Uncertain economic conditions, disruptive industry forces and ever-evolving customer needs are keeping financial services businesses on their toes, demanding they keep abreast of emerging technologies.
Fortunately, finance executives are cognizant of the critical role technology investments play in their ability to transform their business to counter competitor disruption. The 2022 Gartner CEO and Senior Business Executive Survey found that 92% of CFOs planned to increase their investment in technology, up from 70% in 2021.
To harness the power of technology, however, executives not only need to know what’s out there but also must effectively implement those technologies that are most relevant to their business propositions. Regarding the latter, the Gartner Executive Survey highlights that it’s not as easy as it seems, with the results showing that only 30% of technology projects ultimately succeed.
Legacy technology systems are often complex and siloed, which stands in the way of developing a seamless technology architecture that is flexible and scalable. Gartner recommends that companies build a cohesive, forward-thinking technology strategy that provides a roadmap for CFOs to follow and prioritizes investments in emerging market technologies that are likely to have the greatest impact to changing business realities.
Ultimately, a business should have a modular technology system based on composable application building blocks.
So, what are the emerging technologies that are likely to have the biggest impact on finance in the next two years? In our Fintech Market Report 2023 you can find out that the top three emerging technologies in order of importance are expected to be personalized experiences and predictive analytics, artificial intelligence and blockchain and cryptocurrencies.
Personalised experiences and predictive analytics
The traditional players in the financial services industry have been slower than other industries to respond to the changing needs of their customers. But with disruptive forces on the rise, they have little choice but to overhaul their way of doing business or they will be left behind. Banking is an industry that traditionally interacts with its customers in person in its extensive branch networks. However, the industry’s transition to providing primarily online services has meant that personal engagement has fallen to the wayside.
At the same time, customers in other industries, like entertainment (Netflix and Spotify), retail (Amazon) and travel (Airbnb), have become used to the convenience of getting what they want, when they want it. These companies maximise engagement by delivering hyper-personalised experiences that anticipate customer needs and wants based on predictive analytics that is derived from their clients’ past behaviour and preferences.
Banks stand to benefit in a variety of ways if they too rely on predictive analytics to interact with customers and provide them with the services that best suit their needs. Personalisation enhances customer experience, increases customer loyalty and retention, reduces the cost of acquiring new customers, boosts revenues and ensures that marketing efforts are more cost-effective and have greater returns on investment.
The Impact of Artificial Intelligence on The Banking Industry
The benefits to incorporating Artificial Intelligence (AI) as an emerging technology into banks' front, middle and back-office operations are potentially endless. But banks need to adopt an AI-first 360-degree strategy if they want to reap all the benefits of scaling up AI-driven operations, reduce costs and maximise revenues.
Adopting a piecemeal approach would likely do more bad than good to the bank’s operational success because of investing and implementing AI solutions comes at a considerable financial and human resource cost. If well done, however, the financial benefits more than outweigh these costs.
How can banks use AI?
The most visible use of AI is the frontline chatbots many banks have used for some time. But to date these have been relatively unsophisticated, fulfilling rudimentary customer requests. With machine learning and the potential offered by the immense customer data sets owned by the bank, chatbots are becoming far more sophisticated and human-like in their interactions with customers.
They can draw on the predictive analytics generated by the machine learning programmes to anticipate and meet the needs of customers, making them aware of other financial services solutions available to them. Maximising the AI potential of chatbots thus paves the way for massive revenue-generating potential and enhancing customer satisfaction and loyalty.
This year, AI-enabled ChatGPT has been dominating the news headlines because it is expected to have such a profound impact worldwide across many industries but is expected to transform the fintech and banking industries. According to Parvin Mohmad, Chat GPT can potentially disrupt the financial services industry in several ways because its neural network architecture and training data have been developed to learn human language's complex and nuanced details and provide meaningful, cogent responses. As a result, it promises better customer services, provides personalized financial advice, reduces errors and offers greater security,
AI also offers immense, arguably the greatest, potential in fraud detection and assisting banks in meeting regulatory compliance requirements. These middle-office responsibilities have grown exponentially as fraudsters have become ever more sophisticated and the regulatory burden significantly more onerous.
The robotic process automation that underlies most AI capabilities is well suited to these tasks because they require monitoring innumerable complex real-time transactions that are spread across the globe. Thus, AI has the potential to reduce the size of compliance teams by taking over the time-consuming and error-prone work and enabling these skills to be better used to make judgement calls on alerts of potentially fraudulent transactions, money laundering or terrorist financing.
Banks can also make use of AI in back-office operations, improving the underwriting process by enhancing the information available to make better-informed credit and loan decisions. Banks typically rely on narrow credit scoring and customer reference evidence to make these decisions. AI-based systems can build a more sophisticated and detailed view of a client’s creditworthiness by assessing their wider financial behavioural patterns and what these say about the risk of a customer defaulting.
Examples of banks that have already begun to unlock the benefits of AI in their operations include JP Morgan, which has won awards for building an AI/ML platform it named OmniAI to accelerate the speed at which the firm deploys AI/ML applications across the firm, enhancing many of its internal processes. Meanwhile, Bank of America created Erica, a sophisticated virtual assistant that is the financial services version of Alexa because it helps customers stay on top of their finances.
What obstacles prevent banks from deploying AI capabilities at scale?
Two of the biggest hurdles that stand in the way of banks incorporating AI at scale include legacy systems that are inflexible and outdated and data, the lifeblood of AI, scattered across business silos and thus not much use in its current form. These challenges don’t stand in the way of deploying AI, which has become a business imperative if banks want to survive. But the necessary overhaul of these two key components does mean banks need to have a clear AI strategic plan to guide them on their journey towards creating the robust foundation that will be the bedrock of an AI-first future.
The strategy also needs to encompass big banks' two divergent business objectives: to instil the innovation, flexibility, and agility that drives the success of disruptive fintechs so that they can compete head on, but without compromising the security standards, compliance, and complexity of a large traditional bank.