Automating Insurance Claims Fraud Detection for Banking & Finance

Key Highlights

  • OptiSol partnered with a leading insurance provider to automate fraud detection and improve claims processing efficiency by addressing manual review processes.
  • We implemented AI and machine learning models, including Isolation Forest and Support Vector Machines, to identify anomalous claims by analyzing key variables like claim timing and driver-related factors.
  • The solution streamlined the claims process by reducing manual intervention, enabling faster decision-making, and accelerating claim resolutions.
  • OptiSol’s machine learning-based solution improved fraud detection, preventing cybercrime, lowering overhead costs, and enhancing the client’s ability to detect fraudulent claims.

Problem Statement

01

Unlabeled Data: The client’s claims data lacked labels to identify whether a claim was fraudulent or genuine, posing challenges in detecting anomalies.

02

Manual Review: Existing mechanisms relied on manual review processes, which were time-consuming, inconsistent, and unable to effectively handle large volumes of claims.

03

Fraud Prediction: The client required a solution capable of analyzing claim details filed during the process and predicting the likelihood of fraud with accuracy and efficiency.

Solution Overview

01

A thorough evaluation of the client’s existing policies, mechanisms, and regulations was conducted to identify potential gaps in detecting fraudulent claims.

02

Comprehensive data analysis was performed to examine key variables and detect abnormalities or inconsistencies indicative of potential fraud.

03

The time duration between the accident occurrence and the claim filing date was scrutinized to identify delays or patterns that could signal fraudulent activities.

04

Driver-related factors, such as license classification, driving experience, and vehicle restrictions, were analyzed and visualized to identify unusual patterns and potential red flags.

05

Advanced machine learning models, including Isolation Forest and Support Vector Machines (SVM), were developed to identify and flag anomalous claims, enabling the detection of potential fraud.

Business Impact

01

AI Processing: AI integration streamlined insurance claim processing, improving efficiency and speeding up decision-making, resulting in quicker and more accurate resolutions.
0
%
improvement in efficiency

02

Cybercrime Detection: Advanced technology enhanced the banking sector’s ability to detect cybercrime and anomalies, ensuring stronger fraud prevention and protecting sensitive data.
0
%
increase in fraud prevention

03

Fraud Detection: OptiSol’s ML-based solution accurately identified anomalous claims, reduced false positives, and lowered overhead costs, improving fraud detection effectiveness.
0
%
reduction in overhead costs

About The Project

OptiSol partnered with the client to address fraud detection challenges in their insurance process. We implemented AI and machine learning to automate fraud detection and streamline claim analysis, improving operational efficiency. By analyzing data variables and incorporating driver-specific factors, we identified anomalies using models like Isolation Forest and SVM. This solution reduced manual intervention, accelerated decision-making, and resulted in improved fraud detection, optimized workflows, and cost savings for the client.

Technology Stack

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