ClaimWise

Insurance claim automation platform using NLP and computer vision — cuts manual verification time by 80%.

The Problem

Insurance claim processing remains manual, slow, and inconsistent. Adjusters must review documents, verify damage, cross-reference policy details, and detect fraud — a process that takes days per claim and produces uneven results across cases.

Tech Stack

Python

FastAPI

TensorFlow

React

AWS

Key Features

OCR document parsing that extracts structured data from claim forms, invoices, and medical reports using NLP models
Fraud detection engine that computes risk scores using ML models trained on historical claims data
Automated underwriting that applies business rules and ML predictions to make coverage determinations
Real-time dashboard providing claims processors a centralized view of claim status, risk scores, and actionable tasks

Challenges & Solutions

Document Understanding at Scale

Problem

Insurance documents arrive in diverse formats — scanned PDFs, photos of forms, digital files — with unstructured layouts that vary by provider.

Solution

Combined OCR preprocessing with fine-tuned BERT-based NLP models to extract relevant fields (policy numbers, dates, amounts) with high accuracy across document types.

Computer Vision for Damage Assessment

Problem

Analyzing vehicle and property damage from user-uploaded photos required accurate classification despite varying lighting, angles, and image quality.

Solution

Trained custom CNN models on labeled damage datasets with aggressive data augmentation to handle real-world variability, achieving consistent classification across lighting conditions.

Fraud Detection Sensitivity

Problem

Fraud detection systems often face a trade-off between catching fraudulent claims and flagging legitimate ones, eroding trust with false positives.

Solution

Implemented an ensemble approach combining rule-based signals with gradient-boosted tree models, using calibrated probability thresholds adjustable by claim value tier.

End-to-End Pipeline Latency

Problem

Processing a claim from document upload to decision output needed to complete in seconds to maintain real-time workflow usability.

Solution

Architected an async FastAPI backend with AWS SQS for task queuing, parallel processing of independent checks, and incremental result streaming to the dashboard.

Outcome

ClaimWise reduces manual verification time by 80%, enabling claims processors to handle five times more claims per day while maintaining consistent, auditable decision quality and reducing fraud losses.