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

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. As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs. Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort.

• From an organizational risk standpoint, Mall (2018) used a neural network approach to examine the behavior of defaulting customers, so as to minimize credit risk, and increase profitability for credit-providing institutions.
• As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest.
• Cloud computing also offers a higher degree of scalability, which makes it more cost-effective for banks to scrutinize transactions.
• JPMorgan, for example, is using bots to respond to internal IT requests, including resetting employee passwords.

In the early stages of AI implementation, it is essential to develop fast and reliable AI infrastructure (Larson, 2021). Baesens et al. (2005) utilized a neural network approach to better predict loan defaults and early repayments. Ince and Aktan (2009) used a data mining technique to analyze credit scores and found that the AI-driven data mining approach was more effective than traditional methods.

To Deliver Faster, Personalized Customer Experiences

As previously discussed, one of the key research areas, AI and banking, relates to credit applications and granting decisions; these are processes that directly impact customer accessibility and acquisition. Here, we develop and propose a Customer Credit Solution Application-Service Blueprint (CCSA) based on our earlier analyses. To be competitive in this rapidly changing sector, you need the most innovative and robust tools available. Understanding the value of AI technology like intelligent automation and natural language for banking institutions like yours, will give you the resources you need to successfully manage the essential functions of your organization. Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization. Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.

The finance and banking industries rely on a variety of business processes ideal for automation. Many professionals have already incorporated RPA and other automation to reduce the workload and increase accuracy. However, banking automation can extend well beyond these processes, improving compliance, security, and relationships with customers and employees throughout the organization.

RPA Case Study in Banking & Financial Services

The sub-theme of AI and customer experience (Papers 11) covers the use of AI to enhance banking experience and services for customers. For example, Trivedi (2019) investigated the use of chatbots in banking and their impact on customer experience. This research provides insights for practitioners and marketers in the North American banking sector. To assist in the implementation of AI-based decision-making, we encourage banking automation in banking industry professionals to consider further refining their use of AI in the credit scoring, analysis, and granting processes to minimize risk, reduce costs, and improve customer experience. However, in doing so, we recommend using AI not only to improve internal processes but also as a tool (e.g., chatbots) to improve customer service for low-complexity tasks, thereby directing employees’ efforts to other business-impacting activities.

You can make automation solutions even more intelligent by using RPA capabilities with technologies like AI, machine learning (ML), and natural language processing (NLP). According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry. Systems powered by artificial intelligence (AI) and robotic process automation (RPA) can help automate repetitive tasks, minimize human error, detect fraud, and more, at scale. You can deploy these technologies across various functions, from customer service to marketing.

For instance, Khandani et al. (2010) utilized machine learning techniques to build a model predicting customers’ credit risk. Koutanaei et al. (2015) proposed a data mining model to provide more confidence in credit scoring systems. From an organizational risk standpoint, Mall (2018) used a neural network approach to examine the behavior of defaulting customers, so as to minimize credit risk, and increase profitability for credit-providing institutions. No one knows what the future of banking automation holds, but we can make some general guesses. For example, AI, natural language processing (NLP), and machine learning have become increasingly popular in the banking and financial industries. In the future, these technologies may offer customers more personalized service without the need for a human.