By Grant Caley, CTO of NetApp
At the leading edge of the financial services industry, artificial intelligence (AI) is transforming the way that businesses operate. As that wave crashes over the industry at large, we might expect to see the legacy IT system – monolithic, in-house, and bespoke – become a thing of the past as banks prepare for the reality of data-led operations. With new technologies helping to streamline and optimise processes ranging from quantitative trading to risk management, bringing the benefits to bear for customers will mean analysing vast datasets and making them actionable and transparent.
Consumers, meanwhile, are already having their expectations conditioned by this new reality across other sectors, particularly in terms of self-managing their purchasing. Whether you want to transfer money, apply for a mortgage, order food, hail a taxi, or just speak to friends, apps are now the go-to tool. Faced with tough challenges around security and trust, the financial sector is still catching up – and will continue to do so as AI-powered offerings become endemic. Those legacy IT systems, meanwhile, are failing to deliver the flexibility and visibility that customers need in order to fully manage their own finances.
The evolution of legacy systems
Financial services have been a data-heavy proposition for a long time, but as banks and insurers open up this kind of functionality to their customers – and attempt to do so in a way which offers ease of use – the additional data flows created cause ripples which can affect every part of the IT estate, imperilling speed and reliability. For many, this pressure is accentuated by cloud-native start-up competitors built expressly to enable the digitally-native experiences customers now expect.
Today, many companies are positioning themselves as convenience providers by offering a seamless transaction experience. For instance, Rocket Mortgage’s slogan “push a button, get a mortgage” promises a quick online mortgage application. This customer-friendly tech, not only captures the attention of the customer but also offers them accessible support through social media platforms and apps. The impact of this trend can be seen with JPMorgan, which has allotted $11.4 billion on technology for the year ahead, signifying a serious prioritisation of tech within the company.
The magnitude of that spend also, however, indicates the magnitude of the challenge. In a situation where root-and-branch system overhauls can be either prohibitively expensive or, given the value of pre-existing datasets, prohibitively risky, while ad-hoc solutions are either insufficient or only redouble the pressure on legacy systems, businesses are turning to alternative, AI-led solutions for transformation.
The way in which many organisations have begun to combat lethargic IT systems has been to use automation tools such as Robotic Process Automation (RPA). According to a recent NetApp survey, decision-makers in both the banking and insurance industries see this as the ideal starting point for integrating AI solutions in their companies. By intelligently integrating with existing systems and managing how data flows through and around it – rather than relying solely on the transport networks those systems already have – RPA can be an effective bridge between the accrued value of long-running systems and the competitive advantage of innovative customer experiences.
Banks and insurance companies are, in fact, the leading advocates and adopters of RPA technology. The analyst firm Gartner currently values global spending on RPA software at $680 million– and by 2022, that’s expected to reach $2.4 billion. Our study, designed to assess the extent of AI’s impact on the industry and forecast its direction of travel, found that almost half of organisations within the industry already work with AI. In particular, portfolio management (27%), customer service (47%), and fraud prevention (40%) were all bright spots in adoption rates – these areas speaking particularly well to RPA’s capacity to carry out repetitive tasks with a low error rate. In the future, managing employees’ workloads and making customer care more personal were identified as growth areas for AI.
The Boost of the Cloud
Cloud computing is moving to the forefront as a focus in the financial world. The adoption of the cloud is becoming a catalyst for businesses to transform their operations for future-proofed capability. In the NetApp survey, 87% of participants revealed that they rely on AI services that draw their computing power from the cloud. The cloud provides a more elastic alternative to on-premise data storage and enables both flexibility and the necessary scale of performance to process large quantities and varieties of data.
Businesses must synchronise modernisation programmes with the need for speed and stability in order to maintain a reliable IT system. Without the boost of cloud, financial services organisations are unable to build resilient operations and break down operational data silos separating risk, regulation, and customer support, limiting their ability to operate at the necessary scale. Once massive data sets are combined in one place, this helps security teams to effectively identify and flag fraudulent transactions, which is an ever-emerging concern for the financial services industry.
AI also underpins chatbots which, using Natural Language Processing, can perform basic customer service workloads and even automatically translate messages. With this, customers can be helped at any time of the day, meeting their expectations with a real-time reality. The inference computation which this requires – and which is typically performed on highly specialised hardware – is again a workload best suited to cloud-based infrastructure.
Adapt, Enhance, Evolve
Adding AI to the fleet of technologies within the finance and insurance industries will significantly enhance the services they can provide to consumers. As we’ve seen from the increasing implementation rates of AI within both industries, the shift is already underway. When the survey came out, over 56% of participants had already been developing their own strategy for several years and had even gone so far as to integrate one or more solutions into daily operations.
Looking forward, 30% of respondents declared that they plan to introduce a whole department focused on AI into their business. As we see more businesses invest in AI as a strategic solution, financial institutions must have access to the data locked in technical and organisational silos in order to thrive. Building a clear data strategy, maintaining an open mind and recognising the potential of AI is necessary for banks and insurance companies to evolve.
Global Banking & Finance Review
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