Fintech leading in AI adoption, need new AI legislation: Moody’s Analytics
Fintech is the leading sector for artificial intelligence (AI) adoption and readiness in risk and compliance, whereas sectors such as insurance, asset and wealth management have been slower to adopt these, according to a report by Moody’s Analytics.
As per the study, 18 per cent of fintech respondents are currently actively using AI, double than the 9 per cent respondents across all surveyed sectors. The banking sector was second in line with 12 per cent respondents actively employing AI.
On the other hand, only 3 per cent insurance, asset management, and wealth management players are actively using AI currently but another 11 per cent are piloting it, the report titled ‘Navigating the AI landscape – Insights from Compliance and Risk Management Leaders’. The study surveyed over 550 senior compliance and risk management professionals from 67 countries to assess their perspectives on and uses of AI.
“While fintechs are demonstrating higher rates of usage and piloting of AI for risk management and compliance processes, the banking sector is not far behind,” said Keith Berry, General Manager, KYC Solutions at Moody’s Analytics, adding that there is appetite across sectors to leverage AI technologies for productivity gains, particularly targeting data screening and augmenting staff performance.
“The compliance professionals who joined our research clearly expect AI to be transformative within their profession, with the fintech sector presently at the vanguard of this change,” he said. Overall, 30 per cent respondents are actively using or conducting trials with AI, while 49 per cent are considering adopting the technology.
Nine of the ten early adopters of AI reported seeing a positive impact on risk and compliance among other benefits. Outside of the early adopters too, there is agreement that AI technologies, including GenAI, will deliver advantages for risk and compliance given that two-third respondents said their data is fragmented or containing inconsistencies.
Challenges
The poor quality of internal data could be a barrier to AI implementation “if firms can’t get a firmer handle on it”, the report said adding that there is a stark gap between the lack of awareness of AI-related regulation and consensus that new legislation is needed. However, poor internal data quality, lack of clarity around regulation, and a gap in specialist knowledge gap continue to be obstacles.
The study found that while 79 per cent of professionals feel new legislation to regulate the use of AI in compliance is important, the majority lack awareness of existing AI-related regulations.
“With many of the professionals expecting the widespread adoption of AI in the next one to five years, steps need to be taken for it to meet its transformative potential across risk management and compliance,” Berry said adding that the overall outlook for AI is strong if compliance teams acquire the right expertise and data.
Among other use cases of AI application, 63 per cent was data analysis and interpretation, 53 per cent for risk management and 51 per cent for fraud detection.