Automated Claims Processing and AI Fraud Detection Workflow

Automated Claims Processing and Fraud Detection

1. Claim Submission

1.1 Customer Initiation

Customers submit claims through a digital platform, such as a mobile application or website.

1.2 Data Collection

AI-driven tools like Chatbots collect necessary data from customers, including incident details, policy information, and supporting documents.

2. Initial Claim Assessment

2.1 Automated Verification

AI algorithms, such as Optical Character Recognition (OCR), extract and verify information from submitted documents.

2.2 Eligibility Check

AI systems analyze policy details to confirm coverage and eligibility for claims.

3. Fraud Detection

3.1 Anomaly Detection

Utilize AI models like Machine Learning (ML) to identify patterns and flag anomalies in claim submissions that deviate from historical data.

3.2 Risk Scoring

AI-driven products such as Fraud Detection Engines assign risk scores to claims based on various factors, including claimant history and claim type.

4. Claims Processing

4.1 Automated Decision Making

AI systems automatically approve or deny claims based on predefined criteria and risk assessments.

4.2 Human Review

Claims flagged for potential fraud or complexity are escalated to human adjusters for further investigation.

5. Communication and Resolution

5.1 Automated Notifications

AI tools send automated notifications to customers regarding claim status updates, using platforms like Email Automation Tools.

5.2 Final Resolution

Once claims are processed, payments are issued through automated payment systems integrated with banking APIs.

6. Post-Processing Analysis

6.1 Data Analytics

AI-driven analytics tools analyze claim data to identify trends, operational efficiencies, and areas for improvement.

6.2 Continuous Learning

Machine learning models continuously improve based on new data, enhancing future fraud detection and claims processing accuracy.