Impact of AI and RPA in Banking SpringerLink

A survey in the financial section by PricewaterhouseCoopers shows that 30% of the respondents were not only experimenting with RPA but were on the way to adopting it enterprise-wide. Interestingly, as ATMs expanded—from 100,000 in 1990 to about 400,000 or so until recently—the number of tellers employed by banks did not fall, contrary to what one might have expected. According to the research by James Bessen of Boston University School of Law, there are two reasons for this counterintuitive result. If you’re of a certain age, you might remember going to a drive-thru bank, where you’d put your deposit into a container outside the bank building.

Wu and Olson (2020) highlight the need for banking institutions to continue investing in AI technologies to reduce future risks and enhance the integration between online and offline channels. 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). A service blueprint is a method that conceptualizes the customer journey while providing a framework for the front/back-end and support processes (Shostack, 1982). For a service blueprint to be effective, the core focus should be on the customer, and steps should be developed based on data and expertise (Bitner et al., 2008).

Customer Service

At this stage, the customer receives a credit decision through the robo-advisor. The traditional approaches for credit decisions usually take up to two weeks, as the application goes to the advisory network, then to the underwriting stage, and finally back to the customer. However, with the integration of AI, the customer can save time and be better informed by receiving an instant credit decision, allowing an increased sense of empowerment and control. The process of arriving at such decisions should provide a balance between managing organizational risk, maximizing profit, and increasing financial inclusion.

Regarding processes, AI and credit is one of the areas that has been extensively explored since 2005 (Bhatore et al., 2020). We recommend expanding beyond the currently proposed models and challenging the underlying assumptions by exploring new aspects of risks presented with the introduction of AI technologies. In addition, we recommend automation in banking industry the use of more practical case studies to validate new and existing models. Additionally, the growth of AI has evoked further exploration of how internal processes can be improved (Akerkar, 2019). For instance, we suggest investigating AI-driven models with other financial products/solutions (e.g., investments, deposit accounts, etc.).

New technologies are redefining the customer and employee experience in financial services.

The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. This is not to suggest that as computers become more intelligent, they may not able to perform the more abstract tasks that still require humans. In my view, we will ultimately get to that world, although probably at a slower pace than most people expect. But as machines become more dominant, further product innovations and changes to competitive market structure will lead to new and more complex tasks that will still require human effort.

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 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. In addition, while there is an abundance of research on credit risk, the exploration of other financial products remains limited. Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications. As a virtual workforce, this software application has proven valuable to organizations looking to automate repetitive, low-added-value work.

When it comes to maintaining a competitive edge, personalizing the customer experience takes top priority. Traditional banks can take a page out of digital-only banks’ playbook by leveraging banking automation technology to tailor their products and services to meet each individual customer’s needs. RPA in banking industry can be leveraged to automate multiple time-consuming, repetitive processes like account opening, KYC process, customer services, and many others. Using RPA in banking operations not only streamlines the process efficiency but also enables banking organizations to make sure that cost is reduced and the process is executed at an efficient time. According to reports, RPA in banking sector is expected to reach $1.12 billion by 2025.

• The gradual shift toward its customer-centric utilization has prompted the exploration of new dimensions of AI that influence customer experience.
• A successful gen AI scale-up also requires a comprehensive change management plan.
• They should approach skill-based hiring, resource allocation, and upskilling programs comprehensively; many roles will need skills in AI, cloud engineering, data engineering, and other areas.
• While implementing and scaling up gen AI capabilities can present complex challenges in areas including model tuning and data quality, the process can be easier and more straightforward than a traditional AI project of similar scope.
• With UiPath, SMTB built over 500 workflow automations to streamline operations across the enterprise.
• Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities.

For example- one of our clients HDFC bank had been facing huge challenges in process inconsistency and a high rate of errors that were leading to lower revenue and higher operational costs. To process a single loan application through HDFC bank processing time was 40 minutes. But leveraging the AutomationEdge RPA solution made the process a lot simple and helped the banking staff t bring down the time spent on a loan application from 40 minutes to 20 minutes. Too often, banking leaders call for new operating models to support new technologies. Successful institutions’ models already enable flexibility and scalability to support new capabilities. An operating model that is fit for scale-up is cross-functional and aligns accountabilities and responsibilities between delivery and business teams.

Blanc Labs’ Banking Automation Solutions

Barclays introduced RPA across a range of processes, such as accounts receivable and fraudulent account closure, reducing its bad-debt provisions by approximately $225 million per annum and saving over 120 FTEs. Today, many of these same organizations have leveraged their newfound abilities to offer financial literacy, economic education, and fiscal well-being. These new banking processes often include budgeting applications that assist the public with savings, investment software, and retirement information.

It’s not just about operational efficiency; it’s about enabling strategic decision making, ensuring compliance and driving profitability. The revolution in banking M&As, driven by technological advancements, promises a future where banks are more resilient, efficient and prepared for the challenges of an ever-changing financial world. Such platforms offer enhanced data analytics, providing clear insights into the merged loan portfolios.

AI-bank of the future: Can banks meet the AI challenge?

In addition to strong collaboration between business teams and analytics talent, this requires robust tools for model development, efficient processes (e.g., for re-using code across projects), and diffusion of knowledge (e.g., repositories) across teams. Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual process. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.

For example, professionals once spent hours sourcing and scanning documents necessary to spot market trends. Today, multiple use cases have demonstrated how banking automation and document AI remove these barriers. Connect people, applications, robots, and information in a centralized platform to increase visibility to employees across the organization. Greater visibility not only helps provide a view as to whether tasks are performed as they should be, but also provides insight into where any delays are occurring in the workflow. This enhanced visibility also aids decision-making and makes reporting simpler, and helps identify opportunities for improvement.

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. Two-thirds of senior digital and analytics leaders attending a recent McKinsey forum on gen AI1McKinsey Banking & Securities Gen AI Forum, September 27, 2023; more than 30 executives attended. Said they believed that the technology will fundamentally change the way they do business. The pressing questions for banking institutions are how and where to use gen AI most effectively, and how to ensure the applications are fully adopted and scaled within their organizations. In addition, the COVID-19 pandemic has brought on a plethora of challenges in the implementation of AI in the banking sector. Although banks’ interest in AI technologies remains high, the reduction in revenue has resulted in a decrease in short-term investment in AI technologies (Anderson et al., 2021).

This is particularly beneficial when one of the entities involved in the merger is distressed, and there’s a need to quickly identify and address high-risk loans or nonperforming assets. Customers expect fast, personalized experiences from onboarding to any future interactions they have with the bank. Having access to customer information at the right point in an interaction allows employees to better serve customers by providing a positive experience and promoting loyalty, ultimately giving them a competitive edge. Award-winning global asset management company, Insight Investment optimized transparency around its end-to-end business processes by visualizing the data stored in Bizagi applications, facilitating process management and further process improvement. Figure 4 highlights the concept associations and draws connections between concepts. The identification and classification of themes and sub-themes using the deductive method in thematic analysis, and the automated approach using Leximancer, provide a reliable and detailed overview of the prior literature.

However, banking automation helps automatically scan and store KYC documents without manual intervention. If you are curious about how you can become an AI-first bank, this guide explains how you can use banking automation to transform and prepare your processes for the future. Many, if not all banks and credit unions, have introduced some form of automation into their operations. According to McKinsey, the potential value of AI and analytics for global banking could reach as high as $1 trillion. As a result, it’s not enough for banks to only be available when and where customers require these organizations. Banks also need to ensure data safety, customized solutions and the intimacy and satisfaction of an in-person meeting on every channel online.

Similarly, Khandani et al. (2010) found machine-learning-driven models to be effective in analyzing consumer credit risk. The sub-theme, AI and credit (15 papers), covers the use of AI technology, such as machine learning and data mining, to improve credit scoring, analysis, and granting processes. For instance, Alborzi and Khanbabaei (2016) examined the use of data mining neural network techniques to develop a customer credit scoring model. Post-2013, there has been a noticeable increase in investigating how AI improves processes that go beyond credit analysis. The sub-theme AI and services (20 papers) covers the uses of AI for process improvement and enhancement.

Ultimately, the lessons for the banking industry maybe to anticipate and proactively shape how automation will spur innovation, increase demand, and alter the competitive dynamics, beyond operational transformation. Discover how leaders from Wells Fargo, TD Bank, JP Morgan, and Arvest transformed their organizations with automation and AI. With RPA and automation, faster trade processing – paired with higher bookings accuracy – allows analysts to devote more attention to clients and markets. Traders, advisors, and analysts rely on UiPath to supercharge their productivity and be the best at what they do. Address resource constraints by letting automation handle time-demanding operations, connect fragmented tech, and reduce friction across the trade lifecycle. Discover smarter self-service customer journeys, and equip contact center agents with data that dramatically lowers average handling times.