Trōv: AI-Powered Fraud Detection

Trōv:
AI-Powered
Fraud Detection

Get in touch

Client

Trōv

Industries

Insurance

Services

Solution engineering

Technologies

Python, NLP, DBSCAN

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 embed insurance products within other digital experiences to 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

Clustering

Avenga's team leveraged an unsupervised clustering approach to identify suspicious groups of users. We gathered all the vital metrics that signal potentially suspicious clients, including ID checks, a PEP/Sanctions/Deceased Warning, Forwarding address link warning, Email/Mobile Risk, credit card scores, etc.

Feature engineering

Feature engineering was applied which leveraged the domain knowledge in order to manually create new important features that could affect the fraudster’s identification.

Supervised Machine Learning

At the later development stages, having collected the labeled dataset, our expert teams applied a supervised machine learning approach to make fraud-related predictions that helped to boost the accuracy of the fraud detection system.

Neural networks

Neural networks were used that can learn suspicious patterns from samples and later detect them in new unseen cases.

Results

Fraud detection analysis helped to minimize fraud, improve underwriting and enhance risk management.

The client benefited from:

automatic real-time fraud detection;
improved accuracy of fraud detection, 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 claims;
improved customer experience.