Insurance Claims Auditor AI-Powered Fraud Detection & Risk Assessment System

Result

▫️$4.2M in fraudulent claims identified in first quarter
▫️Investigation time reduced by 68% for routine claims
▫️False positive rate decreased from 35% to 12%
▫️Previously undetected fraud rings identified through network analysis
▫️Legal defensibility of fraud determinations improved by 40%

How We Helped a National Insurance Provider Identify $4.2M in Fraud—And Cut Investigation Time by 68%

Client Overview

▫️Confidential (national insurance provider)
▫️Industry: Insurance / Claims Processing Engagement
▫️Duration: 18 weeks Team
▫️Deployed: 2 AI engineers, 1 fraud specialist, 1 insurance domain expert from TechSteck Solutions

Goal

▫️ Detect potentially fraudulent claims with 90%+ accuracy
▫️ Cut investigation time in half for routine claims
▫️ Provide investigators with a natural language query tool
▫️ Uncover hidden relationships between claims, providers, and households
▫️ Deliver risk-scored, evidence-backed fraud assessments at scale

Problem

- - ▫️ Fraudulent claims were slipping through due to siloed systems and limited historical analysis
    ▫️ Investigators spent weeks manually reviewing low-risk claims
    ▫️ Patterns of coordinated fraud were rarely surfaced until after payouts
    ▫️ False positives created unnecessary costs and legal headaches

Solution

- - TechSteck Solutions built an AI-powered Claims Auditor—an advanced RAG-based fraud detection system that parses documents, analyzes relationships, and flags risk with source-cited explanations. It combines OCR, graph analysis, and machine learning to spot fraud faster and smarter
  - ▫️ OCR engine for document digitization and structured extraction
    ▫️ LangChain for orchestration and natural language interface
    ▫️ Neo4j for graph-based network analysis
    ▫️ Custom fraud risk engine with ML-based scoring
    ▫️ GPT-powered RAG models for real-time question answering

How It Works

1. 1. ▶ Claims Document Processing
  - - ▫️OCR digitization of claim forms, medical records, and police reports
    - ▫️Structured data extraction for policy details and coverage
    - ▫️Medical terminology recognition and standardization
    - ▫️Historical claim linkage across customers and providers
  1. ▶ Natural Language Claims Investigation
  - - ▫️Interactive query interface for adjusters and investigators
    - ▫️Real-time access to policy details and coverage limitations
    - ▫️Historical claim pattern analysis across customer history
    - ▫️Medical procedure and billing code verification
  1. ▶ Network Analysis
  - - ▫️Graph-based relationship mapping across claims
    - ▫️Detection of provider networks with unusual billing patterns
    - ▫️Family and household relationship mapping
    - ▫️Geographic clustering of similar claims
  1. ▶ Fraud Risk Scoring
  - - ▫️AI-generated risk scores based on multiple factors
    - ▫️Explanation of risk factors with supporting evidence
    - ▫️Inconsistency detection across claim documentation
    - ▫️Prioritized investigation queue based on risk levels

Conclusion

By combining OCR, network analysis, and advanced risk scoring, TechSteck Solutions gave this insurer a proactive fraud detection system that improves over time, surfaces previously invisible risks, and slashes wasted time on low-risk claims—turning compliance into a competitive advantage.

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