A portrait image of Mustafa Domanic.

Adapting to this new way of work takes more than repackaging your current offering.

The smart deployment of artificial intelligence is increasingly essential for refining customer experience and empowering data-driven decisions, writes Mustafa Domanic.

The banking industry is changing, and fast — to stay relevant, both incumbents and newcomers now need to prioritise the use of artificial intelligence (AI).

The playing field has shifted a lot in recent history: traditional banks were once seen as untouchable due to geographic spread and entrenched brand loyalty, but this shifted because of the agile digital approach of neobanks and fintech start-ups. But these newcomers have often sacrificed profitability for size, which at some point simply stops making sense.

So, both incumbents and newcomers will benefit from taking AI seriously, as well as the layers of data that power it. Each group has its competitive edge: the newcomers have quickly amassed data and with digital in mind; while the incumbents already have size and scale, and therefore their own large pool of data.

Players that prioritise AI now — and consider all factors, from infrastructure to ethics — will soon find themselves on more solid ground than their competitors.

The predictive power of AI — and its potential pitfalls

A key strength of AI is its ability to collate consumer data that can be used to target more precisely, offering products and services that match a person’s needs. Imagine, for example, a client has recently started making several purchases at a baby goods store. By analysing this activity, the bank may be able to deduce that they are about to have a child — and so may need a range of new products they may not have previously considered, from a home loan for a larger house to education policies and life insurance.

Beyond refining the customer experience, AI can also empower decision-makers to make data-driven decisions; it can increase reliability by reducing error rates; and it can automate operations and strengthen monitoring. Some specific use cases include AI transaction surveillance (using real-time detection of abnormal payments and alerting users via mobile); an AI-powered robo-advisor for investment opportunities; or real-time loans (using data and a risk management system to provide loans instantaneously).

But what about the pitfalls? Well, the biggest AI issue banks are facing right now is data quality and accessibility — while banks have data, they often don’t know what they have, nor have it in the right place or in the right shape. This is especially the case for incumbents, which started collecting data long before AI was even a consideration.

If stale and low-quality data is inputted, it will teach AI the wrong things and can lead to negative selection bias, at the risk of the bank’s reputation. Banks, as responsible owners of AI, need to overlay ethical considerations because historical biases can affect certain customer groups when models unfairly select based on class, race and gender; or they can act like a loan shark — lending unethically.

Invest in more than just a repurpose

Yes, the scrubbing and rebuilding of data sets is a huge task, but it is one that must happen. So, where to begin on the AI journey? Well, the first step is to realise that adapting to this new way of work takes more than repackaging your current offering for an online world.

having high-quality raw material, and the right data infrastructure, is simply non-negotiable

Often, an entirely new strategy is required: a recent Oliver Wyman report identified four key pillars for a bank’s AI transformation.

First, deploy AI everywhere: the tools need to be utilised from front-office to back. We recommend starting with applications that will deliver value quickly, thus winning the support of the team.

Next, work like a tech company: banks must mimic the agility that gives these companies a competitive edge.

Then there is transforming the technology behind the data brain. Data is the core of AI, so having high-quality raw material, and the right data infrastructure, is simply non-negotiable. Then there are also the important matters of data governance, stewardship and ethics.

Last, there is building an AI factory that can deploy at scale. Assembling a high-calibre team (which can mean employing people whose tech knowledge trumps their banking knowledge) and also formalising a centre of excellence to drive the change is key.

AI is here to stay and it will continue to be transformative. Using it, and using it well, is essential for both incumbents and newcomers. The transition will be time-consuming and many lessons will be learned along the way, but those who take it seriously now will reap ample rewards in the very near future.

Mustafa Domanic is a partner in the financial services practice at Oliver Wyman and works on AI transformation projects in banks across the Middle East.

 

Bracken

The Bracken column is named after Brendan Bracken, the founding editor of The Banker in 1926 and chairman of the modern-day Financial Times from 1945 to 1958.

Read other articles from the column here.

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