Back in 2019, the Bank of England made a prediction. The future for financial services, it said, would be built to a very great extent around machine learning. And this stands to reason.
We are living in an era where artificial intelligence is increasingly integrated into every aspect of life. Across industries, the transformative potential of these technologies is becoming abundantly clear – from the significant savings in costs, time and human error to leveraging data for insights to drive decision-making and competitive edge, when it comes to AI, the sky it seems is the limit. For banking alone, a 2020 McKinsey report suggests that AI technologies could amount to a whopping $1 trillion of additional annual value globally.
What then is holding some financial services players back from fully exploiting the promise of machine? Why are so many firms still struggling to implement machine learning models at scale?
Partly there’s fear; fear of the new, of change, of the unknown. This era, dubbed The Fourth Industrial Revolution, is characterised by extraordinary technology-driven transformations; so-called cyber-physical systems that are reshaping the way we live and work. And an unstoppable digitisation of everything from onboarding to compliance and fraud detection, to loans and investments.
Beyond fear, there’s a lack of clarity about how to use AI – where to deploy it, and where not to; and how to build the right strategies to integrate and fully leverage it across the organisation.
Many firms today still grapple with inflexible core technology systems – outmoded or under-invested systems that fail to function as a single, centralised data backbone. Data assets are often fragmented across business and technology silos, while legacy operating models make collaboration between these teams difficult if not impossible.
Meanwhile the pace of change continues to quicken unabated. The Covid-19 pandemic has accelerated many of the big trends in digital engagement and digitisation globally - so much so that for financial executives the choice has become very simple: understand AI or risk being left behind.
Here are a few things that you need to know if you want get ahead of the curve, compete successfully and thrive in the new normal.
1. AI should be the foundation for new value propositions and distinctive customer experiences.
Remember, AI technologies can help your firm:
- Lower costs through the efficiencies created by optimal resource allocation, automation of time-heavy processes and reduced error rates.
- Increase revenues by enabling the personalisation of services offered to customers.
- Boost innovation and identify new opportunities by processing huge amounts of data for critical information and game-changing insights.
2. AI helps you cut through the noise, and make better decisions
In financial services, the pain is real. Sifting noisy and complex data sets with endless potential scenarios is not only very hard for human beings, but it also comes with a huge risk of making mistakes – and expensive ones. Machine learning algorithms can perform these tasks at a fraction of the time. They can distinguish between significant patterns and anomalous ones in nano-seconds. And they can be used to help analysts distil hundreds of potential indicators for future investment returns into a few robust measures. This is a technology that can synthesise massive amounts of data and make fresh insights – so you can make well-informed decisions.
3. AI can help personalise your customer services
There is a growing appetite among customers for the personalisation of services and products that extends to financial services. A 2019 survey by Accenture found that one in two banking customers were open to things like personal conversations and targeted product marketing. AI-powered data sifting and social listening can provide a wealth of data and insights about customer preferences, needs, interests and pain points – insights that can be deployed to drive engagement, improve the customer experience and increase retention while reducing costly turnover rates.
4. AI can help you find new customers
Lead generation is both labour and time-intensive. But AI can take the pain out of the process by parsing multiple factors – individual characteristics, profitability, costs, potential for growth – to create an ideal customer profile. It can also be used to find the ideal customer in external data sources and identify the best tactics for approaching individuals. And it can do all this fast.
5. AI brings its own challenges too
AI is a whizz at seeing patterns and extracting them for decision-making. But when those patterns connect to historic data points that in turn tie to bias, the risk of customer dissatisfaction and bad publicity are all too real. Recent headlines have trumpeted around the world about poor mortgage lending to minorities and credit loans to women – all generated by “black box” AI applications. A solid rule of thumb is this: algorithms are only as good as the data they use.
The future for financial services is inexorably tied to the future of technology.
To fully exploit the promise of machine learning, executives need to be knowledgeable about AI, and that means understanding the (huge) benefits it represents, while being able to navigate the risks. Ignoring its promise means being left behind and rendered obsolete. Astute firms are making the investment in building a rock-solid foundation in AI and machine learning, exploring the future of innovation in financial services and developing practical techniques that can be implemented today.
Programmes like the Imperial College Business School online AI & Machine Learning in Financial Services are here to educate leaders about these AI applications in their industry and are vitally important for leaders in understanding the realm of the possible – and the potential risks that exist along the way.