Robotic process automation in banking industry: a case study on Deutsche Bank Journal of Banking and Financial Technology

However, the current literature lacks either research scope and depth, and/or industry focus. In response, we seek to differentiate our study from prior reviews by providing a specific focus on the banking sector and a more comprehensive analysis involving multiple modes of analysis. The three main channels where banks can use artificial intelligence to save on costs are front office (conversational banking), middle office (fraud detection and risk management) and back office (underwriting).

The second necessary shift is to embed customer journeys seamlessly in partner ecosystems and platforms, so that banks engage customers at the point of end use and in the process take advantage of partners’ data and channel platform to increase higher engagement and usage. ICICI Bank in India embedded basic banking services on WhatsApp (a popular messaging platform automation banking industry in India) and scaled up to one million users within three months of launch.9“ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com. The presence of AI and RPA technology is prominently seen in the customer experience zone. However, it plays a significant role in the operations of banks and financial services.

How to Implement RPA in Banking: Use Cases in 2024

From taking over monotonous data-entry, to answering simple customer service queries, RPA has been able to save financial workers from spending time on repetitive, labor-intensive tasks. The bank also used the intelligent automation platform to expedite its document custody procedures. Consider, for example, the laborious paperwork that is typically required to refinance homes. By leveraging these data-driven insights, banks can optimize their loan portfolios to align with the newly formed entity’s goals and risk appetite.

Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts. Automation helps banks streamline treasury operations by increasing productivity for front office traders, enabling better risk management, and improving customer experience.

Report Automation

This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives. For example, leaders at a wealth management firm recognized the potential for gen AI to change how to deliver advice to clients, and how it could influence the wider industry ecosystem of operating platforms, relationships, partnerships, and economics. As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest. Generative AI (gen AI) burst onto the scene in early 2023 and is showing clearly positive results—and raising new potential risks—for organizations worldwide.

In addition, while there is an abundance of research on credit risk, the exploration of other financial products remains limited. In the Customer theme (26 papers), we uncovered the increasing use of AI as a methodological tool to better understand customer adoption of digital banking services. The sub-theme AI and Customer adoption (11 papers) covers the use of AI as a methodological tool to investigate customers’ adoption of digital banking technologies, including both barriers and motivational factors. For example, Arif et al. (2020) used a neural network approach to investigate barriers to internet-banking adoption by customers. Belanche et al. (2019) investigate factors related to AI-driven technology adoption in the banking sector. Payne et al. (2018) examine the drivers of the usage of AI-enabled mobile banking services.

Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities. Another emerging change is that banks are opening up APIs to the surrounding ecosystem. Three-quarters of today’s APIs are still internal (that is, they are targeted at developers or customers within an organization), while only one-quarter are available to partners or the public. Internal APIs continue to be important and should not be overlooked; they enable efficiency and speed, and they significantly reduce integration costs. Banks are planning on increasing the share of APIs available for partners and the public to almost 50 percent over the next three years, laying the technical foundation for wider ecosystems.

The numerous benefits of RPA make it inevitable to the financial and banking services. As per the reports, by 2025, the RPA industry is expected to grow to $6.7 trillion in the global economy. The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions. Just as banks could believe they were finally bridging the infamous divide between business and technology (for example, with agile, cloud, and product operating model changes), analytics and data rose to prominence and created a critical third node of coordination. While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent. Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities.

Our recent global survey on APIs in banking revealed that 88 percent of respondents believe APIs have become more important over the past two years (see sidebar, “About the research”). Large banks are launching API programs and allocating about 14 percent of their IT budget to APIs on average. Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application. Banks that foster integration between technical talent and business leaders are more likely to develop scalable gen AI solutions that create measurable value.