Future of Robotic Process Automation RPA in The Banking Industry
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.
An automated approach to loan participation and syndication management can streamline this process significantly. They offer a comprehensive view of the combined loan portfolios, facilitating decisions on which loans to retain, sell or restructure. 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. 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. As RPA and other automation software improve business processes, job roles will change.
Enhancing Decision Making With Data-Driven Insights Through Standardization
Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort. The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands. 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. Banks that utilize RPA have given employees back time to spend on more complex tasks while artificial intelligence technology handles back-end operations. By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise.
- 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.
- 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).
- Some leading companies have achieved a technological maturity that outpaces the business side’s understanding.
- This standardization is key to avoiding data chaos and ensuring efficient, coherent management post-merger.
- Hence, RPA is a technology that involves an entity with the ability to mimic human abilities in a sequence of steps to complete a task without human intervention.
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.
Game-Changing Processes Leading Banks Has Automated
The industry has benefited from the proliferation of API developer portals for fast and efficient publishing and usage. Further, the establishment of more standards and guidelines, as well as the introduction of an API taxonomy, facilitate development and reuse. Interestingly, as ATMs expanded—from 100,000 in 1990 to about 400,000 or so until recently—the number of automation banking industry 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.
Banking automation has facilitated financial institutions in their desire to offer more real-time, human-free services. These additional services include travel insurance, foreign cash orders, prepaid credit cards, gold and silver purchases, and global money transfers. InfoSec professionals regularly adopt banking automation to manage security issues with minimal manual processing.
In addition, over 40 processes have been automated, enabling staff to focus on higher-value and more rewarding tasks. Leading applications include full automation of the mortgage payments process and of the semi-annual audit report, with data pulled from over a dozen systems. 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. As we’ve highlighted, AI offers powerful use cases that are set to transform the delivery of financial services.
Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency. To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences. We begin from the initial step of customer acquisition, and proceed to credit decision, and post-decision (Broby, 2021). In the acquisition step, customers are targeted with the goal of landing them on the website and converting them to active customers.
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. Banks’ traditional operating models further impede their efforts to meet the need for continuous innovation. Most traditional banks are organized around distinct business lines, with centralized technology and analytics teams structured as cost centers. Business owners define goals unilaterally, and alignment with the enterprise’s technology and analytics strategy (where it exists) is often weak or inadequate.
- RPA and intelligent automation can reduce repetitive, business rule-driven work, improve controls, quality and scalability—and operate 24/7.
- Xu et al. (2020) examined the effects of AI versus human customer service, and found that customers are more likely to use AI for low-complexity tasks, whereas a human agent is preferred for high-complexity tasks.
- Potential reasons include APIs not being as embedded in the business case life cycle, and a reduced focus on regulatory programs that require APIs.
- The sub-theme, AI and marketing (nine papers), covers the use of AI for different marketing activities, including customer segmentation, development of marketing models, and delivery of more effective marketing campaigns.
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. Also, by leveraging AI technology in conjunction with RPA, the banking industry can implement automation in the complex decision-making banking process like fraud detection, and anti-money laundering.
To that end, you can also simplify the Know Your Customer process by introducing automated verification services. For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative. Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success.
For instance, Trivedi (2019) examined the factors affecting chatbot satisfaction and found that information, system, and service quality, all have a significant positive association with it. Ekinci et al. (2014) proposed a customer lifetime value model, supported by a deep learning approach, to highlight key indicators in the banking sector. Xu et al. (2020) examined the effects of AI versus human customer service, and found that customers are more likely to use AI for low-complexity tasks, whereas a human agent is preferred for high-complexity tasks. It is worth noting that most of the research related to the customer theme has utilized a quantitative approach, with limited qualitative papers (i.e., four papers) in recent years.
When you hear the word “bots,” your mind goes to physical robots; the kind of factory floor automation you see in a car plant. But it means something very different for financial services companies, and it can be the thing that helps you get the edge over your competitors. Beyond the impact on tellers, ATMs also introduced new jobs—armored couriers to resupply units and technology staff to monitor ATM networks.