Risk management software provider Provenir has stated that AI algorithms are being overwhelmingly sought by European companies to combat fraud in the financial sector.

Research released by Provenir explored this year’s in-house investment plans of a 100 fintech and financial service firms across Europe.

Findings show that decision makers are mainly influenced by fraud prevention (91%) in their choice to go ahead and greenlight the funding of AI-enabled risk decisioning.

Carol Hamilton, SVP, Global Solutions at Provenir, said: “The risk of fraud has heightened across the entire financial services landscape, and with attacks only becoming more sophisticated and widespread, it is positive to see that more firms are turning to AI-enabled technologies to minimise these threats.

“The key benefit of using AI-enabled decisioning for fraud detection is its ability to get smarter with each decision it processes. So, as fraudsters evolve their methods, AI models can use real-time data to identify new patterns, learn, and adapt to constantly detect fraud threats and minimise risk.”

The use of alternative data is also considered to be highly effective against malicious actors, with 68% of those surveyed having favourable opinions of alternative data incorporation as a fraud detection measure.

Relevantly, access to data is shown to be the biggest challenge for organisations (88%) when it comes to the task of putting together a risk strategy, followed closely by the lack of a centralised view on data across the customer lifecycle (74%).

Therefore, AI-powered algorithms are likely to receive financial backing due to most businesses wanting improvements in automated decisions across the credit lifecycle (68%), competitive pricing (65%) and cost savings and operational efficiency (61%).

None of the survey participants expressed full confidence in their risk model being completely accurate. According to Provenir, the overall trust in current credit model accuracy is low, with only 22% reporting good risk model accuracy for most of the time.