Automated Banking For The People

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. In recent years, AI-based technologies have been shown to be effective and practical.

Orchestrating technologies such as AI (Artificial Intelligence), IDP (Intelligent Document Processing), and RPA (Robotic Process Automation) speeds up operations across departments. Employing IDP to extract and process data faster and with greater accuracy saves employees from having to do so manually. Banking automation is applied with the goals of increasing productivity, reducing costs and improving customer and employee experiences – all of which help banks stay ahead of the competition and win and retain customers. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Leading corporate and investment banks, for example, have built up expert teams of quants, modelers, translators, and others who often have AI expertise and could add gen AI skills, such as prompt engineering and database curation, to their capability set. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task.

RPA Case Study in Banking & Financial Services

In addition to RPA, banks can also use technologies like optical character recognition (OCR) and intelligent document processing (IDP) to digitize physical mail and distribute it to remote teams. A system can relay output to another system through an API, enabling end-to-end process automation. The simplest banking processes (like opening a new account) require multiple staff members to invest time.

• Automation at scale refers to the employment of an emerging set of technologies that combines fundamental process redesign with robotic process automation (RPA) and machine learning.
• Leaders must acquire a deep personal understanding of gen AI, if they haven’t already.
• Sharma et al. (2017) used the neural network approach to investigate the factors influencing mobile banking adoption.
• 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).
• The key to an exceptional customer experience is to prioritize the customer’s convenience wherever possible.

Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating automation in banking industry capabilities from technology vendors and partners, including AI specialists. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise.

Mortgage Loan

This process is crucial in identifying loans that may not align with the acquiring bank’s balance sheet strategies, such as those overly concentrated by borrower, geography or asset class. Effective balance sheet merging involves decisions on retaining, restructuring or selling parts of the loan portfolio. Once you’ve successfully implemented a new automation service, it’s essential to evaluate the entire implementation.

​The UiPath Business Automation Platform empowers your workforce with unprecedented resilience—helping organizations thrive in dynamic economic, regulatory, and social landscapes. The world’s top financial services firms are bullish on banking RPA and automation. Customers want a bank they can trust, and that means leveraging automation to prevent and protect against fraud. The easiest way to start is by automating customer segmentation to build more robust profiles that provide definitive insight into who you’re working with and when. To that end, you can also simplify the Know Your Customer process by introducing automated verification services. Similarly, Deutsche Bank saw substantial returns on investment when it embarked upon a comprehensive digital transformation journey where it deployed software to introduce both attended robotic process automation and unattended intelligent automation.

Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance. And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture. These gains in operational performance will flow from broad application of traditional and leading-edge AI technologies, such as machine learning and facial recognition, to analyze large and complex reserves of customer data in (near) real time.

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. Here, we systematically analyze the past and current state of AI and banking literature to understand how it has been utilized within the banking sector historically, propose a service framework, and provide clear future research opportunities. In the past, a limited number of systematic literature reviews have studied AI within the management discipline (e.g., Bavaresco et al., 2020; Borges et al., 2020; Loureiro et al., 2020; Verma et al., 2021).

With it, banks can banish silos by connecting systems and information across the bank. This radical transparency helps employees make better decisions and solve your customers’ problems quickly (and avoid unsatisfying, repetitive tasks). At Hitachi Solutions, we specialize in helping businesses harness the power of digital transformation through the use of innovative solutions built on the Microsoft platform.

Automation simplifies this process, ensuring that all data is consistent and up to date, thereby saving considerable time and reducing the risk of errors. As RPA and other automation software improve business processes, job roles will change. Employees will inevitably require additional training, and some will need to be redeployed elsewhere. Responsible use of gen AI must be baked into the scale-up road map from day one. 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. Leaders must acquire a deep personal understanding of gen AI, if they haven’t already.

Why RPA is Important in Banking?

Employees in that area should be eager for the change, or at least open-minded. It also helps avoid customer-facing processes until you’ve thoroughly tested the technology and decided to roll it out or expand its use. When referring to “concept co-occurrence,” we refer to the total number of times two concepts appear together. In comparison, the word association percentage refers to the conditional probability that two concepts will appear side-by-side. Overall, the papers related to Processes (77%) were the most frequently occurring, followed by Customer (59%) and Strategy-based (48%) papers. From 2013 onward, there was an increase in the inter-relation between all three areas of Strategy, Processes, and Customers.

At one institution, a cutting-edge AI tool did not achieve its full potential with the sales force because executives couldn’t decide whether it was a “product” or a “capability” and, therefore, did not put their shoulders behind the rollout. 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. 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. 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).

How Intelligent Automation Is Redefining the Banking Industry

Siloed working teams and “waterfall” implementation processes invariably lead to delays, cost overruns, and suboptimal performance. Additionally, organizations lack a test-and-learn mindset and robust feedback loops that promote rapid experimentation and iterative improvement. While smartphones took many years to move banking to a more digital destination—consider that mobile banking only recently overtook the web as the primary customer engagement channel in the United States6Based on Finalta by McKinsey analysis, 2023. Goldman Sachs, for example, is reportedly using an AI-based tool to automate test generation, which had been a manual, highly labor-intensive process.7Isabelle Bousquette, “Goldman Sachs CIO tests generative AI,” Wall Street Journal, May 2, 2023. And Citigroup recently used gen AI to assess the impact of new US capital rules.8Katherine Doherty, “Citi used generative AI to read 1,089 pages of new capital rules,” Bloomberg, October 27, 2023.

Indeed, the emergence of AI has generated a wealth of opportunities and challenges (Malali and Gopalakrishnan, 2020). In the banking context, the use of AI has led to more seamless sales and has guided the development of effective customer relationship management systems (Tarafdar et al., 2019). While the focus in the past was on the automation of credit scoring, analyses, and the grants process (Mehrotra, 2019), capabilities evolved to support internal systems and processes as well (Caron, 2019). Exhibit 3 illustrates how such a bank could engage a retail customer throughout the day. Exhibit 4 shows an example of the banking experience of a small-business owner or the treasurer of a medium-size enterprise. The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank.

Some of the most significant advantages have come from automating customer onboarding, opening accounts, and transfers, to name a few. One banking organization has used automation to apply a rule in the loan origination process that automatically rejects loans that fail to meet minimum requirements. This reduces employee workload and enables them to focus on the customers that will generate profit. Applying these tools to ticketing systems means more efficient troubleshooting processes and faster response times to critical support issues. Email management will also be improved, resulting in fewer redundancies and a more accurate categorization of messages according to priority. Likewise, chatbot interactions are more straightforward due to automation based on commonly asked questions and answers.

Increasing customer expectations, stringent regulations and heightened competition are making it more important than ever for banks to optimize and modernize their operations. Automation is helping banks worldwide adapt to organizational and economic changes to reduce risk and deliver innovative customer experiences. Gen AI certainly has the potential to create significant value for banks and other financial institutions by improving their productivity. But scaling up is always hard, and it’s still unclear how effectively banks will bring gen AI solutions to market and persuade employees and customers to fully embrace them. 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. This study systematically reviewed the literature (44 papers) on AI and banking from 2005 to 2020.

However, the benefits of these technologies extend to other safety and compliance applications. In addition to monitoring your own textual data, you can also apply intelligent automation and natural language understanding to third-party monitoring of vendors and support services. Similarly, Know Your Customer (KYC) procedures are also greatly simplified and automatable with symbolic rules for document review and extraction of key entities such as names, ID numbers and location. 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. As the banking industry continues to evolve through mergers and acquisitions, the role of automation in balance sheet management becomes increasingly critical.