The survey, which canvassed the views of 100 decision-makers from fintechs and financial services firms across Europe, found that other major drivers for investments in AI-enabled risk decisioning include automating decisions across the credit lifecycle (68%), competitive pricing (65%) and cost savings and operational efficiency (61%).

The survey also highlights the role that alternative data can play in the fight against fraud, with 68% of those surveyed choosing to incorporate alternative data for the purpose of improving fraud detection.

The report also found that access to data is the biggest challenge to an organisation’s risk strategy (88%), closely followed by a lack of a centralised view of data across the customer lifecycle (74%).

Overall, the findings show that current confidence in credit model accuracy is low, with only 22% of respondents believing that their organization’s current risk model is accurate at least most of the time. No respondents believed that their organisation’s risk model is completely accurate.

“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,” said Carol Hamilton, SVP, Global Solutions at Provenir. “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.”