The challenge:
Insurance fraud causes significant losses
Fraudulent claims represent a significant challenge for the insurance industry, inflating costs and undermining trust.
This pan-European insurance company experienced a wave of fraudulent claims across their auto, home, and commercial insurance sectors in different countries.
Traditional insurance fraud detection methods mainly depended on manual reviews and basic analytics and were too tedious and slow to meet the ever-growing customer expectations.
Trying to meet this demand, the company inadvertently allowed several fraudulent claims to fall through the cracks and lost millions of EURO in false payouts.
The company’s investigators regularly struggled to access verifiable data to substantiate the claim, resulting in extended disputes. With the growing sophistication and scale of insurance fraud schemes, the company started to look for a solution to unblock its operations, satisfy customers, and protect its bottom line.
Assessing available solutions
To address these challenges, the company’s leadership team explored the following options:
#1: Enhancing manual processes
The first idea was to increase the number of claim adjusters and investigators. While this change improved detection rates, the related labour costs and low scalability potential made the company look for different solutions.
#2: Advanced data analytics
Implementing a more advanced analytics platform promised better fraud detection mechanisms by analysing historical patterns and flagging anomalies. However, the new system lacked real-time capabilities and integration with emerging data sources.
#3: IoT and AI platform
Finally, the company assessed the potential of using IoT-enabled devices to capture real-time data and AI algorithms to improve anomaly detection.
This approach promised not only better insurance fraud detection but also unlocking new efficiencies by delivering actionable insights and process automation. This multitude of possibilities was precisely why the company opted for its implementation.
The winning solution:
real-time IoT data + AI + automation
As the first step, the company partnered with an experienced IoT & AI technology provider to design and deploy a comprehensive insurance fraud detection system.
The new solution consists of the following features:
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Real-time data from IoT devices
Connected vehicle telematics provides accurate incident details such as speed, location, and impact severity during accidents. Following the same principle, smart home sensors detect environmental anomalies like water damage or fires, verifying if the client’s claim matches recorded events.
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AI-driven anomaly detection
Machine Learning models analyse claims and compare them to IoT data inputs to spot any suspicious patterns potentially indicating fraud. Cross-referencing policyholder behaviour with external datasets like weather reports or repair shop records further enhances detection accuracy.
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Automated claim validation
AI algorithms flag suspicious claims for further review, while low-risk claims get fast-tracked for approval and settlement. Blockchain integration supports transparency, tamper-proofing records, and bolstering trust in the validation process.
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Investigator support tools
A centralised dashboard provides the insurer’s investigators with detailed insights, including flagged anomalies and supporting data. This repository allows the investigation team to accelerate and streamline their workflows significantly.
Results and plans
A01
Agile project methodologies
Thanks to working with an external AI and IoT consultant, the insurance company’s team could rest assured that the project was on track while remaining focused on its core business.
Thorough planning and business analysis combined with Agile project methodologies ensured regular releases on time and within budget.
A02
Within the first year of implementation, the insurer saw the following improvements:
- Fraudulent insurance claim payouts dropped by 35%, saving the company nearly €10 million annually.
- Claim processing times improved by 40% thanks to automation.
- Investigators reported a 50% increase in productivity as the system prioritised high-risk cases and provided comprehensive supporting data.
A03
