Applying artificial intelligence to everything we’re comfortable doing in banking is much easier than changing how we do things — which would make the greatest use of AI.
Few in financial services would argue that the future belongs to those institutions that harness data-driven machine intelligence to do more, better and faster. The insights and efficiencies needed to compete and thrive will come from AI-driven service personalization and optimization.
But AI should do more than speed up a financial assembly line. As Ernst & Young stated in a report: “AI-driven financial health systems will become personal financial operating systems. Consumer finance will unbundle products and rebundle personalized and holistic value propositions based on life events.”
While that is a worthy goal, the retail banking industry will not come any closer to achieving that if it continues the way it is thinking about and implementing AI.
AI Should Be Doing More for Financial Institutions
I call the current mindset for applying AI to financial services “the Product Gun.” It’s the familiar banking model of manufacturing a product, targeting a market segment for distribution, ensuring everything complies, and then shooting it to potential consumers. It’s worked well for many years, but it’s had its day.
Hopes of providing consumers with “personal financial operating systems” and solutions tailored to life events won’t happen merely by blending AI with the same old thing. In fact, applying complexity and leverage to well-understood financial products and processes may produce unintended consequences.
But rethinking from the ground up can be rare. Models and the basic process behind them often don’t change because business typically likes to save energy. Take the Boeing 737. The jet’s design dates back to 1964. The first one flew in 1967. The latest iteration still flies today. This makes a perfect example of leveraging an old business model to sustain profits — as the saying goes, if it ain’t broke…
Because banking is a regulated industry that deals with heaps of money and risk, a control structure has evolved to organize competencies and lines of business. Risk and profit are put in little boxes for success. Boxes like “manufacture,” “target,” and “comply” all have executives, KPIs, spreadsheets and politics. On the whole, it has worked well.
The problem is, innovative tools like AI get shoved into the same old boxes. Instead of using this technology to reimagine traditional processes, we use AI to build a supercharged 737.
This has some benefits to financial institutions’ business lines. This could include improving the consumer credit process, reducing compliance exceptions or automating support desks. Each of these, and similar applications of AI, could benefit the industry and those it serves.
However, AI can produce missteps, such as unwittingly biased outcomes. At best staying trapped inside old processes with new AI insides will do no harm, but it’s still not going to bring us closer to a vision for personalized, holistic financial advice.
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Breaking the Box with AI-Powered Thinking
I want to propose something radical: Financial institutions should be optimizing for their clients’ needs. This sounds extremely simple — but the commitment required, and the roadmap to making this reality is serious, expensive and difficult when it can take a long time to deliver on expectations.
Jeff Bezos said: “Put the customer first. Invent. And be patient.” It took Amazon 20 years to be profitable, and during that time Bezos kept investing to optimize his understanding of and delivery for his customers. Amazon’s impressive margins came about relatively recently, and only after a long battle.
The alternative to the traditional “Product Gun” attitude is something I call “Mother Mind.” This goes beyond simply shooting products at people. It gathers intelligence about what and who people are and what they need. It understands deeply what customers are going through in their lives, then it guides them with strategies that are actually going to be useful in the context of their lives. Used in this way, AI can keep guiding an institution in ways to better serve people and businesses.
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Three Steps Forward, No Steps Back
Change is hard for financial institutions, but it can happen. Here are three achievable pivots that can help put a bank or credit union on the path to success.
• Gather: Move from system-centric data to human-centric data. Even under the progressive framework of Europe’s PSD2 open banking framework, financial institutions still store and access data in a system-centric way: transactions, products, accounts and balances. Data organized this way makes it very difficult to understand much about people’s individual circumstances.
Today data comes in the language of systems and ledgers. To do anything radically different requires shifting to the language of human lives. This means building interfaces to data that will allow financial institutions to ask questions about people’s behavior and needs. What are their financial personalities? What events in their lives offer the chance to be of assistance?
• Understand: Move from products to journeys. The word “customer-centric” means nothing if products continue to be banking’s foundation. “How do we distribute the product for less? How do we recommend products to customers at the right time?” — such thinking is inverse, today.
Consumers’ needs change as their lives and circumstances change. At any point and time they have problems that need solutions and questions that need answers. Whether or not these journeys are successful is going to start meaning a lot more. Customer love — or hate — is going to be a profitability issue in a world where switching providers is easy. Focusing on understanding people will result in institutions working in a completely different way — the measure won’t be on “sales” but on problems resolved.
• Guide: Move from selling to advising. By virtue of living in a product-centric world, financial institutions have become sales-driven. But when the barrier to entry to manufacturing and distributing products keeps lowering, traditional institutions increasingly find themselves fighting fintechs and others for turf they used to think they owned. Shiny objects may grab attention and move a sale once, but when that’s over, if an institution hasn’t built a meaningful relationship, people will leave.
People want to be understood, and they want to be cared for. In the context of financial services, this means people want advice. Advice is not about buying a product. It’s about working towards goals, planning for transitions and hopefully creating an overarching, happy story of personal wealth.
Putting energy into human-centric data and focusing on understanding makes the aspiration of providing personalized holistic advice more possible.
The personal financial operating system won’t happen overnight, but institutions can move towards it. Personal financial management offerings that keep people aware of their situation, tools that help them plan for retirement, and hybrid advice platforms that enable collaboration are all steps in the right direction.