Achieving 90% Accuracy in Insurance Claim Cost Prediction: AI-Driven Medical Records Analysis

Insurance claims assessment depends on reviewing complex medical records, a process that is manual, inconsistent, and slow. Different reviewers reach different conclusions from the same files. Red Buffer built an AI platform that automates extraction, visually classifies documents, and predicts claim costs with 90% accuracy.

Project Overview

An AI-powered medical records analysis system combining NLP, computer vision, and deep learning to automate claims assessment, delivering 90% cost prediction accuracy and 40% faster processing.

ROLE

NLP pipeline design (AWS Textract, Comprehend Medical), computer vision integration (YOLOv5), predictive model training (LSTM and regression), serverless architecture, and analytics dashboard delivery.

TOOL

AWS Lambda, AWS DynamoDB, AWS S3, AWS Textract, AWS Comprehend Medical, AWS OpenSearch, AWS QuickSight, Amazon Honeycode, Python, PyTorch, Pandas, React.js, YOLOv5, OpenCV, Beautiful Soup.

DURATION

Multi-phase engagement with iterative model improvements and performance optimization.

Our Approach

  1. NLP-Driven Medical Data Extraction

    Used AWS Textract and Comprehend Medical to extract structured entities, diagnoses, procedures, and clinical attributes from unstructured records, converting free-text medical documents into analyzable data.

  2. Computer Vision for Document Classification

    Applied YOLOv5 and OpenCV to classify and process scanned medical documents, handling mixed formats including digital text, scanned images, and handwritten notes that commonly appear in medical records.

  3. Predictive Claim Cost Estimation

    Trained deep learning models including LSTMs and regression models on historical claim data to estimate insurance claim costs, learning from patterns across thousands of past cases to predict new ones.

  4. Serverless Processing & Visualization

    Deployed the system on AWS Lambda and DynamoDB for real-time serverless processing. AWS QuickSight dashboards present insights and predictions, enabling rapid decision-making for claims teams.

Why It Matters

Any insurance workflow that depends on structured analysis of medical records, including workers’ compensation, disability assessment, and life insurance underwriting, faces similar extraction, classification, and prediction challenges. The combination of NLP, computer vision, and predictive modeling in a cloud-native architecture creates a scalable pattern applicable across claims-heavy industries.

Outcome

40% Reduction in Processing Time


Automated analysis accelerated insurance claim and malpractice assessments.

90% Accuracy in Cost Prediction


Deep learning models delivered highly reliable claim cost estimates.

30% Operational Cost Savings


Cloud-based automation reduced reliance on manual review teams.

Improved Objectivity & Consistency


Standardized AI analysis removed reviewer-to-reviewer variability.

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