The 2018 McKinsey Banking Review noted that, on average, between 2012 and 2017, banks valuations lagged those of all other industries, meaning that investors are and will be looking elsewhere for value. How will banks respond to this phenomena?

In the UK it appears that revenue is now more important than cost control. In the two years to the start of 2018, the four major UK banks had a collective zero increase in revenue growth. Operating costs for that period were collectively reduced by 22%. The third quarter 2018 year to date results show the collective revenue growth is 3% while costs have risen by 4%. The contribution of the four banks, (revenue minus operating costs) was £27 billion.

The collective reason for the rise in costs was ‘digitalising the bank’ – which is probably fair given that the greatest, immediate need for the banking industry is to stay relevant to their customers. Any industry whose affluent client base is being courted by e-commerce entities offering real-time insight and recommendations on what to do next needs to respond.

One area for the banking industry is to use Artificial Intelligence to help clients and staff to handle the new, expanding digital environment. Culturally this is a hurdle. Historically banks have provided services that rely on set solutions for the average problem with scripted responses. As we are moving from the general to the specific reason an item has been rejected doing so because ‘all the boxes were not ticked’ is becoming untenable.  Having an isolated instance affect the making of a decision on a financial requirement without regard to the rest of the relationship is both exasperating to the customer and expensive for the bank.

Complaints data from the UK Financial Conduct Authority shows:

• A 10% increase in the first half of 2018: 4.1 million complaints in six months
• An 11% increase in Personal Protection Insurance (PPI) complaints to 1.7 million
• A 9% increase in total redress to consumers, totalling £2.6 billion in the period

By applying human insight, machine learning, psychology and anthropology and linking these to consumers and firms, an improved situation evolves. Financial interactions are critical and are treated as such by all. To track and understand how these interactions are made by a customer can be useful to ensure errors are not repeated. AI working with a banking relationship manager can create a much more harmonious customer service.

At the same time the banking industry has shifted its opinion on cloud computing over the past two years, shifting from ‘cloud based services will never catch on’ to ‘does X (specific function) come as a service? For example:

• Payment as a Service
• Trade Finance and Working Capital as a Service
• Compliance and Regulation as a Service
• Consumer and firm aggregated financial ledgers as a Service

The major cloud providers offer readily available and cyber-secure access to the banking industry for the banks and their customers. Again, culturally ingrained attitudes hold sway: banks are used to building their own systems often with enough capacity for all.

By using intelligence, moving away from rote learning, offering alternative financial solutions and providing inexpensive and secure access the banking industry can become much more relevant. Banks by building on their inherent strength of trust, they can become more valuable to their customers and investors.