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The financial services industry is rapidly changing. In a world where the future means hybrid working, hyper-personalisation, and Banking as a Service, there is no doubt that data and AI at scale is going to be needed to succeed in the future of finance.

Technologies such as AI and automation can help organisations solve business problems and inform decision making. It can also increase productivity by taking over repetitive manual tasks. Data and analytics can help organisations better understand their customers, build resilience, discover new opportunities and remain competitive.

However, as more organisations adopt and capitalise on data and AI, those who are successful are the ones who take an unsiloed approach. They bring the whole organisation along on the journey and focusing on the result. This, in many cases, is as much about culture change as it is about technology.

I recently took part in an engaging panel discussion where myself and Peter Jackson, author of the Chief Data Officer’s Playbook and the Chief Data and Analytics Officer at Carruthers and Jackson, Norman Neimer, Chief Data Scientist at UBS, and Steve Higgins, Lead Impact Strategist for financial services at SparkBeyond. We discussed the challenges and opportunities financial services organisations need to consider when implementing AI into their business.

What are the challenges facing the financial services industry?

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Despite the use of chatbots, machine learning programmes, and a lot of experimentation, adoption of AI is lower in financial services than in other industries. And as much as we all wish it were, every new technological implementation is not always smooth sailing.

“It would be hard within one organisation to see a successful uniform pattern of adoption and operationalisation of AI,” agrees Peter. He adds that there are four key challenges to AI adoption for financial services organisations, which the rest of our panel agree:

  1. Low levels of data maturity: Are you ready to embrace AI? Measure your data maturity first.
  2. Low levels of data literacy: To get good AI, you need good data knowledge.
  3. Low levels of investment: The opportunities are huge, but it hasn’t been matched yet. Not only in the tech, but the processes, people and data.
  4. Bad experiences and risk: Ensuring your data is secure and compliant helps reduce risk. To be quick to succeed you need to not be afraid to fail fast and move on.

The key to successfully implementing AI is based on data, company culture, and the business values. Don’t start with AI just for the sake of it. Look at your business values, and what you want to achieve. And instead of starting with the biggest use cases that will drive the biggest impact, start with the smaller value but more achievable cases. That will help you see results faster.

Deliver personalised customer experiences with data and AI

In a recent report by SparkBeyond and Microsoft, banks using AI found that 30-80 percent of conventional patterns are no longer relevant. Customer behaviours are constantly changing, and financial services need to achieve customer intimacy. People want to know that organisations understand who they are, know what they want and can meet their needs. Just look at how quickly we adapted to changes in the last 12 months. Banks need to understand these patterns quickly to best serve their customers. AI can inform decision making to help build personalised experiences for customers, from banking to investments. Additionally, the ability to understand customer experiences and make more dynamic decisions around customer vulnerability using data will help across society more broadly.

ABN AMRO, the third largest bank in the Netherlands, wanted to be able to efficiently access and use data to help support customers during key stages of their lives. By migrating to the cloud, ABN AMRO could scale faster, access better insights to empower both customers and employees.

“I foresee a future where we have a much smaller on-premises footprint. There’s so much data processing and analytics we can do, making predictions, doing all kinds of complex calculations, and developing entirely new, cutting-edge use cases, for example with Azure Machine Learning Services,” says Piethein Strengholt, Principal Data Architect.

Establish a data-driven business

To gain a full view of the customer, internal silos need to be reduced. Data modernisation is key to fixing silos. Additionally, it can help organisations uncover new insights that wouldn’t otherwise get accessed without a collective view of data. At the same time, organisations need to be sure this data is of good quality.

Nationwide uses Dynamics 365 to connect their data silos and provide a holistic view of the customer. “It’s much easier to check in with clients and keep them up to date with key product and service changes,” says Anthony Pooley, Customer Relationship Manager in the business savings team. “We’re spending less time on admin tasks which gives us the capacity to spend more time on value added activities which our clients appreciate. We now have a wealth of information at our fingertips.”

Underpinning a data-driven business is data governance. “There’s a need to have regulatory oversight to ensure the data is safe and protected,” says Steve. Ensure valuable business and customer data is adequately protected, including the proper regulatory compliance. At the same time – think about how that data will be used. The responsible and ethical use of data will build strong AI models, reducing bias and risk. At the same it, it will ensure the models deliver exactly to customer’s needs.

Build a data-first culture in finance

“I would encourage data leaders to really think about how they can build culture and business impact within their organisation,” says Norman. “That’s going to require some rewiring and rethinking of processes.”

According to a CBI/PwC survey, 93 percent of financial service businesses expect a greater need for skills in technological proficiency and 71 percent believe that people management and leadership skills will be needed.

Therefore, when you start implementing AI projects, don’t just bring your C-level and boardroom suite along the journey. Bring your frontline workers too. “Building a model doesn’t get you all the way. What gets you all the way is bringing the users along, turning the model into an app or report that someone can use and drive impact with it,” says Norman.

Illimity bank offers employee discounts for loans, but it’s application and approvals process was slow an inefficient. Originally, a single employee handled all the requests, performed complex prescreening processes and sending summaries to HR for authorisation. By automating this workflow, they saved 15 hours a month of employee time. They also sped up the process for employees wanting to use the benefit. What’s more, they now can see the big picture when it comes to employee loans, using the data to power further insights.

To build data literacy and use new technology such as AI, the right skills are needed. Democratising AI and data for every employee helps uncover innovation. For example, employees can drive innovation by using low/no code apps to automate workflows. This will enable them to spend more time with customers or on other value tasks.

Reduce risk and build resilience with data and AI

Traditionally, incumbent banks and other financial service organisations are risk averse. As a result, there is a slower take up of new technologies such as AI and cloud services. However, as challenger banks and digital-native fintechs appear, traditional organisations need to rise to the challenge.

In this challenge, lies opportunity. Regulators recognise the value in new technology, for example, with the implementation of Open Banking. The Bank of England recently issued a Supervisory Statement on Outsourcing and Third Party Risk Management, which focusses on facilitating adoption of the cloud and other new technologies.

AI can help gain insights into risk – spotting fraud quicker or flagging unusual banking activity. It can also be implemented to help protect data – taking over low-level monitoring, scanning thousands of signals daily to spot cyberthreats.

AI can also help reduce errors, with RPA taking over repetitive manual tasks. Low and no-code AI solutions can improve operations, while reducing errors. And starting with a small AI project, for example in the middle or back office, will increase data literacy, while showcasing its effectiveness at a smaller risk than starting with a big project.

Improve sustainability goals with data and AI

For organisations across every industry, Environmental Social and Governance (ESG) is becoming a reputational importance. “What I’ve seen as a trend is financial firms no longer look at their ESG as a reputational risk, but as a credit risk, i.e., if they don’t help reduce their carbon footprint, that’s going to impact their portfolio. AI can help you understand a complex problem,” says Steve. AI can help uncover insights around financial services value chain to reduce waste, save energy, and optimise processes.

Recently, NatWest and Microsoft announced a partnership to help NatWest’s business customers understand how they can start reducing their emissions, using data and AI to inform the decision making process.

Data and AI for innovation

Implementing AI isn’t just a technology process. It’s requires organisations to think culturally about how to address decision making within the organisation to be able to take opportunities quickly. Start small. Experiment fast, fail fast and learn fast. Keep the focus on the customer and the problems to solve but let the data guide decisions. As a result, challenges previously not already considered may be identified.

To successfully innovate and deliver competitive advantage, organisations need to establish a data-driven business, underpinned by skilled employees and focussing on customers as a key part of their value chain.

Find out more

How are leading finance institutions are capitalising on AI analytics

Future-proofing financial services with AI analytics

Boost the innovation of banking business models

About the author

Janet Jones, Industry Executive – UK Financial ServicesJanet currently leads the Industry Strategy for Financial Services at Microsoft UK. She ensures that drivers of change and emerging technological trends across the sector are core to how Microsoft works with Financial Services organisations, supporting their digital transformation. Before joining Microsoft in 2018 Janet held roles within commercial banking; latterly at Lloyds Banking Group and prior to that, Barclays and RBS. She has a personal interest in cultural transformation and has also played an active role in supporting and driving the inclusion and diversity agenda during her career.

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