ČSOB needed to innovate its insurance fraud detection process, increase efficiency, reduce false positives, and protect against dynamically evolving fraudulent activities.
Automated insurance fraud detection for greater efficiency
Client NameČSOB Pojišťovna
Client CountryCzech Republic
- Client typeEnterprise
- IndustryFinancial Services, Markets & Insurance
- Application areasData & Analytics / Business Intelligence, Legal, Compliance & Risk, Strategy, Planning & Decision-Making
- AI technologiesAI Agents & Task Orchestration, Forecasting & Time Series Analysis, Machine Learning
- Business impactsOperational Efficiency & Cost Savings, Risk Reduction & Compliance
- Data typesStructured Tabular Data, Time Series
- Delivery modelsProduct / Licensed Software, Service / Subscription
- DeploymentsCloud
- Key capabilitiesAnomaly, Risk & Fraud Detection, Decision Support & Augmented Analytics
- Project stagesInitial Production Deployment
- Solution formsAPI / Micro-service Interface, Automated Backend Process, Web Portal / Dashboard
Solution Description
Problem description
Solution
We implemented machine learning using our ADF framework for fraud detection. This process included historical data analysis with algorithms to identify key anomalies and clusters.
Main Users of the Solution
Data analysts, fraud specialists, risk managers.
Project timeframe (months)
6
Technologies used
ADF framework, machine learning algorithms
Additional services
- Annotation / synthetic data / dataset extension
- AI model selection and customisation
- Ongoing maintenance and retraining of the model
Use of Personal / Regulated Data
Yes
Implementation
Project Owner on the Client's Side
Head of Innovation / Digital Transformation
Participation on the Client's Side
- Domain / process experts
- Project and change management
- End users
Form of Supplier Involvement
Joint implementation with the client
Operation and Maintenance
Operational Model
Joint management with the internal team and the supplier.
Needed Competencies on the Client's Side
Data and analytics specialists, fraud detection experts.
Other Resources or Infrastructure
Vendor support; cloud infrastructure.
Impact and Results
Qualitative Benefits
Increased fraud detection capability, higher process efficiency, reduced false positives.
Quantitative Results
60% reduction in false positives, improved fraud detection efficiency.
Client Feedback
“Innovation in fraud detection has allowed us to manage fraud more effectively and focus our resources on relevant cases.”
Lessons Learned and Recommendations
Key Success Factors
Close collaboration with client data experts, adaptability of the AI framework.
Biggest Challenges
Reduction of false positives without compromising real fraud detection.
Recommendation for Others
Integrate historical data and advanced algorithms to strengthen fraud detection in your portfolio.
Promotion
Demo / Public Resources
- CompanyBlindspot Solutions
- ContactMichaela Peterková
- Emailmichaela.peterkova@adastragrp.com
- Websitehttps://blindspot.ai
- AddressKarolinská 706/3, 186 00 Praha