An AI damage locator from MakoLab
Our AI damage locator boosted vehicle claims processing by automating detection, recommending repairs and reducing costs for our client.
Summary
An insurance and financial services sector leader was in search of a tool that would streamline vehicle damage claims processing. We applied advanced machine learning techniques to create an AI damage locator for automating damage detection, assessing claims and recommending repairs. Our solution optimised the time spent performing all the operations involved, radically reducing the costs as a result.
Client
Industry
Automotive
Service
Damage claim management system
Deliverables
time optimisation, lower costs, enhanced fraud detection
Details
The challenge
Traditionally, vehicle damage claims processing is time consuming and resource intensive, involving manual assessments and inconsistent data processing. It is also prone to errors or fraud.
The key challenges we faced included:
- lengthy claim processing times;
- inconsistent claim valuations stemming from subjective human input;
- limitations to the possibility of scaling to 24/7 operations;
- poor anomaly and fraud detection capabilities.
The goal
We set out to design, build and deploy a solution providing:
- machine learning capabilities for effective vehicle damage claims handling;
- support for insurance companies, experts and claim handlers in detecting and evaluating damage;
- potential fraud detection through semantic data analysis and historical comparisons;
- reduced operational costs, in conjunction with process and customer experience transparency.
The solution
An AI damage locator trained on thousands of vehicle damage images. A tool that identifies damaged parts and provides automatic descriptions, it was built using Python, PyTorch and Google AI technologies.
Our AI damage locator can:
- suggest repairs on the basis of the damage it detects;
- apply its self-learning engines to estimating the extent of the damage and costs of the repairs;
- standardise data through ontology integration, enabling semantic understanding and fraud detection;
- use OCR technology to read images and detect anomalies in descriptions.
Some key features of the solution
- 24/7 automated damage assessment on the basis of photographs
- Tailored solutions aligned with company policies and regional requirements
- Transparent, data-driven decisions for repair or write-off assessments
- Internationalisation capabilities for global adaptability
Key results
Deployment of the AI damage locator resulted in:
- an 80-90% detection efficiency rate;
- 16-20% optimisation of performance time per appraisal;
- increased accuracy, achieved through the use of self-learning algorithms;
- improved claim valuation thanks to leveraging historical data;
- reduced operational costs, with automation minimising manual interventions;
- identification of inconsistencies and fraud detection boosted by ontology-based analysis;
- faster, more accurate feedback that drives an enhanced user experience.