Insurance Claim Fraud Detection

Real-time detection of fraudulent claims
Overview
Our AI-driven fraud detection leverages Machine Learning-based proprietary models to predict the likelihood of a submitted insurance claim to be fraud, based on the various attributes provided in the claim form. It also considers the history of the customer’s insurance claims. The information is gathered from multiple data sources to make this prediction.
See how our Insurance Claim Fraud Detection solution can help your organisation
Increased accuracy
Our proprietary ML models are tuned to minimize false positives, thereby enhancing the auto-actioning aspect. AI/ML models help decrease false positives significantly.
Enhanced fraud detection
With AI/ML-based risk scoring, real-time decisions are enabled through seconds, ensuring enhanced fraud detection.
Minimal Fraud run-time
Early detection of unknown patterns with advanced AI/ML models ensures minimal fraud run-time.
Quicker Insights
The NLQ Engine can instantly extract the output from millions of records, thus helping organizations with faster decision-making and increased profitability.
See how our Insurance Claim Fraud Detection solution can help your organisation
Increased accuracy
Our proprietary ML models are tuned to minimize false positives, thereby enhancing the auto-actioning aspect. AI/ML models help decrease false positives significantly.
Enhanced fraud detection
With AI/ML-based risk scoring, real-time decisions are enabled through seconds, ensuring enhanced fraud detection.
Minimal Fraud run-time
Early detection of unknown patterns with advanced AI/ML models ensures minimal fraud run-time.
Quicker Insights
The NLQ Engine can instantly extract the output from millions of records, thus helping organizations with faster decision-making and increased profitability.

Check out Insurance Claim Fraud Detection solution features

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Subex NLQ Engine
Subex NLQ engine uses natural language to derive precise insights from the data for a better-informed decision making. It breaks barriers like database-specific query language and eases the decision-making efforts for BI and non-coding users.
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