We have all read statements such as recent regulatory developments, along with disruptive innovation, will lead to a significant shift in customer behaviour, expectation, and experience. What once looked like hyperbole, however, is rapidly becoming fact.

The revised Payment Service Directive (PSD2) will force banks to provide other participants in the payment ecosystem with access to customers' payment account information (XS2A). This represents a paradigm change in the eyes of bankers, who have traditionally seen this information as amongst their most valuable assets. It is currently foreseen that the information will be shared via an application programming interface (API), i.e. a standardised interface which will become mandatory for banks through PSD2.

The vast volume of data in today's world means that data-extraction tools are an absolute must. Though the rise of the machines is not yet upon us, banks should get used to the ideas of robot process automation (RPA) and digital labour. The development of these technologies in recent years has made them ready—truly ready, today—to support operational processes.

Another term, distributed ledger technology (DLT), i.e. blockchain, has already become well known. DLT will increase the pace of data exchange, enabling transactions to happen in seconds instead of days. Although some bankers still regard blockchain sceptically, there are already numerous concrete use cases that make it worth keeping high on the agenda.

In real terms

Let us now take a concrete, classic example: buying a car. Those interested in an automobile purchase know that financing is required, and in the past this would have been the starting point of a lengthy and cumbersome process that might involve trying to coordinate information and offers from different banks.

Now, however, a customer might begin by configuring the desired car on the manufacturer's website, and then simply generating a quote from any bank that cooperates with the manufacturer's open API. The banks will, if consent is given to access the customer's information, be able to perform an instant credit assessment including an analysis of the customer's income and spending patterns.

The information obtained can then be processed by a robot-supported workflow (an example of RPA), during which time the customer's information is also benchmarked against peer groups, enabling the bank to allocate a risk profile. The end result is a credit assessment that is executed within seconds.

Additionally, any credit request to obtain finance can be shared with other banks in order to get the best end result for the customer.

Next, to onboard the customer, the preferred bank can use DLT, which will turn the once-lengthy process of completing paperwork into the simple and automated process of accessing the customer's official identity information.

The end result: the financing is confirmed within minutes, and the customer can buy the car—perhaps without even leaving the couch.

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