Even a routine purchase like putting gas in the car requires consumers to think through how they will make the payment. Will they use a credit card, debit card, cash, or a digital wallet?
Once they decide, they have to navigate through their wallet (digital or physical) and make the payment. Usually that requires an additional digital entry or even paperwork before they can pump the gas.
While people are used to this routine (and others like it) there is a better way. Consider this simplified scenario: A consumer pulls up, pumps the gas and drives away. That’s it.
The payment is made without anything being done by the driver. This type of “invisible transaction” may become common sooner than many think. Already a variation of it is in use at Amazon Go stores in several cities where people can buy goods and “just walk out” — no checkout required.
Here’s what has to happen to enable this type of frictionless payment in an open environment, such as the gas pump scenario:
- Consumers grant the oil company permission to access their digital identity and payment information.
- When they pull up to the gas station, cameras identify the consumer and their car’s license plate.
- When they finish the purchase, the gas company’s point-of-sale system submits the payment via open-APIs (application programming interfaces) used by the consumer’s bank.
- Banks establish open APIs to allow authorized third parties to use their own software to connect to the bank directly and execute transactions.
Not only is this a faster, more convenient transaction for consumers, it also presents them with opportunities to both protect their data and receive the kind of personalized service they increasingly crave.
A Digital Identity Gives Consumers Control
In the past, banks, credit unions and their vendors needed to hold a great deal of information about their customers. In the era of the internet and cloud, there is no reason consumers can’t have a digital identity online with the same availability as with any cloud service.
There are multiple ways to accomplish this. One is to use distributed ledger technology, often referred to as a blockchain. An advantage of distributed ledger is that it addresses trust issues that typically require consumers to give their data, and control of it, to third parties. Distributed ledger allows consumers to retain control of their data, but prevents tampering, even by them. For example, a person could not edit the expiration date of their driver’s license after the document had been verified to be correct.
As digital identity technology moves into the mainstream, it will allow consumers to control how their identity is accessed and by whom. Speed of adoption is a different question. Although people are adopting new technologies at an accelerating rate, distributed ledger technology is not yet well-known or understood by the public, beyond Bitcoin and some other cryptocurrencies that use the technology.
In the gas-station example, consumers would grant permission to the oil company to identify them via facial recognition, and would share their bank information to complete the payment. Consumers could change their mind at any time and revoke access without having to ask the oil company. Because identity and payment information would no longer be spread or stored across numerous vendors, the issues of information lockup, and of having each vendor requiring its own copy of personal information would be eliminated. As a result, the risk of exposure from a data breach would be greatly reduced.
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AI Creates Opportunities to Differentiate
We believe consumer payments powered by a combination of distributed ledger technology for trust, security and identity and artificial intelligence (AI) will become the norm. But even now, by leveraging AI, machine learning, and various forms of automation, banks and credit unions can offer financial services to consumers that today are reserved for commercial clients. A common challenge for both consumers and companies, for example, is liquidity management. Both need money to meet their short- and long-term obligations.
Financial institutions offer commercial clients a variety of liquidity and cash management tools. Consumers, however, have a much smaller range of options. And unless they choose to seek help, generally they are left to manage their own problems.
Banks and credit unions could meet this unfilled need by leveraging AI and automation to build tailored models for each consumer’s funding needs. The model would use existing data, data accumulated through various new interactions and data the institutions are given permission to use. This allows banks and credit unions to engage consumers much more proactively.
“Consumers want more advice on their spending and, in some cases, expect a bank to intervene to help them stick to their budget.”
As a person’s situation changes, the models can adjust. In a study, we found that consumers want more advice on their spending, and in some cases, expect a bank to intervene in transactions to help them stick to their budget.
If through geolocation or other means a credit union or bank knows that a one of their Millennial customers is visiting a car dealership, and if the institution knows further that the customer is at, say, a Ferrari dealer, instead of pushing out loan rates to the person, the institution could proactively send a message saying, “A Ferrari is probably may not be a good choice at this time based on your student loan payments. If you really want one, let’s build a plan to get you to that goal.”
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Not Too Soon to Begin Preparing
Even though distributed ledger technology has yet to see wide adoption in retail banking, we predict that digital identity may emerge as one of the widely adopted use cases for this technology. By introducing the additional trust of distributed-ledger driven digital ID, the road to frictionless payments would be simplified.
Similarly, most financial institutions are just starting their journeys toward ubiquitous AI. And most still have a fair amount of work to do to ensure their data is in good shape to enable it to drive the AI models of the future. This fundamental step should be a priority to enable them to take part in the unfolding future of retail banking.