Artificial Intelligence in Banking 2022: How Banks Use AI

These process-related uses of technology include institutional uses of technology to improve internal service processes. For example, Soltani et al. (2019) examined the use of machine learning to optimize appointment scheduling time, and reduce service automation banking industry time. Overall, regarding the process theme, our findings highlight the usefulness of AI in improving banking processes; however, there remains a gap in practical research regarding the applied integration of technology in the banking system.

Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. In the event of an M&A, if a bank’s loan trading desk is managing loans manually, using spreadsheets and traditional methods, the integration process can become cumbersome and error-prone.

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From a customer perspective, COVID-19 has led to an uptick in the adoption of AI-driven services such as chatbots, E-KYC (Know your client), and robo-advisors (Agarwal et al., 2022). Disruption from players such as digital-only banks, FinTech firms, and Big Tech companies with designs to move into the financial services sector all threaten to upend the banking industry as we know it, and to encroach on traditional banks’ market share in the process. This clear and present danger has led many traditional banks to offer alternatives to traditional banking products and services — alternatives that are easy to attain, affordable, and better aligned with customers’ needs and preferences. Bank employees deal with voluminous data from customers and manual processes are prone to errors. Moreover, a single error in the important banking process leads to the case of theft, fraud, and money laundering case. Instead of humans processing data manually, simple validation of customer information from 2 systems can take seconds instead of minutes with bots.

This study systematically reviewed the literature (44 papers) on AI and banking from 2005 to 2020. We believe that our findings may benefit industry professionals and decision-makers in formulating strategic decisions regarding the different uses of AI in the banking sector, and optimizing the value derived from AI technologies. We advance the field by providing a more comprehensive outlook specific to the area of AI and banking, reflecting the history and future opportunities for AI in shaping business strategies, improving logistics processes, and enhancing customer value. As the banking industry continues to evolve through mergers and acquisitions, the role of automation in balance sheet management becomes increasingly critical.

Front office

Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. Such platforms offer enhanced data analytics, providing clear insights into the merged loan portfolios. This facilitates informed decisions regarding asset allocation, risk management and strategic planning. It’s a critical process during the post-merger integration phase, where aligning financial strategies and objectives of the combined entity is essential. The Banking and Financial industry is seen to be growing exponentially over the past few years with the implementation of technological advancements resulting in faster, more secure, and reliable services.

In call centers, AI-powered virtual assistants help consumers manage financial transactions, from bill payments to opening a new account to transferring money. Customer service agents that are augmented with AI assistants are able to focus on higher value assignments, which improves employee efficiency and enhances customer experience. In today’s rapidly evolving landscape, the successful deployment of gen AI solutions demands a shift in perspective—that is, starting with the end user experience and working backward. This approach entails a rethinking of processes and the creation of AI agents that are not only user-centric but also capable of adapting through reinforcement learning from human feedback. This ensures that gen AI–enabled capabilities evolve in a way that is aligned with human input. First, as the data show, automation, by reducing the cost of operating a business, may free up resources to invest in other areas.

Finance Industry Commits to Scaling Enterprise AI

Despite some initial setbacks, fintech has finally made good on its promise to transform the way banks do business, leading 88% of legacy banking institutions to report that they fear losing revenue to financial technology companies. Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies.

Only by following a plan that engages all of the relevant hurdles, complications, and opportunities will banks tap the enormous promise of gen AI long into the future. Early successes in scaling gen AI occurred when banks carefully weighed the “build versus buy versus partner” options—that is, when they compared the competitive advantages of developing solutions internally with using market-proven solutions from ecosystem partnerships. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing. Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts. For many, automation is largely about issues like efficiency, risk management, and compliance—”running a tight ship,” so to speak. Yet banking automation is also a powerful way to redefine a bank’s relationship with customers and employees, even if most don’t currently think of it this way.

Customer Experience

Let’s look at some of the leading causes of disruption in the banking industry today, and how institutions are leveraging banking automation to combat to adapt to changes in the financial services landscape. Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com. Automated platforms can harmonize disparate data systems from merging institutions, ensuring seamless integration.

The term AI was first used in 1956 by John McCarthy (McCarthy et al., 1956); it refers to systems that act and think like humans in a rational way (Kok et al., 2009). In the aftermath of the dot com bubble in 2000, the field of AI shifted toward Web 2.0. Era in 2005, and the growth of data and availability of information encouraged more research in AI and its potential (Larson, 2021). More recently, technological advancements have opened the doors for AI to facilitate enterprise cognitive computing, which involves embedding algorithms into applications to support organizational processes (Tarafdar et al., 2019). This includes improving the speed of information analysis, obtaining more accurate and reliable data outputs, and allowing employees to perform high-level tasks.