Robotic Process Automation RPA in Banking: Examples, Use Cases

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. During our inclusion/exclusion criteria, it is plausible that some AI/banking papers may have been missed because of the specific keywords used to curate our dataset. In addition, articles may have been missed due to the time when the data were collected, such as Manrai and Gupta (2022), who examined investors’ perceptions of robo-advisors. Second, regarding theme identification, there may be a potential bias toward selecting themes, which may lead to misclassification. In addition, we acknowledge that the papers were extracted only from the WoS and Scopus databases, which may limit our access to certain peer-reviewed outlets. It is a method for identifying, analyzing, and reporting patterns within data (Boyatzis, 1998).

Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies. These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision making in terms of both speed and accuracy. The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1). 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. A number of financial services institutions are already generating value from automation.

Thematic analysis

This facilitates informed decisions regarding asset allocation, risk management and strategic planning. It’s a critical process during the post-merger integration phase, where aligning financial strategies and objectives of the combined entity is essential. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges. Some have installed hundreds of bots—software programs that automate repeated tasks—with very little to show in terms of efficiency and effectiveness.

• Banks, lenders, and other financial institutions may collaborate with different industries to expand the scope of their products and services.
• The use of these two approaches provides additional validity and quality to the research findings.
• This includes improving the speed of information analysis, obtaining more accurate and reliable data outputs, and allowing employees to perform high-level tasks.
• Using traditional methods (like RPA) for fraud detection requires creating manual rules.
• 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).

Instead of seeing the results of numerous disparate experiments across the enterprise, these leaders will now see clear transformation opportunities—and be justifiably excited to build the capabilities, systems, and approaches necessary to reach automation at scale. McKinsey sees a second wave of automation and AI emerging in the next few years, in which machines will do up to 10 to 25 percent of work across bank functions, increasing capacity and freeing employees to focus on higher-value automation in banking industry tasks and projects. To capture this opportunity, banks must take a strategic, rather than tactical, approach. In some cases, they will need to design new processes that are optimized for automated/AI work, rather than for people, and couple specialized domain expertise from vendors with in-house capabilities to automate and bolt in a new way of working. Despite some early setbacks in the application of robotics and artificial intelligence (AI) to bank processes, the future is bright.

Investing in banking automation

Banks, lenders, and other financial institutions may collaborate with different industries to expand the scope of their products and services. Conceptual analysis refers to the analysis of data based on word frequency and word occurrence, whereas relational analysis refers to the analysis that draws connections between concepts and captures the co-occurrences between words (Leximancer, 2019). 3 shows, the most prominent concept is “customer,” which provides additional credence to our customer theme. For the concept “customer,” some of the key concept associations include satisfaction (324 co-occurrences and 64% word association), service (185 co-occurrences and 43% word association), and marketing (86 co-occurrences and 42% word association).

Convolutional natural network is a multilayered neural network with an architecture designed to extract increasingly complex features of the data at each layer to determine output; see “An executive’s guide to AI,” QuantumBlack, AI by McKinsey, 2020. But scaling gen AI will demand more than learning new terminology—management teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality. In this report, Business Insider Intelligence identifies the most meaningful AI and machine learning applications across banks’ front and middle offices. We also discuss the winning AI strategies used by fintechs and legacy financial institutions so far, as well as provide recommendations for how banks can best approach an AI-enabled digital transformation. Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. The future of financial services is about offering real-time resolution to customer needs, redefining banking workplaces, and re-energizing customer experiences.

What is robotic process automation (RPA) in banking?

Add natural language capabilities like that offered by the expert.ai Platform to the equation and you can review legal and compliance documents in record time. Most importantly, you gain critical insight into the most relevant language and its implications in each document. According to Business Insider Intelligence’s AI in Banking report, financial institutions’ implementation of AI could account for $416 billion of the total potential AI-enabled cost cuts across industries, which are estimated to be $447 billion by 2030.

Banks can leverage the massive quantities of data at their disposal by combining data science, banking automation, and marketing to bring an algorithmic approach to marketing analysis. Data science helps banks get return analysis on those test campaigns that much faster, which shortens test cycles, enables them to segment their audiences at a more granular level, and makes marketing campaigns more accurate in their targeting. IA consists mainly of the deployment of robotic process automation and artificial intelligence solutions.

Full banking automation coverage

For its unattended intelligent automation, the bank deployed a learning automation platform. The platform helped it seamlessly integrate its own systems with third-party systems for time and cost savings. The bank’s teams used the platform’s cognitive automation technology to perform several tasks quickly and effortlessly, including halving the time it used to take to screen clients as a part of the bank’s know-your-customer process. With these six building blocks in place, banks can evaluate the potential value in each business and function, from capital markets and retail banking to finance, HR, and operations. When large enough, these opportunities can quickly become beacons for the full automation program, helping persuade multiple stakeholders and senior management of the value at stake.