A New Era of Banking has begun

A decade or two from today, we might look back at the 13th January 2018 as a date of historical significance for financial institutions across Europe. Payment Services Directive 2 (PSD2) is in effect from today. Also, the UK Open Banking is in force for the top 9 banks across UK and Ireland. For once, ‘exciting’, ‘innovative’ and ‘financial regulations’ can be used in the same sentence. These regulatory changes will create a transparent and open market to foster innovation, put the end-user/ the account holder in control of how they want to use some of the banking services.

In a nutshell, under the PSD2 provision, a bank account holder can allow a third-party-provider (TPP) to:

  • Access their account information and transactional data (things we see on our bank statement)
  • Authorise and initiate payments from a bank account without using long card numbers or issuing schemes. For example, in an ecommerce transaction, there will be a choice to ‘pay direct’ in addition to credit/ debit card.

Financial institutions are having to open up their core systems of record and platforms to the regulated TPPs (but potentially unknown and untrusted by the institution) and treat them without any discrimination. TPPs do not need to enter any contractual relationship with the bank and they can access these systems — most likely via APIs (application programming interface) at no cost. They do, however, need to be fully authorised by the regulator for the services they want to provide in their respective markets. A bank can also play the role of a TPP as well.

With all the changes happening, I thought it might be fun to explore what a TPP can really do with the data. What the art of the possible might look like and, as a result, how the industry might evolve. In pursuit of this goal, I took my own banking data and performed very rudimentary analysis. I am not a data scientist by any stretch of the imagination but here are the dots I joined and conclusions I drew based on very limited data I had access to.

Types of transactions

  • Scheduled and Regular Payments via standing orders and direct debits
  • Regular but unscheduled payments. For example, meals I might buy at or near work on a weekday
  • Irregular payments. This includes impulse purchases, holidays etc.

derived and inferred metadata

· Income and sources

· Number of bank accounts held

· Other Banks and Financial Institutions I have accounts with

· Number of standing orders and direct debits

· Any charges paid on the account either in form of product fees and charges

· Types of savings I have (ISAs, Savings account, pensions etc.)

· Other properties held

· If I am an employee or a business owner

· Employer’s names and locations

· Shopping habits including locations, shops/ brand-affinity, online/ offline

· Utility and service providers and the specific utility plans/ products I have subscribed to

· Size of the house

· Specific products purchased or subscribed to e.g. Amazon Prime, Netflix, BT, Sky, magazine subscriptions, professional memberships

· Charities supported

· Car(s) owned

· Eating habits based on categories of restaurants, frequency, locations, amounts spent

· Hobbies — regular club membership payments or, equipment shops.

· If I have children and potentially even work out their age.

· Preference for using banking services. For example, number of transactions conducted in-branch/ online/ mobile, style of payments (Chip-and-Pin, Contactless, Apple Pay)

· If I travel overseas and my spending habits when I am overseas (do I use cash/ preloaded cards, do I buy cash at airports etc.)

· How much cash do I use, which ATMs I tend to use

· Digital savviness; do I use disrupters — Uber, Airbnb, robot-advisors etc.

· Long term medical illness

· Branch using habits (payments made in branch)

· Taxes paid

· Loan and mortgage repayments.

This analysis demonstrated that the metadata will reveal personal and sensitive information about individuals and families — and could be argued that it is more valuable than individual transactions. And to do this at scale, using algorithms, can enable a TPP to mine this data and create value for the customers. Also, if a TPP is an aggregator (known as an AISP) and a payment initiator (known as a PISP), they can combine additional data and metadata to enrich information and the confidence in it. For example, a bank may not provide full description of a transaction but it may be available to a PISP as they work with merchants to initiate ‘request to pay’ requests.

A TPP can generate and deliver meaningful value to retail customers and creating offerings for B2B buyers as well. Here are some examples:

Creating value for retail customers

Providing guidance/ nudges to help individuals identify financial and non-financial products that are suitable, affordable and better value for money for an individual or, a family. The end goal here will be to help people be better off over time; help them make better decisions based on facts and data rather than emotion. For example:

  • Current accounts that provide similar feature/ functions, better terms (which end-customers generally do not read) and has no monthly fee.
  • Help avoid overdraft fee and charges by giving early notifications when an algorithm predicts the user to go into overdraft as scheduled payment might push the user into an overdraft. Suggest other accounts where money could be moved from temporarily and make it happen in a click.
  • Find “moments” to provide achievable tips and financial education; for example, how to improve credit ratings, or long-term savings for children, or showing how just saving little amounts can result in large savings in the long-term
  • Build and link key life events with financial events such as going to university, getting married, buying a house, buying a car, having children and so on. Giving nudges to save early and at the right ‘moments’.
  • Financial/ Budget planning tools — showing families how much they would save if they were to use alternative products from the same supplier (e.g. fixed rate rather standard rates for utilities) or, by leaving certain vendors covering utilities, media and entertainment etc.
  • Add loyalty points for every shop where the customer may have forgotten to swipe their points card.
  • Manage purchase receipts digitally and help people do their expenses.
  • Help get refunds from retailers, travel and transport companies
  • Micro-lending at the point-of-sale; this could be particularly useful for individuals with low credit rating and who do not have a credit card (more on this in another blog).

Creating value for business customers:

Here are examples of offerings, products and platforms that a TPP may be able to create for the B2B market:

  • As a PISP, a TPP can build a new payment gateway that can process ‘direct from bank’ payments in ecommerce transactions and in-App purchases. For example, a supermarket could embed the new payment style in their mobile app, shoppers can scan and put items in the basket and checkout directly from their mobile. No need to queue and reduce cost of processing payments at the same time. Retailers, Travel and Transport, Holiday companies are likely to be the biggest beneficiaries of direct payments.
  • Over time, as significant number of users join a TPP, they will be able to derive trend and demographic information at a street, borough, city and national level. Hedge funds and investment managers might be interested in this data to give them insights into how much, where and what people are spending money on so they can take these ‘alternative’ data points as inputs in their investment decision-making engine. Similar systems have been making their way into the industry.
  • A new alternative for credit ratings. Most people do not know how credit ratings work and/ or how to improve them. With visibility to transactional and granular data and the metadata, a different type of credit rating engine could be developed — particularly for the ‘underbanked’ segment of our society and those with poor ratings for no fault of their own. A retailer, for example, may be interested in using this type of credit rating to offer micro-loans to customers.
  • Better fraud detection. Most banks have at least one payment fraud engine; however, these systems generally rely on a user’s past behaviour with the bank or rules set within the boundaries of the bank. With access to user’s activities from across multiple banks, the user profile could be richer and the false positives ratio can be dropped further. Thereby enhancing customer experience.

With all the excitement, we must not forget the challenges that come. TPPs must treat the data and the insights that they can draw with care and attention it deserves. Moral and ethical discussions ought to be held and standards need to be established to ensure that the data is ultimately used for the benefit of the end-user without the creepy factor and product sales agenda. More needs to be done to educate end-users about PSD2 and how new types of services that, we haven’t yet seen, will be created. As without mass adoption, the innovation and value will not be financially rewarding for TPPs.

Despite its imperfections, I am excited about PSD2 and looking forward to what the next iterations of the regulations will enable. Combined with similar initiatives in other industries, I am excited about the possibility when all of us are in complete control of our data and that it cannot be stolen or impersonated. We can choose to share our data with whomever we want — in exchange for value. It becomes a valuable commodity we can trade.