Thinking Beyond Chatbots in Banking: What’s Really Possible With AI?

Data Basics Rose Wijnberg

One of the most well-known examples of artificial intelligence, chatbots in banking and across other industries tend to garner lots of attention. This is partially because they are so visible to end customers. Who hasn’t interacted with banking chatbots in a customer service capacity? This visibility is only growing in 2023 with the revived interest in conversational AI thanks to ChatGPT. But when you look under the hood, there is so much more to AI in banking than meets the eye.

AI & Us, the new web series from Dataiku, looks at how AI is changing our everyday lives. Next up? You guessed it — banking, one of the largest industries in the world impacting the lives of nearly everyone on the planet. 

Watch the latest episode below, featuring experts from Standard Chartered Bank and Dataiku, which digs deeper into the banking industry and asks fundamental questions about what is — and what could be — possible with AI in the coming years. For example: Could AI predict, or even prevent, the next banking or financial crisis?


Bots in Banking: Fantasy, or the Future?

In some ways, banks are way ahead when it comes to AI. For example, data has always been the foundation of the industry, and even the use of AI technology in banking dates all the way back to the 1980s. Yet at the same time, many banks struggle amidst the complexities and volume of today’s data, regulations, and more. Chatbots in banking aside, when it comes down to it, we’re a long way off from AI replacing humans in any meaningful way in this industry.

“What we’re seeing is very, very specific scorable tasks often being augmented very effectively. What we are not seeing is the removal of a person entirely from a fundamental position.”

— John McCambridge, Global Solutions Manager at Dataiku

Where AI Does Play a Role

Today, there are three primary areas where augmentation by AI is becoming a reality for banks and countless use cases for advanced analytics, data science, and machine learning techniques in each:

AI in Banking for Customer Management

Leveraging AI for customer management allows financial institutions to innovate and provide a modern, customer-focused experience. Examples include leveraging machine learning for deeper and more meaningful customer segmentation, turning a vast sea of customer reviews into actionable insight for the business, more robust analysis of distribution networks, and more. 

AI in Banking for Risk Management

Risk management, which is largely focused on bringing the benefits of machine learning technology plus the processes of MLOps to existing systems. This includes anti-money laundering models that are explainable and easier to monitor and update, credit card fraud models that leverage machine learning alongside business rules, and enhanced credit scoring.

AI in Banking for Operational Efficiency

Operational efficiency and resilience, which is an especially important area of development during strained economic periods with increased cost pressures. Examples of advanced analytics and AI techniques here include process mining and P&L impact modeling, to name a few.

The bottom line? In a market where customers have increasingly more choices to meet their financial needs, chatbots in banking aren’t the only way AI is playing a role. While many of these use cases aren’t things that we outside of the banking space see day to day in our interaction with our financial institutions, as with many industries, AI is being used ever more extensively in ways that nonetheless impact our lives.

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