AI in Insurance
AI is ideally suited to fraud detection for insurance claims. Machine learning models can be used to automate claims assessment and routing based on existing fraud patterns. This process flags potentially fraudulent claims for further review, but also has the added benefit of automatically identifying good transactions and streamlining their approval and payment. More advanced anomaly detection systems can be deployed to find new patterns and to flag those for review, which leads to prompt investigation of new fraud types. AI systems can also provide clear reason codes for investigators, so they can quickly see the key factors that led the AI to indicate fraud which streamlines their investigation. With AI based fraud detection, fraudulent claims can be evaluated and flagged before they are paid, which reduces costs for insurance providers and helps reduce costs for consumers.
AI is a great solution for customer churn prediction as the problem involves complex data over time and interactions between different customer behaviors that can be difficult for people to identify. AI can look at a variety of data, including new data sources, and at relatively complex interactions between behaviors and compared to individual history to determine risk. AI can also be used to recommend the best offer that will most likely retain a valuable customer. In addition, AI can identify the reasons why a customer is at risk and allow insurance company to act against those areas for the individual customer and more globally.
Personalized Rate Management
Determining the rate for an insurance policy has traditionally been driven by simple factors. For example, in auto insurance, the rate was determined by the year, make and model of the vehicle. This method does not consider individualized factors such as driving behavior, location, weather, or time of day, all of which could have a significant impact on individual risk. Assessing individual rates for consumers is difficult using traditional processes because of the volume of data available. Segmenting customers can be helpful, but even these macro groups miss the opportunity presented by the large volumes of data being collected at the individual level.
AI is ideal for automating repetitive processes and finding anomalous behavior that may indicate fraud or other issues. AI can streamline processing by scoring claims for issues like fraud and allowing claims with low probability of an issue to be processed automatically while higher probability claims are routed to investigators for review. AI models can also provide reason codes for claims denials, which streamlines the review process by allowing the analyst to quickly resolve those issues, so the claim can go through, or by showing the investigator the key issues to focus on. Reason codes are also helpful for customers because they can inform them of issues with their claim which can help them to fix the claim for reprocessing, approval and payment.