Insurers worldwide are facing mounting economic and regulatory pressures on profitability. However, with an increase in fraud in automobile insurance, operational costs for claims data preparation and analysis is on the rise, while post-claims investigations are adding to the customer dissatisfaction.
When coupled with the need of faster claims processing to improve customer experience, it is vital to manage fraud leakages, false positives and operational costs.
What if there is an AI/ML based solution that can help you to deal with the above problems? What if there was a solution that could leverage unstructured external data to identify emerging fraud patterns, like fraud rings, staged accidents, low-speed impacts and much more.
Nia Fraud Detection for Insurance is an Artificial Intelligence (AI) and Machine Learning (ML) enabled application that sits on top of the existing claims processing systems to automatically enrich it with fraud detection intelligence.
The application leverages advanced ML to ingest unstructured data like Vehicle telematics, call logs and transcripts along with internal transactional data to evaluate each claimant’s intent along with evaluating the claim for fraud.
By Leveraging Infosys Nia TM the purposeful AI and automation platform, Nia Fraud Detection for Insurance generates accurate predictions and suggestions to help automate the decision making process and alleviate bottle necks in your business processes. The Nia platform helps you move from descriptive (reactive) to predictive to real time prescriptive analytics and automation.
With Nia Fraud Detection for Credit Cards, enterprises can both fortify and differentiate their business using the real power of data by leveraging recent advancements in AI/ML without having to deal with the complexity that comes along with it.
Real-time Fraud evaluation, Predictive modelling, Real-time value-based scoring, Data management, Machine learning, Deep Learning, NLP to readily ingest, transform data, build models, predict outcomes, automate processes.
Claims Prioritization based on risk score
Process optimization in the form of risk predictions, ML-based queue prioritizations help claims handler focus on accounts with higher likelihood of fraud
Prediction explanation to facilitate auditability and regulatory compliance for ML models.
An over the top solution which integrates with your existing claims processing systems to retrieve data and to also trigger actions through Open APIs
Nia Fraud Detection for Insurance helps you detect fraudulent claims with realtime risk evaluation of incoming claims It seamlessly introduces insights from external unstructured data to enrich the fraud intelligence, reduce false positives and identify different types of fraud such as fraud rings, staged accidents and low speed impacts (LSI).
Utilization of internal and external data to evaluate the claimant’s intent and claims risk, possible with accurate AI led fraud predictions thereby reducing claim losses
Improve claims processing efficiencies, productivity and compliance based on intelligent prioritization of risky claims, data preparation, analysis and streamlining of post-claims investigations.
Faster and more efficient claims handling to improve customer experience
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