Secure Claim Tracker Bot

Secure Claim Tracker Bot uses OCR, NLP, and GenAI to analyze claims, detect anomalies, extract insights, and identify fraud—automating insurance processing with accuracy and speed.

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ClaimTrackr Bot

ClaimTrackr is an AI-powered automation tool designed to simplify and accelerate the insurance claim process. By combining OCR, NLP, document embeddings, and fraud detection, it reduces manual effort, improves accuracy, and delivers faster claim approvals.

Problem Statement

Insurance claim processing is traditionally time-consuming, error-prone, and data-heavy. Manual validation of policy terms, medical records, and historical claims increases operational burden and delays settlements. ClaimTrackr solves this through intelligent automation using AI, GenAI, and machine learning.

Objectives

  • Automate insurance claim processing with AI and NLP
  • Build a chatbot to support real-time claim evaluation and feedback
  • Deliver valid/invalid claim decisions with detailed summaries
  • Act as an educational resource referencing policy handbooks

Context

ClaimTrackr leverages:

  • OCR + NLP to interpret medical and policy documents
  • AI + GenAI for deep insights and predictive analysis
  • EDA for trends and anomaly detection
  • Embedded fraud detection for risk analysis

How It Works

Step 1: Data Collection + EDA

  • Automatically gathers customer records, policy info, external data, and previous claims
  • Performs Exploratory Data Analysis to uncover patterns and anomalies

Step 2: Embedding Generation

  • Converts claim-related documents into vector embeddings
  • Enables efficient semantic search and matching across policy terms and claim details

Step 3: Query Execution + Report Generation

  • Uses OpenAI LLM to analyze claim validity and generate comprehensive reports
  • Includes risk evaluation, summaries, and recommendations

Step 4: Parsing + Final Output

  • Extracts key insights from reports and presents clean, actionable data
  • Speeds up approvals and reduces processing time

Bonus: Fraud Detection Module

  • Detects potential fraud using data comparison, AI pattern recognition, and historical claim analysis

Key Inputs

  • Insurance company handbook & supporting documents
  • Previous claim data and historical patterns
  • Claimant details: personal info, medical records, bills

Architecture

PDF/Image Input ➜ OCR (EasyOCR/OpenCV) ➜ Preprocessing ➜
Embeddings (LangChain/OpenAI) ➜ Query Engine ➜
LLM-based Report Generation ➜ Parsing ➜ Final Decision Output

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