AI has an incredible breadth and variety of use cases for financial services organisations – but there is a strategic thread that should tie them all together. When used well, AI helps financial firms do more of what they’re great at. Banks want to do more banking, provide more mortgages, make good credit risk decisions and take the right assets onto their balance sheets. Investment firms want to maintain value-adding advisory relationships and make good market risk and portfolio management decisions. And insurers want to underwrite accurately and manage claims efficiently. It’s about freeing up time and energy to focus on what matters most, both short term and long term.

However, banking and insurance firms seeking to get the most value from the technology must prepare properly – not least because AI's power can only truly be unlocked with suitable guardrails in place.

Preparing to unlock AI

The first step is to complete earlier generations of digital transformation programmes, because AI is only as good as the data it can access. So, technical debt, including fragmented information or an unwillingness to embrace cloud opportunities, will hold financial organisations back. Modernising core infrastructure and getting to a well-architected estate can also free up capital for investing in leading digital technologies, which can be a differentiator and save AI initiatives time later on.

It’s also important to set and communicate principles for responsible AI. As with any important new technology, AI must build on a foundation of security, risk management, and trust. Financial firms need to be clear on how risk and controls related to AI maps across their 'Three Lines of Defence', in terms of risk taking, risk policy setting, plus checking and assurance. Tracking evolving regulatory compliance considerations is paramount for safe, secure, resilient, and responsible adoption of AI. At Microsoft, the advancement of AI is driven by six principles for responsible AI.

Once these measures are in place, the best approach is to move quickly from planning to action, because while evaluating use cases is important, it’s often tempting for financial firms to invest too much time scenario planning. Ask first what your organisation needs, rather than sifting through the thousands of things AI can now do, and encourage a culture of rapid experimentation that empowers staff to test solutions in practice. Getting projects from pilot to production is where the magic happens – people learn best by doing. So, a good approach is to start with internal use cases to build the organisational muscle and then expand, exposing new technologies such as Generative AI to customers in service interactions before embedding these capabilities natively in products.

Impressive use cases are already emerging

Generative AI may still feel new, but leading financial services organisations are already using it – and building on their existing AI investments in cloud, analytics, and automation – to improve the customer and employee experience.

Hargreaves Lansdown (HL) is using Copilot for Microsoft 365, the generative AI companion that’s integrated into the existing productivity apps. This is boosting productivity and creating new efficiencies in the ways HL staff work, with meeting notes and summaries being automatically generated for financial advisers, for them to review and edit before sending to clients. Capitalising on AI tool helps HL’s advisors to complete client documentation four times faster than previous workflows, saving employees an estimated two to three hours per week.

Rabobank is deploying text and voice capable chatbots, known as Power Virtual Agents, to improve the customer journey and experience and handle 60,000 to 80,000 calls every month. This efficiently routes conversations to the most qualified human adviser, while immediately answering easy-to-solve questions or connecting customers to self-service channels when appropriate. The virtual agents helps Rabobank to start and complete 40-50% of customer enquiries without having to escalate the conversation to a human agent. This is a prime example of how the deployment of conversational AI leads to increased efficiency and cost savings, which in turn helps financial institutions to stay competitive and retain their investment capabilities.

NatWest uses Dynamics 365 to empower frontline staff, by providing a single AI enabled platform for managing customer data and interactions. On average, NatWest’s 14,000 agents respond to 50 million customer interactions per year, which equates to 95 years of continuous conversation if each conversation is as short as a minute. Aggregating customer data to have a single view of the customer and using AI capabilities to pull the right contextual information and automatically share it between colleagues at the right time, makes collaboration faster and easier, and helped NatWest achieve over £10 million in software savings.

Generative AI can also accelerate financial sector innovation, by providing developers with code suggestions for dozens of programming languages and accessing application programming interfaces (APIs) more quickly, which helps to speed up banking software development. Non-technical business users can also now interrogate code using natural language, and take part in low-code development and innovation – testament to the democratising power of generative AI.

Maintaining AI momentum

There are some other important areas financial organisations should focus on longer term, if they want to keep driving positive momentum for AI adoption, in their organisation and for the sector.  

Track more than productivity: Collaboration with government and regulators is essential: Financial firms should see regulation as an opportunity to create a safer and more secure operating environment for AI, which means engaging positively and constructively with legislators and trade bodies. Collaboration will achieve the best outcomes for the industry.

Measuring the outputs from AI should go beyond whether staff are doing the same things faster. The goal is also for people to feel like they have more time and energy, with all the benefits these entail for company culture. People using AI, not AI itself, are still financial organisations biggest competition. Using these new tools to augment, rather than replace, human performance - especially in areas like risk analysis and underwriting, will deliver the best results.

Value sustainability in platforms and partnerships: AI technology itself should be environmentally sustainable. Microsoft is powering its cloud computing datacentres with 100% carbon-free energy where available and have roadmaps to do so for all of them by 2030. Modern finance firms need partners for whom this remains a priority and will keep investing in R&D to make AI run more efficiently.

These are exciting times for our sector. As David M. Brear, CEO of challenger FinTech consultancy 11:FS, comments, if we leverage the technology correctly, AI has the potential unlock a new wave of human creativity and change financial services, forever: "The arrival of generative AI can bring a whole wealth of new creative capacity to teams, drive business growth and efficiency, and enhance leadership decision-making. If adoption is as widespread as I believe it’s going to be, this new era for AI will truly be one for the history books.”