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Artificial Intelligence: Where Does The Real Value Lie?

 
 

Banking | Wynn Davies |
05 May 2021

Recently, I attended American Banker’s Digital Banking: AI & Automation virtual event and was fortunate enough to attend a session with Mastercard Worldwide’s Executive Vice President, Artificial Intelligence, Rohit Chauhan.

As I listened to Rohit discuss his company’s approach to artificial intelligence (AI), I nodded my head, despite this being a virtual event. Rohit described an approach to AI that I talk to banks about – but is still the road less travelled in a world where 54% of financial services organisations with 5,000+ employees have already adopted AI.

Rohit shared that when he took on his role, Mastercard’s CEO told him he wanted AI to be everywhere at Mastercard – in every single group and division.

I wanted to applaud the CEO from my home office. Most financial firms are still asking, “Where should I leverage AI?” They’re thinking point solutions and quick wins. Too few realise that the real value of AI is as a holistic foundation. The difference in business value between a comprehensive approach and a piecemeal one is substantial.

Allow me to build on Rohit’s points based on my experience consulting on technology and business. I see three main areas where financial services companies have crucial decision points around AI.

1. IN AI, YOU GET WHAT YOU PAY FOR.

Business leaders who view AI within a strategic framework for their entire organisation will initially invest more. This causes many executives to balk and look for smaller AI projects to fund. However, the value derived from an overall AI strategy and roadmap will be far greater over time. You can still build in quick wins, but they’re not one-offs. They all build upon each other in a master plan that will lead to the business outcomes you want.

That said, I understand the conundrum some clients are facing – the perceived high cost of AI, especially diving into it heavily, limits their ability to invest. In fact, it’s the top constraint cited by firms. However, in firms where the C-suite sees the value in comprehensive AI – and sells the board on it – great results can be seen in various business areas, from customer acquisition and retention to operational efficiency.

2. DON’T UNDERESTIMATE THE POWER OF MINDSET.

This applies not only to AI but also to its companion, Cloud. When we talk about migrating to the cloud, the firms who are most successful approach it with the right mindset: one focused on accelerating product development, creating clear and transparent cost models, understanding where they need to re-host, re-platform and re-factor, leveraging data gravity, moving to infrastructure as code and continued testing – all to get true benefit.

AI needs the same mindset. Leaders need to approach it as a predictive technology. Its power lies in the ability to develop repeatable patterns, which allows solutions to be applied to adjacent problems and accelerates solutions across an organisation. Building transparency into mindset means that not only are AI costs transparent, but so is AI logic. Regulators and audit groups can’t keep it a black box; they need to be able to explain the AI approach and the logic it’s using.

And last, mindset needs to include scalability. AI solutions need to leverage Cloud where appropriate, with an emphasis on growing and evolving. As with all the algorithms we develop, AI solutions need to be maintained, trained and monitored.

3. GETTING YOUR DATA HOUSE IN ORDER IS ESSENTIAL.

It’s not just cost concerns that hold some firms back. Insufficient infrastructure and data quality are second and third on the list of executives’ top challenges.

It’s tempting to believe firms who tell you that you can overlay AI on less-than-stellar data. You can, but you won’t reap value from it – and you may then be running your business based on bad assumptions. AI is all about predictive operations, and those predictions should be based on real-time, accurate information. Data architecture doesn’t have the “wow” factor that AI does, but what you can do with AI and a good architecture will impress your board.

For example, just 16% of financial institutions are leveraging data to provide specific one-to-one guidance to customers on how to finance their life goals and ambitions. With AI-driven data insights, doing this for individual clients and customers becomes so much simpler. Given the drive toward hyper-personalisation throughout the financial services sector, being able to better know your customer is a competitive advantage.

FORESIGHT BEATS RETROACTIVE ACTION.

Almost 9 out of 10 financial services executives say they will increase AI-related investments through 2025. Investment banking currently leads the way, followed by retail. Now is the time to think carefully about how and where to invest, how your use cases tie together for greater value and how to plot out quick, successive wins that build upon a larger AI roadmap.

I’ve seen too many business leaders hastily jump aboard the AI train with siloed investments; that path is filled with regrets. A wise, timely, holistic investment in scaled AI across your teams, however, will bring business value for years to come, benefiting your organisation as well as your customers.

Wynn Davies

EVP, US Finance Sector

Wynn has 20+ years of experience in advisory and delivery for Financial Services firms. At Endava, he leads our North American Financial Services team and helps to lead our thinking for Banking globally. Wynn refers to himself as a Financial Services geek; much to his wife’s dismay, he is always happy to talk shop. Outside of work, Wynn is kept busy by his two young children, coaching rugby, and supporting his family’s focus on alleviating food poverty.

 

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