AI in banking is maturing, bringing the potential for higher-complexity solutions that generate positive ROI across business segments. Adoption of AI solutions in banking has become more mainstream: A majority of financial services companies say they’ve implemented the technology in business domains like risk management (56%) and revenue generation through new products and processes (52%), per the Cambridge Centre for Alternative Finance and the World Economic Forum. As AI gains popularity in banking, financial institutions (FIs) are building on their existing solutions to solve increasingly complex challenges.
Most banks (80%) are highly aware of the potential benefits presented by AI and machine learing, per an OpenText survey of financial services professionals. In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they’re currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Insider Intelligence. Certain AI use cases have already gained prominence across banks’ operations, with chatbots in the front office and anti-payments fraud in the middle office the most mature.
Banks can use AI to transform the customer experience by enabling frictionless, 24/7 customer service interactions — but AI in banking applications isn’t just limited to retail banking services. The back and middle offices of investmentbanking and all other financial services for that matter could also benefit from AI.
Applications of AI in Banking
The three main channels where banks can use artificial intelligence to save on costs are front office (conversational banking), middle office (fraud detection and risk management) and back office (underwriting).
In this report, Business Insider Intelligence identifies the most meaningful AI and machine learning applications across banks’ front and middle offices. We also discuss the winning AI strategies used by fintechs and legacy financial institutions so far, as well as provide recommendations for how banks can best approach an AI-enabled digital transformation.
The companies mentioned in this report are: Capital One, Citi, HSBC, JPMorgan Chase, Personetics, Quantexa, and U.S. Bank
Here are some of the key takeaways from the report:
- Front- and middle-office AI applications offer the greatest cost savings opportunity across digital banking.
- Banks are leveraging algorithsm on the front end to smooth customer identification and authentication, mimic live employees through chatbots and voice assistants, deepen customer relationships, and provide personalized insights and recommendations.
- AI is also being implemented by banks within middle-office functions to assess risks, detect and prevent payments fraud, improve processes for anti-money laundering (AML) and perform know-your-customer (KYC) regulatory checks.
- The winning strategies employed by banks that are undergoing an AI-enabled transformation reveal how to best capture the opportunity. These strategies highlight the need for a holistic AI strategy that extends across banks’ business lines, usable data, partnerships with external partners, and qualified employees.
Analysis: This article notes that AI is being used in banking to make processes in the front office, middle office, and back office easier and cheaper. It talks about how certain banks are using AI to mimic employee – customer interactions and create more personable banking experiences. However, I find it valuable to take a step back from this article and think about what would be more meaningful to the customer, an artificial experience, or real people with real empathy helping them to manage their money. People take great pride in their money, it is how we survive, so I question if it is truly ethical to set a customer up with a bot when they need help. On the other side of this argument is the concept of efficiency and affordability. There are many instances where AI can get the job done faster than humans and our inevitable human error. Overall, I believe AI is something we need to be looking at through a close critical lens if it is something we want to be implementing into banking experience design.
Citation:
Digalaki, E. (n.d.). The impact of artificial intelligence in the Banking Sector & How Ai is being
used in 2022. Business Insider. https://www.businessinsider.com/ai-in-banking-report
