Financial services firms are using Artificial Intelligence (AI) and Machine Learning (ML) more and more heavily.

The latest research from The Bank of England and Financial Conduct Authority, for example, suggests that 72% of UK firms in the sector are developing or deploying ML, a branch of AI that gives machines the ability to "learn" from data to improve computer performance.

The same report predicts that the median number of ML applications used by organisations in the sector will grow by 3.5 times in the next three years. 

But what exactly will that use of AI and ML in financial services look like? 

Where next for AI and ML in financial services? Three visions

The short-term future could mean AI and ML tools being widely used, but in isolated, specialised cases – to streamline a few processes and provide insights to a few teams – without these tools being used in core operational decision-making. This approach would broadly mean carrying on with developing specialist financial uses for AI and ML, such as fraud detection. 

Alternatively, it could mean continuing a current trend in financial services AI and ML, where much use of the tools is solely customer-facing, leaving internal operations largely untouched by the insights and automation they bring. That would mean using the tools for things like faster and more efficient onboarding, and developing virtual assistants to deliver more personalised support to customers. 

Or, thirdly, it could mean both of the above with a crucial addition: operational data – analysed by AI and ML – forming a digital backbone that runs throughout the business. 

With that third approach, financial services firms could use AI and ML to:

• streamline business operations through automation
• optimise employee experience by freeing people from repetitive, low-value work
• improve productivity.

Technology and people, working together, unlock real potential – enabling businesses to strengthen competitive advantage, be more responsive to customers, deliver greater economic and social value, and generate more meaning and purpose for individuals in their work.

This third approach would be about equipping staff and leaders to make confident decisions faster than ever with AI-assisted insights and recommendations.

A data-driven financial services firm

A genuinely data-driven financial services firm would use AI and ML to help everyone, in all areas of the business, answer business-critical questions and make informed predictions about the future. 

It would mean running flawless business and financial operations, using touch-free automation to handle repetitive, predictable tasks, for improved accuracy and productivity.

Automation would eliminate repetitive manual data entry, sorting, scanning, indexing and archiving. These tasks would be done faster, with less opportunity for error – and people would be freed to focus on work that adds value.

It would mean empowering people for maximum performance through engaging experiences tailored to each person and organisation.

In HR, for example, data can improve the impact of upskilling. Personalised learning experiences offer a route to improvement that's right for each worker. Automated assessments can deliver real-time feedback. And the most effective training can be identified based on skills, career goals, and emerging industry trends. 

These tools can also improve how you improve life for employees en masse. Natural language processing can analyse millions of comments and feedback reports to identify where managers and leaders can make the most significant impact.

AI and ML analysis in finance can identify the most likely future trends and allow informed decision-making. 

How to use AI effectively but ethically

AI could be the most transformative technology of our lifetime. Unsurprisingly, businesses are still determining how to use it effectively but ethically. 

Several factors are essential to organisations getting AI and ML right.

Keeping humans in the loop  

Your teams are augmented, not replaced, by AI and ML.

A recent study showed that 93% of business leaders believe humans should be involved in AI decision-making. We agree. 

AI and ML should enhance workers, not replace them. Humans and machines should work in partnership, with AI and ML applications providing supporting information and recommendations, while humans stay in control of decisions.

Decisions should be guided by data, not dictated by it. Ethically, this also involves making AI and ML explainable, being transparent about training data and potential for bias, and establishing clear roles and responsibilities around its use.

Clearly there is more work to do here. Only 29% of business leaders are currently very confident that AI and ML are being applied ethically in business.

Data structure  

Structuring the data in a way that allows you to apply AI and ML is 85% of the effort. By taking that same data structure and applying a different model that solves a different problem, we can roll out more use cases, faster.

Trustworthy, transparent AI 

That means a laser focus on AI and ML ethics and customer trust, and an eye on emerging regulation globally. 

And it means transparency about how AI and ML models are designed and function. Follow Explainable AI and ML principles to help customers understand how models are intended to function, what data is used in their build and how it is used, what outcomes to expect, how the models were trained and tested, and how they were tested for bias.

What are the leading firms doing? 

Cutting-edge financial services firms are already using AI and ML technology, not just in specialist applications, but to help deliver better employee experiences, improve operational efficiencies, and provide insights for faster, data-driven decision-making. 

These businesses are building the intelligent digital backbone that is empowering them to optimise the management of their two most important assets – their people and their money.