Home > Business Applications > Nia Fraud Detection For Insurance

Key features

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
  • 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.
Better business outcomes through accurate predictions
  • 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 Explainability Framework
  • Prediction explanation to facilitate auditability and regulatory compliance for ML models.
Minimum technology infusion
  • An over the top solution which integrates with your existing claims processing systems to retrieve data and to also trigger actions through Open APIs

Key benefits

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).

Improve Combined Ratio
  • 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
Decrease Operational Overheads
  • Improve claims processing efficiencies, productivity and compliance based on intelligent prioritization of risky claims, data preparation, analysis and streamlining of post-claims investigations.
Improve Overall Customer Experience
  • Faster and more efficient claims handling to improve customer experience

Contact Us