The Financial Services industry is keen to seize opportunities from AI adoption, but sustainable success hinges on high-quality data and responsible strategies as Nic Leszczynski, Principal Solutions Engineer UKI, at Riverbed Technology, explains in the following analysis for IFA Magazine.
The Financial Services sector has long been seen as a trailblazer. With the UK market reaching £133.5 billion in 2023, successful innovations from within the field – such as blockchain, crypto, embedded finance and open banking – have filtered down to other industries. Now, the Financial Services industry has AI firmly in its sights.
Recent research revealed that Financial Services is among the most forward-thinking industries when it comes to developing and implementing AI, as 94% of business and IT leaders agree that it will help them deliver a better digital experience to end users. With 46% of Financial Services leaders also saying that they’re fully prepared to implement AI now, it seems to have a more established foothold than other sectors.
However, less than half of the leaders feel they are fully ready. Many decision-makers expressed concern over data quality and its readiness for full AI deployment, with only around a third rating their data as excellent for completeness (36%) and accuracy (34%). The Financial Services sector must therefore temper its early enthusiasm for AI with a responsible, well-rounded adoption strategy and implement practical AI that works and scales.
AI: a strategic necessity for Financial Services
With competition intensifying and customer expectations growing, the drive towards AI is no surprise. In fact, automated decision-making, predictive analytics and personalised customer interactions are already revolutionising commercial finance enterprises across the UK. Take NatWest, for example, which claims its embedding of AI across operational processes has broadened its ability ‘to transform the efficiency of [its] business, track compliance and accelerate engineering capabilities’.
The potential benefits are plain to see, which makes it tempting for businesses to view AI adoption as a race. But to truly sustain their sector’s current pole position, organisations operating in Financial Services should exercise some restraint. After all, the competitive advantages enticed by AI are only achievable if its primary foundation – data – is fit for purpose.
Why data readiness is non-negotiable
AI thrives on high-quality data – which is fortunate, because Financial Services organisations are likely to already have plenty of user information at their disposal. As a result, their focus needn’t be on acquiring more data; it should be on effectively handling it. However, with the Bank of England identifying that ‘data privacy and protection; data quality; data security; and data bias and representativeness’ are four of the top five perceived current risks of AI, data management is becoming a strategic hazard.
Navigating this digital minefield is vital. Without excellent data, AI algorithms cannot be trusted to deliver reliable insights and outcomes – meaning insurers could offer mispriced policies or banks could make biased lending decisions. Similarly, inaccurate information might also lead to regulatory breaches, which are costly in both financial and reputational terms.
To avoid being exposed to risk, wasting budget or damaging performance, organisations must ensure their datasets are comprehensive, current and clear of errors. They can do this by:
- Centralising data management: Disparate data slows AI adoption and reduces visibility across all digital estates. Investing in unified data stores creates a consolidated view, offering more detailed analytics and greater transparency into operations.
- Adhering to data governance: To ensure AI is being used ethically, regulations are becoming increasingly stringent. In order to keep up, organisations should appoint a designated AI team tasked with overseeing data function, integrity, security and compliance.
- Collaborating with AI solution providers: A trusted technology partner can assist in deploying AI-ready infrastructure tailored to the needs of Financial Services – accelerating the value delivery of AI and aligning businesses with industry standards.
Factoring in these initiatives as part of a wider data-first implementation strategy will help organisations to shore up their digital operations. From that point, they will be better equipped to move forward with confidence in their AI investments.
AI as the gateway to greater user experiences
Ultimately, one of the key aims of responsible AI adoption is to protect and transform the end-user experience. With AI backed by excellent data, banks can resolve payment disputes faster, insurance companies can deliver bespoke recommendations, and wealth managers can use real-time insights to form their investment strategies.
These benefits aren’t just hypothetical either. NatWest’s chatbot Cora ‘handled c. 11 million retail customers conversations in 2023 alone’. Likewise, Virgin Money’s AI-powered chatbot, Redi, has already ‘supported over one million conversations since its launch in March 2023’. As a result, the business has reported a 39% uplift in customer satisfaction and saved operating expenses by retaining over 50% of interactions through its virtual assistant.
Evidently, AI is already setting new standards for efficiency and customer engagement within the Financial Services sector. What’s more, if these technologies are trained on comprehensive datasets, then it’s fair to assume that their self-learning capabilities mean their positive impact will become even more scalable.
Setting the foundation for future growth
For an industry that sets the precedent for global technology, balancing ambition with responsibility is crucial for Financial Services to continue leading the next chapter of digital transformation. Right now, the revolutionary power of AI promises to streamline processes, driving operational efficiency and growth – but without an up-to-standard digital infrastructure, these benefits will remain out of reach.
To close that gap and to harness AI in the right way, financial firms must underpin their adoption strategies with high-quality data. They can make strides towards this by establishing the processes and partnerships that safeguard the accuracy, completeness and security of the information they already possess.
By prioritising this aspect of the adoption strategy before anything else, organisations can display the patience and thoroughness that can help them reap all the operational rewards AI has to offer.
