Fraud detection using Machine Learning
Fraud detection in Insurance industry is a known problem with variety of fraud patterns and fraud types. Manual gathering of large and voluminous amounts of structured and unstructured data for fraud detection analysis is tedious, time-consuming and highly error-prone, often resulting in too many false positives.
Machine learning techniques facilitate predictive accuracy, enabling loss control units to achieve higher coverage with less false positive cases. Machine learning models learn from data without having to rely on pre-determined rules and shape their own rules by figuring out what reallaay matters. Also machine algorithms detect complex anomalies or patterns in user behaviour or the nature of transactions, by comparing the data from previously used models and inspecting fraud in the form of rules (patterns)
Investigation is expensive and doesn't happen in time bound manner. This results in
- Increased premium cost
- Lack of trust during the claim process
- Pressure from industry regulators
- Impacts on process efficiency causing delayed issuance in policy / pay-outs