Fraud prevention is the biggest driver for investments in AI

Provenir has found in its newest study that more than 90% of European fintechs will adopt AI-enabled risk decisioning to combat fraud

Provenir, a global leader in AI-powered risk decisioning software for the fintech industry, has found in its latest study that fraud prevention is the biggest driver for investments in AI-enabled risk decisions this year.

The survey, which offers 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%).

Heightened risk of fraud across the fintech landscape 

The survey highlighted 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.

It 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%).

“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.”

Using AI-enabled decisions for fraud detection

Digital transformation intensifies data issues. Data silos and data overload can lead to an incomplete view of risk exposures, preventing the visibility of patterns and behaviours needed for prediction.

Fast transaction times and ever-evolving fraudster schemes make it increasingly difficult to immediately identify, predict, counteract and recover.

Using AI to detect fraud has aided businesses in improving internal security and simplifying corporate operations. AI has therefore emerged as a significant tool for avoiding financial crimes due to its increased efficiency. It can be used to analyse huge numbers of transactions in order to uncover fraud trends, which can subsequently be used to detect fraud in real-time. 

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

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