1️⃣ Objective
Build an end-to-end analytics platform for vehicle insurance claims that identifies fraudulent or suspicious claims, predicts claim severity and cost, optimizes reserves and pricing, and provides operational dashboards to speed up claim handling and reduce loss ratios.
Key Goals:
✨Detect likely fraudulent claims early using supervised & unsupervised models.
✨Predict claim cost & settlement time (severity regression).
✨Segment claims by risk and recommend reserve amounts for faster financial planning.
✨Provide dashboards for adjusters to prioritize investigations and automate routine workflows.
✨Measure impact of analytics on detection rates, average settlement, and operational efficiency.
2️⃣ Problem Statement
Insurance companies face rising claim volumes and complex fraud patterns. Manual review is slow and expensive; inaccurate reserves and pricing increase financial risk. There is a need for a data-driven system that improves detection accuracy, speeds claim processing, and helps actuaries and underwriters make better decisions.
3️⃣ Methodology
The project will combine data engineering, statistical modeling, machine learning and visualization:
✨Phase 1 — Data Ingestion & Warehouse: Collect policy, claimant, vehicle, claims history, adjuster notes, images, telematics (if available), and third-party data (repair shops, police reports).
✨Phase 2 — Feature Engineering: Create features: claim history counts, time-to-report, claim narrative embeddings (NLP), image features (vision models), geo/time anomalies, and telematics-derived driving risk metrics.
✨Phase 3 — Fraud Detection Models: Train supervised classifiers (XGBoost, CatBoost) on labeled fraud/not-fraud; augment with unsupervised anomaly detection (Isolation Forest, Autoencoders) to flag new patterns.
✨Phase 4 — Severity & Cost Prediction: Build regression models (LightGBM / neural nets) to estimate claim cost and settlement time; calibrate with actuarial loss development factors.
✨Phase 5 — Rule Engine & Scoring: Combine model outputs with business rules to compute a risk score, triage level, and suggested reserve.
✨Phase 6 — Dashboard & Workflow Integration: Visualize alerts, case timelines, and model explanations (SHAP). Integrate with claim management systems for workflow automation and investigator assignment.
✨Phase 7 — Evaluation & Monitoring: Deploy A/B tests for new triage policies, monitor model drift, and retrain on fresh labeled outcomes.
4️⃣ Dataset
Core Entities:
✨Internal claim system exports: claim header, line items, payment history.
✨Policy & customer master data: vehicle make/model, age, coverage, underwriting info.
✨Third-party: police reports, repair-shop estimates, parts pricing, court records.
✨Multimedia: photos of damage, CCTV (optional), telematics / dashcam (optional).
✨Label data: past confirmed fraud cases, settlement outcomes.
Patient Records Table (Sample):
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨AUC / ROC: Classifier discrimination ability.
✨MAE / RMSE for Severity: Error metrics for cost prediction.
✨Reserve Accuracy: % difference between suggested reserve and eventual paid amount.
✨Investigation Efficiency: Avg time-to-resolution for flagged vs non-flagged claims.
✨Operational KPIs: Reduction in average settlement time, lower leakages, and savings from prevented fraud.
✨Model Stability: Drift detection metrics and periodic re-training performance.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Source 1: SAP PM / S/4HANA EAM
Maintenance Notifications, Work Orders, Equipment Master Data, Functional Locations.
Source 2: IoT & Sensor Data
Asset health readings (Temperature, Vibration, Pressure) for predictive modeling.
Source 3: Inventory (MM) & Finance (CO)
Spare parts consumption, labor costs, and settlement figures for maintenance activities.
SAP Data Services (BODS) / SLT
Extracts structured PM/EAM data, including historical work order details and costs.
SAP Data Intelligence / PaPM
Ingests and pre-processes unstructured/time-series sensor data and runs predictive models.
Central Data Lake (e.g., AWS S3)
Long-term storage for high-volume, granular IoT data prior to selective loading into HANA.
SAP BW/4HANA (Maintenance Data Mart)
Integrated data model for Maintenance History, Cost Aggregation, and Budget vs. Actual reporting.
SAP HANA Predictive Models
Calculates Remaining Useful Life (RUL), Failure Probability, and optimized service intervals.
SAP Analytics Cloud (SAC) / Fiori Launchpad
Visualizes key metrics: OEE, MTTR/MTBF, Maintenance Backlog, Cost Variance, and Predictive Alerts.
Source 1: SAP PM / S/4HANA EAM
Maintenance Notifications, Work Orders, Equipment Master Data, Functional Locations.
Source 2: IoT & Sensor Data
Asset health readings (Temperature, Vibration, Pressure) for predictive modeling.
Source 3: Inventory (MM) & Finance (CO)
Spare parts consumption, labor costs, and settlement figures for maintenance activities.
SAP Data Services (BODS) / SLT
Extracts structured PM/EAM data, including historical work order details and costs.
SAP Data Intelligence / PaPM
Ingests and pre-processes unstructured/time-series sensor data and runs predictive models.
Central Data Lake (e.g., AWS S3)
Long-term storage for high-volume, granular IoT data prior to selective loading into HANA.
SAP BW/4HANA (Maintenance Data Mart)
Integrated data model for Maintenance History, Cost Aggregation, and Budget vs. Actual reporting.
SAP HANA Predictive Models
Calculates Remaining Useful Life (RUL), Failure Probability, and optimized service intervals.
SAP Analytics Cloud (SAC) / Fiori Launchpad
Visualizes key metrics: OEE, MTTR/MTBF, Maintenance Backlog, Cost Variance, and Predictive Alerts.
9️⃣ Expected Outcome
✨Higher precision in fraud detection and early triage of suspicious claims.
✨Accurate claim cost predictions and improved reserve allocation.
✨Reduced investigation workload via prioritization and explainability tools.
✨Better operational KPIs: faster settlement, lower leakage, and measurable cost savings.
✨Production-ready model deployment with monitoring, retraining pipelines, and a documented integration playbook.