Curate and compile: the future of AI-driven fintech experiences
Today’s digital banking experiences are not a million miles away from their analogue predecessors.
Even for the most cutting-edge fintech proposition, an individual has to go through the time-honoured process of:
- Identifying a company to do business with.
- Engaging with that company and applying for an account.
- Proving they are who they say they are.
- Funding the account.
- Using the available channels to manage the account.
- And maybe being cross-sold and upsold to.
The sector has become an enthusiastic adopter of algorithms, with processes streamlined and increasingly straight-through as machines have taken over from people.
Gradually, these algorithms have become essential components of how institutions measure risk, communicate with customers and offer products – but it is very much AI 1.0.
Overall though, banking remains stubbornly unchanged.
But I am convinced that generative AI will have a profound impact. It will completely radicalise the industry, ushering in new ways of doing business and creating new products, services and experiences. AI will be at the heart of the fintechs of the future.
But what might that look like?
I started by asking the latter-day Oracle (of Delphi), ChatGPT-4, to peer into its crystal ball and tell me how generative AI could change the banking industry.
Its response is what you might expect and provides a laundry list of some of the basics:
- Personalised financial advice: Analyse a customer’s transaction history, income and other financial data to provide customised recommendations on managing their money, saving for retirement or investing in the stock market.
- Automated loan approvals: Quickly analyse a customer’s credit history and financial data to approve or deny loan applications in real time without needing a human underwriter.
- Chatbots for customer service: AI-powered chatbots could handle routine customer service inquiries freeing human customer service representatives to address more complex issues and improve response times.
- Fraud detection and prevention: AI could use predictive analytics to identify and prevent fraud in real time. This could include analysing transaction patterns, detecting anomalies and flagging suspicious activity.
- Seamless mobile banking: AI could make mobile banking faster and easier by providing predictive search, voice-activated commands and a personalised dashboard that shows relevant information such as account balances, transaction history and spending patterns.
- Investment advice: AI algorithms could use machine learning to analyse stock market data, news articles and other relevant data to provide investment advice to customers.
So much for the crystal ball. None of this feels revolutionary or transformational. In fact, the reality is that products already exist or are coming online that are doing much of what it suggested.
For example, Personetics already employs AI to process data and deliver “hyper-personalised” insights to customers via its Engage product. By analysing user-specific data streams, Engage generates a tailored list of actionable insights and uses a proprietary learning algorithm to determine the optimal prioritisation for each user. Over time, Engage refines its recommendations by learning from individual customer interactions and incorporating explicit user feedback, such as ratings and likes, into its algorithm.
Many banks are already using algorithms to determine suitability for loans, and we’re certainly seeing more chatbots that handle at least the triage aspect of dealing with customer problems.
So, what do I think?
With new technologies, cloud computing and an increasing need for individuals to control their own data, generative AI’s capabilities will help usher in a model of finance that is much more geared around the individual: the curate and compile model.
An AI will soon be able to recommend the best provider for a product based on an individual and their needs (be that a person or company), even before they know that they need it. Using available data, AI will then decide on suitability and determine to its satisfaction that someone is who they claim to be, without any involvement from the individual, before building a product and services suite tailored to them. This could include pricing and terms. Some new core banking platforms, such as Thought Machine, already offer personalised ledgers. Connecting this technology with AI-driven risk and suitability models means a product set could be tailored to an individual precisely.
Taking this a step further, AI could compile a bank around an individual. So instead of me going to HSBC’s app, I could go to Dave’s Big Bank app, which curates products and services from a range of providers and assembles the experience just for me – a true best-of-breed offering.
The way my bank is branded, designed and functions could also be generated by AI.
Presently, designing a bank’s digital experience is a manual process that takes an age, involving multiple people across various activities. In theory, you will soon be able to ask an AI to render the best experience based on parameters in a chosen skin. Combine this with the best-of-breed approach mentioned above, and suddenly, banking has changed.
In this scenario, a bank’s brand would become extraneous, and its product set atomised, ultimately calling into question its role as a direct-to-end-user proposition. Banks as manufacturers… Now there’s a cost-saving idea!
The next few years will see some new and exciting propositions emerge. A new generation of generative fintechs is coming, and finally, the banking sector will be truly transformed.
About the author
Dave Wallace is a user experience and marketing professional who has spent the last 25 years helping financial services companies design, launch and evolve digital customer experiences.
He is a passionate customer advocate and champion and a successful entrepreneur.
Follow him on Twitter at @davejvwallace and connect with him on LinkedIn.