Trōv: AI-Powered Fraud Detection

Introduction 

Trōv is a global leader in embedded insurance, powering the future of digital insurance distribution and emerging mobility. Its robust insurtech platform empowers financial institutions and insurance incumbents to easily adopt insurance products within other digital experiences and increase recurring revenue.

“We’re in touch with them daily, and they’re very much integrated into everything we do. They’ve taken on a lot of work and consistently delivered on it. We work in an agile setting, with two-week sprints. Our velocity has increased, and the whole company has started working in the same methodology. The analytics velocity is always high, and that’s largely because of Core Value’s effort. One of the most impressive things about them is their ability to understand our business, and not just the technical implementation. They understand why and how we’re doing things.”

Mark Merhom Data & Analytics Engineer

Challenge 

Insurance companies spend several days to weeks carefully assessing a claim, however, the insurance business is still affected by fraud. The most common issues are property damage, car insurance scams, renters, and pet issues. Carefully selected models may help to successfully detect fraud. Our client aimed at automatic identification of fraud activities and preventing fraud-related losses and damages before claims are paid, and before fraud activities could do any harm to their system. The client also wanted to decrease the time spent for claim consideration and payments to genuine customers.

Solution 

Process 

Data Collection and Preparation

The initial phase focused on gathering relevant data points from Trōv’s systems, including customer information, claim histories, and transaction data.

We established data pipelines to consolidate information from multiple sources, performed data cleaning to address inconsistencies, and created a unified dataset suitable for analysis.

Model Development and Training

In this phase, we built and implemented various analytical models using unsupervised clustering, feature engineering, and neural networks. We first applied DBSCAN to identify patterns without labeled data, then created custom features to enhance detection capabilities.

As labeled data became available, we incorporated supervised machine learning techniques to improve accuracy. Regular evaluation and refinement cycles ensured optimal model performance.

Integration and Deployment

The final stage involved integrating the fraud detection system into Trōv’s existing claims processing workflow. We developed user-friendly dashboards for fraud analysts, implemented real-time scoring mechanisms, and established alerts for suspicious activities.

Comprehensive testing and training ensured smooth adoption by the client’s team, while continuous monitoring systems were put in place to maintain and improve performance over time.

Results 

Insurance fraud detection software helped Trōv minimize fraud risks, improve underwriting, and enhance risk management.

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The client benefited from:

• Automatic AI insurance fraud detection
• Improved accuracy of the AI fraud detection insurance, false positives reduction, and the minimization of human errors
• Costs optimization of the processes and resource usage
• Lowered loss ratio, fraud or break identification before any harm is done
• Accelerated payments on genuine claim
• Improved customer experience