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Early adopters of financial services AI report mixed returns

While customer onboarding and risk detection are the primary uses of AI cited by 29% of respondents, data management and customer insights are the most common use cases.

“As organizations become increasingly digital, data is becoming more important,” said Vishal Maria, CEO and founder of Quantexa (which conducted the survey). “However, these huge waves of data often lead to decision gaps that plague organizations, leaving them unable to extract meaningful value. AI and technological advances such as entity resolution can strategize and measure this data decision gap. Ways are helping to close, allowing organizations to combine muted data to create a meaningful connected view that directly leads to higher accuracy, productivity, and ultimately, reliable decision making.”

AI adoption challenges

Data preparation, integrating internal/external data sources (15%), deployment of AI (14%) and availability of skills (14%) were reported as the biggest challenges in the adoption of AI in financial services firms goes.

“The barriers to success for AI projects can be tough,” says Ian Gilmour, incoming chairman of the AI ​​Forum Advisory Board. “An important finding from the respondents’ feedback is that an organization needs to work with skilled third-parties to increase its internal AI expertise and increase its chances of success. The fact is that such a high percentage of firms are only in testing And the learning phase tells a lot about the gap between expectations and potential. This report provides essential inputs for business strategy, helping budget committees to identify and resource AI projects that provide a competitive advantage Is.”

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