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AI set to transform compliance; data, knowledge barriers remain, Moody’s research reveals

By Puja Sharma

November 01, 2023

  • AI
  • AI Algorithms
  • Big Data
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AI, A new Moody’s Analytics study reveals that while there is an overwhelming desire for AI adoption in compliance, significant barriers hinder progress including poor internal data quality, a lack of clarity around regulation, and a specialist knowledge gap.

The study, which included a survey of more than 550 senior compliance and risk management professionals from 67 countries, assessed their perspectives on and uses of AI. It shows that the top three areas where AI is being applied are data analysis and interpretation (63%), risk management (53%), and fraud detection (51%).

Despite these issues, early adopters are already realizing the benefits of AI. Nine out of 10 early AI adopters report it is having a positive impact on risk and compliance. Almost 70% of respondents believe AI will have a transformative or major impact on their work. Nearly a third (30%) of those surveyed are actively using or trialling AI, while 49% are considering adopting the technology.

Keith Berry, General Manager Know Your Customer Solutions at Moody’s Analytics, said, “Compliance professionals are convinced that AI will be transformative for their industry, but obstacles remain that could hinder risk management and compliance functions from capitalizing on its potential. The benefits of AI are currently viewed in easy-to-measure quantitative terms. Process efficiencies are a good start to AI adoption, but they are only scratching the surface of the technology’s capabilities. Advanced data analytics, accurate predictions, and the scalability of data are all features compliance teams will not want to miss out on.

“With many of the professionals we spoke to 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. For example, when based on high quality data, AI is able to drastically reduce the number of false positives in a KYC screening process at scale, and can result in up to 80% of level one investigation and triage happening instantly and accurately. The overall outlook for AI is strong if compliance teams acquire the right expertise and data to fully capitalize on the opportunity.”

Looking ahead, 83% of all surveyed respondents expect widespread adoption of AI in risk and compliance in the next one to five years. However, the study found that challenges remain, including:

  • Internal data quality: Only 14% of those surveyed rated their own data as high quality. Resolving data issues is critical to reducing Large Language Models (LLM) hallucinations and improving the accuracy of AI outputs. Early adopters of different types of AI are more likely to rate their data quality as high (36%), compared to 9% who are not considering the use of AI. There is a clear data maturity gap, with 75% who are not contemplating the adoption of AI considering their data quality to be poor.
  • Regulation: The study found 79% of professionals feel that new legislation to regulate the use of AI in compliance is important, while 66% seek greater clarity around any existing AI-related regulations in risk and compliance.
  • LLM conservatism: Despite the rapid growth of LLMs, caution remains in risk and compliance. Only 28% take a positive stance on these models, 25% are actively discouraging or prohibiting their use and 46% have yet to adopt an LLM policy. Just 41% associate LLM terminology with risk and compliance.
  • Use case understanding: Only a quarter (26%) rated their overall understanding of AI’s relevance to risk management and compliance as high. Compliance professionals are most likely to identify improved efficiency in processes (72%), increased speed of data processing and analysis (72%), and cost savings due to automation or improved decision-making (66%). Fewer currently recognize the potential for more advanced, transformative benefits, such as improved accuracy of results and predictions (51%) and the reduction of false positives (49%).

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