AI Prediction of Repair Costs in Motor Claims Management

Claims management refers to the systematic coordination and processing of insurance claims. The process covers the entire chain from the initial notification (FNOL — First Notice of Loss) through damage capture and calculation to settlement and payout.

In the motor insurance sector, this means specifically: a policyholder reports a claim, a claims handler captures the information, and based on this data, a decision is made whether to proceed with a fictional settlement, assign a workshop, or commission an expert appraisal. How precisely and quickly this decision is made determines both customer satisfaction and the insurer's claims costs.

The system receives structured information about the vehicle and the damage during the phone-based claims notification. Based on over 800 data points per claim — including vehicle data, damage patterns, component combinations, and calculation data — PLAN D extracted features and trained a machine learning model that predicts the expected repair costs within seconds, broken down by labor, paint, and material costs.

Additionally, the system calculates the replacement value and provides a routing recommendation for the next process step. The claims handler receives this information live during the phone call and can immediately trigger the correct follow-up processes.

Straight-through processing (STP) refers to the fully automated handling of insurance transactions without manual intervention. A claim is reported, automatically assessed, and settled — the entire process runs without a claims handler needing to intervene.

In motor claims management, AI-driven prediction enables a significantly higher STP rate: when the system predicts repair costs and the optimal process step with high confidence, the case can be processed automatically. This reduces processing times and handling costs.

The combined ratio is the key profitability metric for an insurance company. It compares total expenditure on claims and administration to premium income. A combined ratio below 100 percent means the insurer operates profitably.

AI-driven claims management improves the combined ratio on multiple levels: more precise predictions reduce unnecessary appraisal costs, automated routing lowers processing costs, and more consistent settlement avoids excessive payouts.

During a phone-based claims notification, damage is captured based on the policyholder's statements. Often, not all damaged components are mentioned — not because claims handlers are careless, but because specific enough questions cannot be asked over the phone.

The AI model solves this problem: based on the components already captured, it calculates which additional components are likely also affected. If the front bumper and left headlight are damaged, the system suggests also checking the left fender and hood. The claims handler receives targeted prompts that lead to a more complete damage profile.

PLAN D designs AI systems from the outset to scale beyond the initial use case. In this project, a standalone solution evolved across six development stages into a multi-tenant SaaS platform — with its own cloud architecture, configurable rule sets per tenant, and a central admin backend.

The key was architecture: multi-tenancy, automated ML-Ops pipelines, and a low-code control logic that onboards new tenants in days rather than months. PLAN D supports the entire journey — from initial data analysis through the prediction model to productive SaaS operations with a licensing model and an entirely new business line.

The ML model achieves a prediction accuracy of 94 percent. It is trained on a wide range of damage types — from minor parking dents to wildlife damage. The average deviation from the subsequent expert appraisal is between 170 and 200 euros.

The model is based on over 800 data points per claim and 15 years of historical appraisal data. The more claims the model processes, the more precise the prediction becomes — a continuous learning effect that further improves accuracy over time.

Already to a large extent today, and for up to 90 percent of all claims within the coming years. AI can predict repair costs, analyze damage images and vehicle data, automatically process documents, recommend the optimal process step, and independently trigger follow-up processes such as workshop booking or appraisal commissioning. Customer communication can also be automated, in writing via email or by phone through voice-controlled systems. For standard claims with clear damage profiles, processing already runs fully automatically, known as straight-through processing.

The key factor is the type of damage: a parking dent, hail damage, or a typical rear-end collision can be reliably assessed algorithmically. With a growing data foundation, better models, and multimodal processing of text, image, and speech, the share of automatable cases will continue to rise. Human decision-making remains relevant where liability questions, contradictory statements, or entirely new damage patterns arise. For everything else, AI is becoming the standard.