With more than 70 billion real-time payment transactions processed globally in 2020, financial institutions need robust systems to prevent fraud and reduce costs.
Artificial intelligence (AI)-enabled applications power banks, insurers, asset managers, and FinTechs to deliver improved services and outperform competitors, increase customer lifetime value, and increase market share.
These AI-enabled applications were detailed in NVIDIA’s “State of AI in Financial Services” report, based on responses from over 500 C-suite executives, developers, data scientists, engineers and IT teams working in financial services.
The top two of the top three priorities across the industry remain fraud detection and algorithmic trading, while conversational AI is a new entrant into the top three.
More importantly, the percentage of companies investing in each use case jumped significantly year over year (YoY), with underwriting and acquisition, conversational AI, and anti-money-laundering (AML) and know-your-customer (KYC) fraud detection showing the largest percentage gains.
Meanwhile, in the latest survey, AI-enabled applications for fraud detection, know-your-customer, and anti-money laundering experienced at least 300% growth.
“Interestingly, nine of 13 use cases tracked in our study are utilised by at least 15% of respondents’ companies. In contrast, none of the use cases had more than 14% industry penetration in last year’s survey. This demonstrates the rapid adoption of AI across financial services, which requires banks to invest in enterprise AI strategies and infrastructure.”
AI for underwriting increased fourfold, from 3% penetration in 2021 to 12% this year. Conversational AI jumped from 8 to 28% year-over-year, a 3.5x rise. There was a dramatic increase in the percentage of financial institutions investing in AI use cases year-over-year.
However, there are challenges in achieving a company’s AI goals.
Only 16% of survey respondents agreed that their company is spending the right amount of money on AI, and 37% believed “lack of budget” is the primary challenge in achieving their AI goals. Additional obstacles included too few data scientists, lack of data, and explainability, with a third of respondents listing each option.
Over half of C-suite respondents agreed that AI is important to their company’s future success. The top total responses to the question “How does your company plan to invest in AI technologies in the future?” were:
- Hiring more AI experts (43%)
- Identifying additional AI use cases (36%)
- Engaging third-party partners to accelerate AI adoption (36%)
- Spending more on infrastructure (36%)
- Providing AI training to staff (32%)
However, only 23% overall of those surveyed believed their company has the capability and knowledge to move an AI project from research to production. This indicates the need for an end-to-end platform to develop, deploy and manage AI in enterprise applications.
Today’s market demands that companies deploy AI-enabled applications at scale.
“To build an AI-powered bank, leadership must invest in the enterprise AI infrastructure that enables data scientists, product managers and IT leaders to enact leadership’s AI strategy.”
“Successfully deploying an AI strategy will help these financial services firms to achieve higher revenues, lower operating costs, greater customer satisfaction, and an overall competitive edge in the industry.”
