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Beat Residual Credit Fraud with Expert Machine Learning

Residual credit fraud may represent “only” 10% of all fraud attempts, but its ongoing presence is a huge thorn in the side of credit institutions, not only in terms of revenue lost, but also when it comes to their reputation. Tackling this small 10% via more “traditional” methods has proven to be a frustrating uphill battle for most companies, so more and more are turning to AI and machine learning as an alternative. Use of these technologies as fraud-fighting tools is expected to triple over the next two years. Why such enthusiasm?

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By François de Pimodan

Yes, you can fight credit fraud and still boost profits

For many institutions that offer credit (traditional banks, neo-banks, auto loan lenders, consumer credit companies, etc.), outmanoeuvring the increasingly sophisticated traps laid by internet scam artists is too often synonymous with staggering costs—in time, human resources and money. Yet some companies out there are managing to fight fraud effectively, while increasing their profits. Their secret? Innovation and—more specifically—machine learning.

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By François SAULNIER

Takeaways on AI and EU regulation challenges

Takeaways on new challenges and transformations on AI at EU. François’ notes his perspective on key points for the following year.

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By Francois Saulnier

Loan fraud: the true cost of a DIY AI project

Building your own AI anti fraud project is a tradeoff between ownership, long-term strategy, as well as immediate ROI and efficiency

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By Bleckwen

Equals Group choose Bleckwen to power their compliance solutions

The Equals Group has chosen the highly modular and extensible Bleckwen Fraud and Fincrime platform to deal with the ever-increasing complexity and volume of payments being processed across the Group.

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