Malverde launches to combat scams and broader financial crime

New London-based AI fintech consultancy opens up after securing seed investment

Douglas Blakey June 03 2024

Scams alone account for 39% of all UK consumer fraud losses in financial services, £459.7m per year. Malverde describes itself as experts in the technology required to detect and prevent fraud and financial crime. This includes using AI and machine learning models to reduce scams and money laundering.

£35bn: UK financial institutions spend on financial crime compliance

Financial institutions in the UK spend over £35bn per year on financial crime compliance. Malverde are firm believers that a substantial proportion of this is waste. They argue that the industry spends far too much effort on attempting to demonstrate compliance to the regulators, rather than on understanding how fraud and financial crime is carried out. By focusing on the latter, they contend, the financial sector would be much more effective and efficient at reducing financial crime and still achieve compliance as a by-product.

Malverde says that its founding team, Richard Elliot-Cooke, Simon McMahon and Spaden Elmhirst, have an exceptional track record of helping financial institutions, including fintechs and global banks, to detect more cases of fraud and organised crime whilst reducing the operational cost of doing so.

Elliot-Cooke, co-founder said: “The crux of the problem is that the software vendors and the consultancies who advise the banks are too far removed from the people who understand the risks. [That is] the people who work on criminal cases on a day-to-day basis. This means that the technology in place, and the advice provided to financial institutions, is too generic. It leads to very sub-optimal financial crime detection programmes.

“The only way to mitigate fraud and financial crime effectively is to get into the nitty-gritty of the cases, understand how it is carried out, and train your detection models to tackle it head on. Only then will the right cases be identified and without creating excessive waste. A significant amount of the remaining manual processes can then also be automated using generative AI”.

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