Robotic Process Automation RPA in Banking: Examples, Use Cases

But how did the introduction and growth of ATMs affect the job of tellers? Despite an increase of roughly 300,000 ATMs implemented since 1990, the number of tellers employed by banks did not fall. According to the research by James Bessen of the Boston University School of Law, there are two reasons for this counterintuitive result. To further demystify the new technology, two or three high-profile, high-impact value-generating lighthouses within priority domains can build consensus regarding the value of gen AI.

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 RPA and other automation software improve business processes, job roles will change.

By processing both e-commerce and consumer finance transactions (including peer-to-peer payments, car loans, credit cards, and so on), a CMS can begin to predict what customers want even before those desires become conscious. Banks can also sharply reduce their own risks because they will know each customer’s creditworthiness better than most credit rating agencies do. It applies AI and big data to reduce Kaspi’s risks on many kinds of loans, including small-business loans and short-term consumer loans for marketplace customers. Within its fintech area, the most widely used service is to buy now and pay later.

In other ways, a gen AI scale-up is like nothing most leaders have ever seen. For example, banks have conventionally required staff to check KYC documents manually. However, banking automation helps automatically scan and store KYC documents without manual intervention. Hyperautomation is a digital transformation strategy that involves automating as many business processes as possible while digitally augmenting the processes that require human input.

Any bank that successfully transitions into a CMS can multiply revenues by ten, with higher profit margins for higher-value services. Tech advances have eliminated size as an advantage in providing excellent services, winning customer loyalty, aggregating and analyzing data, and building networks of capital. Regulation, technology, geopolitical shifts, and unforeseen innovations could radically alter the way that the industry develops. But we do believe that the banks that successfully manage the coming transition will use tech and data to embed themselves deeper into customers’ lives with real-time services that were unimaginable just a few short years ago.

They can also explain to employees in practical terms how gen AI will enhance their jobs. The cost of paper used for these statements can translate to a significant amount. Automation and digitization can eliminate the need to spend paper and store physical documents. The competition in banking will become fiercer over the next few years as the regulations become more accommodating of innovative fintech firms and open banking is introduced. For end-to-end automation, each process must relay the output to another system so the following process can use it as input. AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns.

Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. 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.

Successful gen AI scale-up—in seven dimensions

Its instant-messaging apps WeChat and QQ have about 1.3 billion and 570 million monthly active users, respectively. Intelligent automation can change how work gets done, but organizations need to balance operational efficiencies with evolutionary workforce changes. API management solutions help create, manage, secure, socialize, and monetize web application programming interfaces or APIs.

Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension. Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing.

They manage vendors involved in the process, oversee infrastructure investments, and liaison between employees, departments, and management. Banking automation has become one of the most accessible and affordable ways to simplify backend processes such as document processing. These automation solutions streamline time-consuming tasks and integrate with downstream IT systems to maximize operational efficiency. Additionally, banking automation provides financial institutions with more control and a more thorough, comprehensive analysis of their data to identify new opportunities for efficiency. Too often, banking leaders call for new operating models to support new technologies.

• Banks are already using generative AI for financial reporting analysis & insight generation.
• It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions.
• Learn how SMTB is bringing a new perspective and approach to operations with automation at the center.
• But success will come to only those banks willing to move beyond their traditional operating models.

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. For challengers looking to exploit a tech edge as a way of entering banking, the first step is to analyze which arenas offer maximum advantage based on that edge and which platform-based business model makes most sense.

Challenges in Banking and Solving Them Using RPA

Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities. In our experience, this transition is a work in progress for most banks, and operating models are still evolving. The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential.

Low-code and no-code refer to workflow software requiring minimal (low code) or no coding that allows nontechnical line-of-business experts to automate processes by using visual designers or natural language processing. You can foun additiona information about ai customer service and artificial intelligence and NLP. Green or sustainable IT puts a focus on creating and operating more efficient, environmentally friendly data centers. Enterprises can use automation in resourcing actions to proactively ensure systems performance with the most efficient use of compute, storage, and network resources. This helps organizations avoid wasted spend and wasted energy, which typically occurs in overprovisioned environments. For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative.

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. Companies in the banking and financial industries often create a team of experienced individuals familiar with the entire organization to manage digital acceleration. This team, sometimes referred to as a Center of Excellence (COE), looks for intelligent automation opportunities and new ways to transform business processes.

Data quality—always important—becomes even more crucial in the context of gen AI. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues. Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data. Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations.

Process automation helps bring greater uniformity and transparency to business and IT processes. Process automation can increase business productivity and efficiency, help deliver new insights into business and IT challenges, and surface solutions by using rules-based decisioning. Process mining, workflow automation, business process management (BPM), and robotic process automation (RPA) are examples of process automation. Just as the smartphone catalyzed an entire ecosystem of businesses and business models, gen AI is making relevant the full range of advanced analytics capabilities and applications. 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.

Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts. Robotic process automation (RPA) has been adopted across various industries to ease employee workloads while cutting costs – and banking is no exception. From taking over monotonous data-entry, automation banking industry to answering simple customer service queries, RPA has been able to save financial workers from spending time on repetitive, labor-intensive tasks. In another example, the Australia and New Zealand Banking Group deployed robotic process automation (RPA) at scale and is now seeing annual cost savings of over 30 percent in certain functions.

Capturing the full value of generative AI in banking

And while the advance of digital currencies is unstoppable, its regulatory future is similarly unclear. A decade from now, cryptocurrencies, easily exchanged via blockchain and other tech, might be well established as mainstream alternatives to central-bank currencies. Digital currencies might then be far more convenient for all kinds of transactions and deposits, potentially removing a main function and competitive advantage of banks. On the other hand, there might well be a regulatory backlash against cryptocurrencies, with developed nations cracking down on its misuse for illegal activities or financial warfare. The kind of transformations and competition that we have examined in everyday banking are sure to take place in each of the other four arenas.

The good news is that there’s still enough time for most financial institutions to transform their business models. Additionally, the capital markets are likely to be very supportive in valuing those transformations over the next five to ten years. Chat GPT MyLifeAssistant and its parent have strong incentives not to take advantage of their customers. The more partnerships and personalized services that they offer to both individuals and businesses, the more that everyone involved benefits.

Kaspi’s fintech portfolio grew 42 percent in 2021, and the related average customer savings rose 34 percent. Incumbent banks face two sets of objectives, which on first glance appear to be at odds. On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. So, instead of asking whether automation will completely replace jobs not, you should be seeking to discover what tasks should be done by machines, and what complementary skills are better done by humans (at least for now). Then determine what the augmented banking experience is for the future of banking.

At this very early stage of the gen AI journey, financial institutions that have centralized their operating models appear to be ahead. About 70 percent of banks and other institutions with highly centralized gen AI operating models have progressed to putting gen AI use cases into production,2Live use cases at minimal-viable-product stage or beyond. Compared with only about 30 percent of those with a fully decentralized approach. Centralized steering allows enterprises to focus resources on a handful of use cases, rapidly moving through initial experimentation to tackle the harder challenges of putting use cases into production and scaling them.

The survey found that cyber controls are the top priority for boosting operation resilience according to 65% of Chief Risk Officers (CROs) who responded to the survey. For example, Credigy, a multinational financial organization, has an extensive due diligence process for consumer loans. Banks are already using generative AI for financial reporting analysis & insight generation.

By playing the long game and reimagining the new human-machine interface, banks can prepare for a world where people and machines won’t compete, but will complement each other and expand the net benefits. Navigating this journey will be neither easy nor straightforward, but it is the only path forward to an improved future in consumer experience and business operations. 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.

People crave tailored advice and trust-based relationships that make them feel understood, even when dealing with virtual advisers online. Both individual and organizational customers now seek a long list of attributes from their financial-service providers. Surveys show that these desires include high levels of personalization, zero friction, and a commitment to social and environmental impact.3“The value of getting personalization right—or wrong—is multiplying,” McKinsey, November 12, 2021. As of September 2022, there were at least 274 fintech companies with a unicorn valuation of more than $1 billion, up from just 25 in 2017. While traditional banks have been convenient one-stop shops, many haven’t evolved their products in a way that matches the tech-driven pace of change in other industries.

• These additional services include travel insurance, foreign cash orders, prepaid credit cards, gold and silver purchases, and global money transfers.
• Similarly, transformative technology can create turf wars among even the best-intentioned executives.
• Employees will inevitably require additional training, and some will need to be redeployed elsewhere.
• Advances in robotics, artificial intelligence, and quantum computing make machines so smart and efficient that they can replace humans in many roles now and in the next few years.

Business owners define goals unilaterally, and alignment with the enterprise’s technology and analytics strategy (where it exists) is often weak or inadequate. 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. The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions. While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent.

Such automation contributes to increased productivity and an optimal customer experience. AIOps and AI assistants are other examples of intelligent automation in practice. Organizations use automation to increase productivity and profitability, improve customer service and satisfaction, reduce costs and operational errors, adhere to compliance standards, optimize operational efficiency and more. Automation is a key component of digital transformation, and is invaluable in helping businesses scale. Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent. They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams.

As a result, its non-performing loan (NPL) ratio was just 1.2 percent in 2021, significantly lower than the average NPL level for unsecured retail loans. Kaspi Pay, its app, enables customers to pay for household needs, make online and in-store purchases, and manage peer-to-peer payments. It bolsters Kaspi’s profit margins by removing the intermediaries that previously handled payments for Kaspi.

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Banking leaders appear to be on board, even with the possible complications. 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. Over the past decade, the transition to digital systems has helped speed up and minimize repetitive tasks.

Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage. Despite some early setbacks in the application of robotics and artificial intelligence (AI) to bank processes, the future is bright. The technology is rapidly maturing, and domain expertise is developing among both banks and vendors—many of which are moving away from the one-solution-fits-all “hammer and nail” approach toward more specialized solutions. There are clear success stories (see sidebar “Automation in financial services”), but many banks face sobering challenges.

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. During the pandemic, Swiss banks like UBS used credit robots to https://chat.openai.com/ support the credit processing staff in approving requests. The support from robots helped UBS process over 24,000 applications in 24-hour operating mode. Reskilling employees allows them to use automation technologies effectively, making their job easier.

First, economic forces and technology have ended the run of the universal-bank model, and investors already are recognizing radical specialization to be greater than the traditional one-stop shop. By contrast, the future model relies on breaking up into four specialized platforms we will describe. The sector’s price-to-book value has fallen to less than one-third the value of other industries. That gap is less the result of current profitability and more about uncertain profit growth in the future. While banks have pushed for great improvements recently, margins are shrinking—down more than 25 percent in the past 15 years and expected to fall to 30 percent, another 20 percent decrease, in the next decade. Learn more about tools to help businesses automate much of their daily processes, to save time and drive new insights through trusted, safe, and explainable AI systems.

Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success. Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. Banks introduced ATMs in the 1960s and electronic, card-based payments in the ’70s. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. Download this e-book to learn how customer experience and contact center leaders in banking are using Al-powered automation. Well, automation reduces businesses’ operating costs to free up resources to invest elsewhere.

Most importantly, the change management process must be transparent and pragmatic. Gen AI, along with its boost to productivity, also presents new risks (see sidebar “A unique set of risks”). Risk management for gen AI remains in the early stages for financial institutions—we have seen little consistency in how most are approaching the issue. Sooner rather than later, however, banks will need to redesign their risk- and model-governance frameworks and develop new sets of controls. 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. Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution.

However, dealing with the complexities of having multiple systems access customer information provided new challenges. Our team deploys technologies like RPA, AI, and ML to automate your processes. We integrate these systems (and your existing systems) to allow frictionless data exchange. Using traditional methods (like RPA) for fraud detection requires creating manual rules.

Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank.

Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized. We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized. 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.

Some have installed hundreds of bots—software programs that automate repeated tasks—with very little to show in terms of efficiency and effectiveness. Some have launched numerous tactical pilots without a long-range plan, resulting in confusion and challenges in scaling. Other banks have trained developers but have been unable to move solutions into production.

For example, a sales rep might want to grow by exploring new sales techniques and planning campaigns. They can focus on these tasks once you automate processes like preparing quotes and sales reports. Automation can help improve employee satisfaction levels by allowing them to focus on their core duties. Implementing automation allows you to operate legacy and new systems more resiliently by automating across your system infrastructure. They’ll demand better service, 24×7 availability, and faster response times.