Skip links

Insurance fraud costs the economy over $2 billion per annum – our ML model helped a leading insurance provider to detect fraudulent claims

Our solution analyses large volumes of claims data in real-time, to detect suspicious patterns / anomalies, allowing the prevention of fraudulent claims from settlement process

What we were solving for

Quality of detection processes: Insurance companies struggle to detect fraudulent claims, given the use of sophisticated methods to conceal fraud and quantum of data to analyse

Resourcing: Assessing and processing claims is a time-consuming and labor-intensive process and requires a significant investment in technology and personnel. Furthermore, insurance companies must comply with a wide range of regulations related to claims handling and processing

OptiSol solution

Created an unsupervised learning model that would consume data provided in an insurance claim and then flag if suspicious – a classification model, trained and tested with historical data

Isolation Forest and SVM (Support Vector Machine) model was constructed to identify claims that stood apart as anomalous

Data is analysed to examine and check for abnormal data points, as well as to comprehend variables that may cause anomalies. e.g., the duration between the accident date and the claim reported date

Driver specific analysis is automated in which the license class, experience and vehicle-based restrictions are considered

What our Clients say

This website uses cookies to improve your web experience.
Explore
Drag