Munich Re logo
Not if, but how

Explore Munich Re Group

Get to know our Group companies, branches and subsidiaries worldwide.

Artificial intelligence improves security of e-commerce
Online sales currently generate around €2 trillion in revenue worldwide, and are growing at a rate of roughly 25% per year. But this is a market that involves costly risks for online sellers. One such risk is credit card fraud.
Fingerprint and printed circuit board
© KTSDESIGN / Science Photo Library / Getty Images
    alt txt

    properties.trackTitle

    properties.trackSubtitle

    Munich Re and the start-up Fraugster have joined forces to offer a solution that reduces this risk, lowering online sellers’ costs and increasing their profits.

    Credit card fraud costs online traders around €25bn a year. In order to avoid payment defaults, traders, payment services and credit card companies currently check payments by hand or by means of unwieldy rule-based solutions, which is a laborious, expensive and ineffective process. 

    The German-Israeli start-up Fraugster has developed a self-learning algorithm that checks in milliseconds whether online payment transactions are legitimate. This service allows payment companies to check transactions faster and more reliably, offering them, their clients and online sellers a new level of security for their business. Not only does the algorithm detect and, with a high level of probability, prevent fraudulent transactions in the first place, it also lowers the number of valid payments that are incorrectly rejected. Online sellers can thus reduce losses while at the same time increasing revenues from legitimate transactions. With several million transactions a day passing through the system, the AI technology ensures that the algorithm is continually honing itself and adapting to changes. Fraugster is liable for the costs clients incur if the system incorrectly deems a fraudulent transaction to be legitimate. 

    Munich Re has analysed and tested Fraugster’s algorithm and found the quality of transaction checks and IT infrastructure to be fit for purpose and reliable. In doing so, Munich Re made a detailed analysis of the statistical models used, the self-learning principle of the algorithm, the predictive quality of the algorithm with data it has not yet encountered, the quality of Fraugster’s data scientists, the company’s desire for further development and the quality of its IT infrastructure. On the basis of this analysis, Great Lakes Insurance SE, a Munich Re subsidiary, insured Fraugster should the software return any incorrect results and fail to recognise fraudulent transactions. This means Fraugster offers its clients additional financial security and gives the service the seal of approval that Munich Re trusts the AI predictions of its product.

    The cooperation between Munich Re and Fraugster is creating macroeconomic value: the innovative type of risk appraisal and tailored insurance solution combine to offer greater security, reducing sellers’ costs and increasing their revenue and efficiency. At the same time, Fraugster’s software also lowers the number of valid payments that are incorrectly rejected, thus breaking down barriers for those wishing to trade online. 

    Experts
    Michael Berger
    Michael Berger
    Business Development Manager
    Special Enterprise Risks
    André Knoerchen
    Andre Knoerchen
    Head of New Tech Underwriting
    Munich

    Newsletter

    Stay ahead of the curve with exclusive insights and industry updates! Subscribe to our Munich Re Insights Newsletter for a front-row seat to the latest trends in risk management, expert analyses and assessments, market insights, and innovations in the insurance industry. Join our community of forward-thinkers at Munich Re and empower your journey towards a more resilient future.