Motor claims fraud detection model (Baloise)
by Helvetia Baloise (Baloise) · Primary insurer
Market unit: CH
Baloise's Data Lab and the Swiss consultancy LogiLab developed an adaptive, self-learning framework that combines a weighted rule-based approach with machine-learning models to flag suspected fraud in motor insurance claims for further investigation. The framework can be tuned to different KPIs, such as the fraud-suspicion detection rate or expected savings. On historical data it detected about 65% more fraud within the 20% most suspicious cases than the previous approach; LogiLab stated that it was being implemented in operational business (page last updated December 2023). Platform, volumes and go-live date are not publicly documented. Baloise has been part of Helvetia Baloise since December 2025.
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AI capability and maturity
AI capability level
Capability Maturity Score (CMS)
5 / 6
Business Adoption Score (BAS)
3 / 6
AI Development Index (ADI)
15 / 36
Business context
- Purpose of use
Detect and prevent fraudulent claims earlier in the lifecycle.
- Value chain steps
- Claims managementFraud detection and prevention
- Insurance branches
- Motor vehicle insurance
- Application scope
- Internal only
- Business criticality
- Business critical
- Country or market unit
- CH
Value and impact
- Added value category
- Cost reductionRisk reduction
- Added value estimation
Targets a measurable uplift in detected fraudulent claims value.
- Time to value (months)
- 8
- Quality gain
About 65% higher fraud detection rate in top 20% most suspicious cases (back-test on historical data)
Technical details
- AI category
- Machine learning and predictive analyticsAnomaly detection
- Interfaces used
Claims system event stream
- Architectural insights
Streaming feature pipeline feeding a gradient-boosted scoring model.
- Automation level
- 3/5
- Estimated IT difficulty
- 4/5
Compliance and governance
- EU AI Act risk category
- Not available
- EU AI Act role
- Not applicable
- Relevant policies
- Swiss Federal Act on Data ProtectionFINMA guidelines
- Responsibilities
Special investigations unit reviews every flagged case before action.
- Data used
Claims data, payment records, third-party fraud indicators.
Metadata
- Development type
- Minimum viable product
Publications over time
When this solution was reported on. Rumors are unverified mentions.
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Related solutions
ZFinder is a fraud-detection model developed in-house by Zurich Seguros (Spain) that combines big data and machine learning, comparing each claim with thousands of similar claims and improving with every analysis. Zurich applied it first to home insurance (announced June 2018), with plans to extend it to other lines under the supervision of its internal investigation team. Zurich reported that automatic fraud detection doubled after launch and that in 76% of claims investigated via ZFinder a loss adjuster's work was not essential. The most common fraud types it found were simulated claims (47%), claims for excluded cover (22%) and concealed pre-existing damage (17%). No newer figures were found.
Since September 2021 Baloise has used a virtual voice assistant, developed with Spitch and NTT DATA, in its Swiss non-life customer service. Callers state their concern in standard German or Swiss German dialect instead of using the phone menu; the assistant recognises the intent, mainly claims notifications, and transfers the call with an on-screen text summary for the agent. At launch about 40% of German-language non-life enquiries passed through the assistant and 85% of callers reached the right expert at the first attempt; French was planned for a later stage. Spitch's case study (updated April 2025) describes a hybrid on-premise and cloud setup. Status after the December 2025 Helvetia Baloise merger is not documented.
by Zurich Insurance Group (Zurich Switzerland, Zurich Brazil)
CH, BR
Zurich's in-house claims fraud-detection system was developed by ZCAM (Zurich Customer Active Management), its wholly owned data-analytics company with about 50 specialists. In Switzerland, most of the more than 600,000 claims received each year pass through a proprietary fraud-scoring tool fed by the Allegro claims system and receive a 0-100 risk score, with high scores prompting closer review by the Special Investigation Unit. Alongside the rule-based system, a machine-learning model has been piloted in German since February 2024 for property and commercial lines. Zurich Brazil adopted ZCAM's system in its affinity business (extended warranties, mobile-phone theft) and extended it to motor claims; the affinity fraud detection rate trebled (Zurich, September 2024).