer satisfaction by not challenging innocent claims. With AI based fraud detection system in place, fraudulent claims can be flagged before they are paid which would therein reduce the cost for payers.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting fraudulent behavior, it will automatically engineer and identify the individual reasons for why each fraud will occur. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.