By Arnab Mitra, Programme Manager, BIAN
Consumer-facing generative AI products such as ChatGPT and DALL-E have rapidly transformed industries and begun to show their transformative potential little more than two years on. In fact, according to MCKINSEY GLOBAL INSTITUTE, generative AI is expected to bring a value-added of around $200 – $340 billion to the banking industry every year.
AI will be one of the largest catalysts for technological change that we may ever see. Therefore, it is extremely important to us at BIAN that we explore how generative AI can support our organization and benefit our members as they undergo transformations for the future and keep up with evolving customer service needs.
Redefining value from the basics
Perhaps the most marked difference between AI and computers is how they acquire knowledge. Since the advent of the thinking machine, humans have been writing billions of lines of code to teach computers to operate within the financial services industry.
The issue with this approach, however, is that the human must be able to instruct the computer to cover all possible scenarios that the system might encounter and the actions it should take. For example, if the balance of an account is less than zero, the bank’s system must notify the account owner to add funds, or they may have to pay interest.
With AI, on the other hand, the data professionals can ‘train’ the models using historical data. This ‘know-how’ can then be used by the AI engine to make decisions in the future, deciphering the proximity between two things in various dimensions by using the information it has been trained with to find similarities in a given situation. This means it can make decisions and choose the appropriate responses for many more scenarios.
To put this into perspective, if you asked a tool like ChatGPT to find companies similar to Coca-Cola, it would first identify obvious choices of beverage conglomerates like PepsiCo and Dr. Pepper Snapple Group. It would then go on to provide information on less obvious answers like Unilever and Molson Coors. While these companies aren’t directly related to non-alcoholic beverage companies, they still cater to the consumer market with similar products.
Another exciting aspect of using AI is that it can be trained on huge amounts of data in a very short period of time, compared to humans. An AI engine, for instance, can learn in a day, what a human can probably learn in 20 years. So, this is a very powerful tool that can be trained quickly to perform complex tasks across our industry.
So, what does this look like for BIAN?
At BIAN, we’re currently exploring how we can get the most out of the technology. Some of these areas include:
- The creation of a BIAN consultant: The use of AI can ensure our members have any information they need at any minute of the day. We hope that AI will eventually provide us with the ability to answer questions and eventually design solutions related to BIAN’s content. This is still very much a work in progress, and still requires human consultants to double-check the recommendations made by the AI engine.
- Certification: There are several tech companies, banking software vendors, and indeed banks who aim to be BIAN compliant, and want to take advantage of the benefits that a BIAN-compliant software and services landscape brings for them. Most software, however, is currently only aligned to BIAN on a very basic level. AI would help us find differences much more easily between proprietary banking software and the BIAN standard. It would also make to the journey to compliance (for these banking software solutions) much smoother.
- Using AI to solve common industry problems: We’re also starting to see AI being woven through our working groups. As part of the Coreless Banking initiative, for example, our members are trying to create solutions, where, if a customer shows signs of leaving their bank, they could be offered better rates for example on savings or fixed-term products. Other interesting use cases could involve looking at customer spending and income patterns and partnering with non-financial institutions to offer non-financial products, like health club memberships or even holidays.
These areas only scratch the surface of what is possible through the use of AI, and I am incredibly excited to be exploring these capabilities alongside BIAN’s members.
Interested to know more? Keep your eyes peeled over the next few months as I share more information on our efforts.
