The Role of Automation in Banking Operations

Robotic Process Automation in Banking Benefits & Use Cases

XYZ Bank, a large multinational banking institution, faced numerous challenges in their loan origination process. The manual processing of loan applications, data verification, and eligibility assessments resulted in high operational costs, lengthy processing times, and a higher risk of errors. 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.

They also need to define a target IT architecture (both applications and infrastructure) that uses a variety of integration solutions while maintaining a system’s integrity. The team focused on simplifying the process steps and procedural requirements at each stage—streamlining the information required from the customer and eliminating redundant verification steps—to reduce the complexity of the IT solution. This high degree of manual processing is costly and slow, and it can lead to inconsistent results and a high error rate.

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. Based on our work with major financial institutions around the world and from McKinsey Global Institute research on automation and the future of work, we see six defining characteristics of future banking operations. 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.

• Instead of waiting on hold or being transferred between different departments, they can use the capability to simply chat with an AI-powered chatbot that understands their query instantly and provides relevant information and solutions.
• AI improves customer experiences in banking by enabling personalized interactions, quick query resolution, and tailored financial recommendations.
• Imagine a scenario where a bank needs to assess a loan applicant’s creditworthiness.
• This level of engagement enhances customer satisfaction and fosters loyalty.
• 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.
• One of the key benefits of RPA is its ability to work across different systems and applications, regardless of their underlying technology.

You want to offer faster service but must also complete due diligence processes to stay compliant. During the pandemic, Swiss banks like UBS used credit robots to support the credit processing staff in approving requests. The support from robots helped UBS process over 24,000 applications in 24-hour operating mode. You can foun additiona information about ai customer service and artificial intelligence and NLP. A system can relay output to another system through an API, enabling end-to-end process automation.

Key players in AI-driven automation in banking include established technology companies like IBM, Microsoft, and Google, as well as specialized fintech firms such as Ant Financial and Infosys. Many traditional banks also collaborate with or invest in emerging AI startups to incorporate advanced automation into their operations. The future of AI-driven automation in banking holds even greater potential.

RPA is a cutting-edge technology that leverages software robots to automate repetitive tasks, improve operational efficiency, and reduce costs. These robots mimic human actions and interact with existing systems to perform various tasks, such as data entry, document processing, account reconciliation, and regulatory compliance. Moreover, AI-powered process automation tools are not limited to credit assessment. They can also help in predicting customer churn, optimizing investment portfolios, detecting fraudulent activities, increasing business ROI (Return on Investment), and even personalizing customer experiences. With AI’s powerful capabilities, banks can enhance operational efficiency, minimize risk, improve customer satisfaction, and ultimately gain long-term competitive advantages.

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. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities.

The transformative power of automation in banking

It’s vital to distinguish “tasks” from “jobs.” Jobs contain a group of tasks needing consistent fulfillment—some of which may be more routine (and can potentially be automated), while some require more abstract skills. There is a balance to Chat GPT be struck between the speed and accuracy of computers and the creativity and personalization of human interaction. Download this e-book to learn how customer experience and contact center leaders in banking are using Al-powered automation.

By leveraging AI-powered solutions, banking IT departments can streamline processes, optimize resource allocation, and enhance customer experiences through targeted marketing campaigns. Business analysts and subject matter experts collaborate with managers to identify automation initiatives and deploy automation platforms that accelerate productivity and reduce manual intervention. With the aid of automation software, banks can create, deploy, and manage automation processes efficiently, empowering managers to focus on strategic decision-making while automation builders handle routine tasks. This accelerated automation not only enhances operational efficiency but also ensures compliance and risk mitigation. Ultimately, AI-driven automation facilitates a seamless workflow in banking, empowering institutions to adapt to evolving market demands and deliver exceptional services to their clients. One such innovation that is revolutionizing the banking sector is Robotic Process Automation (RPA).

Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. Banks deal with massive amounts of data on a daily basis – from customer transactions to market trends and regulatory requirements. Extracting valuable insights from this sea of information can be overwhelming without the aid of AI-powered process automation tools. AI algorithms in banking have significantly curtailed fraudulent activities, boasting a remarkable 65% reduction in such incidents. Furthermore, banks that leverage AI driven automation report a substantial 30% increase in operational efficiency, streamlining processes across various facets of their operations.

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An automated system can perform various other operations as well, such as extracting data from internal or external systems and fact-checking the reports. For instance, imagine sending a chat message to your bank’s customer support and receiving an immediate response that adequately addresses your query without any delays or waiting time. It is easy to get buy-in from the business units and functions, and specialized resources can produce relevant insights quickly, with better integration within the unit or function. This archetype has more integration between the business units and the gen AI team, reducing friction and easing support for enterprise-wide use of the technology. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions. In the next sections, we will explore the specific benefits of RPA in banking, along with common use cases and real-world examples of how banks are implementing this transformative technology.

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 capabilities from technology vendors and partners, including AI specialists. 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. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies.

In today’s banks, the value of automation might be the only thing that isn’t transitory. Once the account is frozen, RPA can automatically complete the steps in your fraud investigation process. The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack. Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals. Get started with your complimentary trial today and delve into our platform without any obligations. Explore our wide range of customized, consumption driven analytical solutions services built across the analytical maturity levels.

By leveraging AI to enhance customer interaction, banks can improve satisfaction levels, reduce response times, and enable more efficient and personalized services. The integration of AI chatbots and predictive analytics creates a seamless experience for customers, making their banking journey smoother and more enjoyable. By speeding up processes through AI-driven automation, banks can improve operational efficiency, reduce turnaround times, and provide customers with faster and more seamless experiences. One of the significant advantages of AI-driven data analytics based hyper automation in banking is its ability to accelerate processes across the board. Traditionally, manual tasks such as data entry, document verification, and transaction processing took considerable time and effort.

Despite these challenges, the future of AI driven automation in banking holds immense potential for improving operational efficiency, reducing costs, and delivering seamless customer experiences. AI-driven automation banking is revolutionizing the banking industry by streamlining operations, enhancing customer experiences, and improving operational efficiency. It enables tasks such as document processing, customer communication handling, sentiment analysis, and more. This ai technology empowers banks to provide personalized solutions, faster response times, and gain valuable insights into customer perception, ultimately driving automation exceptional services and competitiveness. AI-driven automation is revolutionizing workflow efficiency within the banking sector by seamlessly integrating virtual assistants, low-code and no-code automation tools, and cutting-edge automation technologies.

More than 90 percent of the institutions represented at a recent McKinsey forum on gen AI in banking reported having set up a centralized gen AI function to some degree, in a bid to effectively allocate resources and manage operational risk. In recent years, banks have embraced RPA with open arms to address operational challenges, enhance productivity, and foster a seamless digital transformation. By utilizing RPA, banks can achieve greater accuracy, faster throughput times, improved compliance, cost savings, and ultimately, an enhanced customer experience. Being future-ready reflects an organization’s ability to scale eight characteristics of operating model maturity. Our research suggests that technology challenges are impeding banks from achieving operational transformation.

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. 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.

You can get more business from high-value individual accounts and accounts of large companies that expect banks to have a top-notch security framework. Automation in banking has become important, especially because of the pandemic. The banking sector needed to improve the way it provides services by using contactless methods.

What’s more, their revenue on assets has not only been greater but has shrunk less than that of their less-digitized peers. The cost improvement, combined with their revenue advantage, means that they have managed to increase operating income per dollar of asset—jumping from 1.22 in 2011 to 1.47 in 2019. Banks have always been committed to improving the efficiency of their operations, and for the most part, their progress has been steady. Book a discovery call to learn more about how automation can drive efficiency and gains at your bank. AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns. Cybersecurity is expensive but is also the #1 risk for global banks according to EY.

June 20, 2019Today, deep within the headquarters and regional offices of banks, people do jobs that no customer ever sees but without which a bank could not function. Thousands of people handle the closing and fulfillment of loans, the processing of payments, and the resolution of customer disputes. They figure out when exceptions can be made for customer approvals and help the bank comply with money laundering rules, to name but a few.

Embracing factory automation and edge computing enables seamless processes, paving the way for a streamlined banking experience. As we stand on the cusp of the Fourth Industrial Revolution, technological prowess is essential for staying ahead. Leveraging emerging technologies https://chat.openai.com/ such as edge AI and ChatGPT not only enhances efficiency but also drives innovation. In this era of rapid change, the integration of AI-driven automation represents a pivotal shift, empowering banks to navigate complexities with agility and precision.

It involves the use of advanced algorithms and machine learning to streamline operations, enhance decision-making, and provide personalized services to customers. The integration of AI-driven financial data analytics solutions enables financial institutions to automate tasks that were previously time-consuming and error-prone, allowing employees to focus on more strategic and value-adding activities. From document processing to customer communication handling, AI tools bring unprecedented speed and accuracy to various workflows. In this article, we explored the concept of RPA and its numerous benefits in banking. We discussed how RPA enhances operational efficiency, reduces costs, ensures accuracy and compliance, and fosters scalability and flexibility.

According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry. The banking industry has particularly embraced low-code and no-code technologies such as Robotic Process Automation (RPA) and document AI (Artificial Intelligence). These technologies require little investment, are adopted with minimal disruption, require no human intervention once deployed, and are beneficial throughout the organization from the C-suite to customer service. And with technology fundamentally changing the financial and consumer ecosystems, there has never been a better time to take the next step in digital acceleration. When banks, credit unions, and other financial institutions use automation to enhance core business processes, it’s referred to as banking automation. Looking ahead, the role of automation in banking is set to expand even further.

Automation speeds up the verification of digital forms and documents provided by customers. A smooth, error-free procedure helps ensure that clients get their funds on time. You can make automation solutions even more intelligent by using RPA capabilities with technologies like AI, machine learning (ML), and natural language processing (NLP).

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. First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8Clayton M. 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.

You can also program RPA systems to perform continuous compliance checks, ensuring that your bank adheres to ever-evolving financial regulations. Additionally, these systems can generate comprehensive reports, streamlining the compliance process and reducing automation in banking operations the risk of regulatory penalties. Through Natural Language Processing (NLP) and AI-driven bots, RPA enables personalized customer interactions. Chatbots can provide tailored recommendations, answer inquiries promptly, and resolve customer issues efficiently.

Traditional methods of customer interaction often involve time-consuming processes like waiting in line or navigating complex IVR systems. However, AI driven automation has the potential to transform this landscape by enhancing customer interaction and providing personalized services. Leveraging tools from Numurus LLC and Ocean Aero, alongside platforms like MuleSoft and ABB’s Ability™, banks harness the power of digital twins and virtual factories for predictive data analytics and resource utilization. This synergy between AI and human ingenuity enables banks to optimize energy efficiency and drive operational excellence, revolutionizing the banking landscape while ensuring regulatory compliance and customer satisfaction. In today’s dynamic banking landscape, the power of AI-driven automation is paramount. With a relentless focus on accessibility, customization, and scalability, financial institutions can harness this technology to revolutionize their operations.

Download this white paper and discover how to create a roadmap to deliver value at scale across your bank. The cost of maintaining compliance can total up to $10,000 on average for large firms according to the Competitive Enterprise Institute. As the world moves online, you’ll need to re-engineer your Customer Experience to make it friction free, faster and more efficient. The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack.

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. In the target state, the bank could end up with three archetypes of platform teams. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking.

On the other hand, intelligent document processing (IDP) helps streamline document management. Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms.

The future looks promising for RPA in banking, as it continues to evolve with advancements in AI, machine learning, and process optimization. Robotic Process Automation (RPA) is a technology that utilizes software robots or “bots” to automate repetitive and rule-based tasks within an organization. These bots are capable of mimicking human interactions with computer systems, applications, and databases, enabling them to perform tasks that were previously done manually. Read our 7 proven banking automation strategies for financial service organizations. 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.

A European bank used automation, analytics and top talent to cut operating costs by 20-30%—freeing up resources to reinvest. Automation enables banks to respond quickly to changes in the market such as new regulations and new competition. The ability to make changes at speed also facilitates faster delivery of innovative new products and services that give them an edge over their competitors.

Helps transform banks and non-banks across a broad range of topics to sustainably drive revenue growth and to enhance efficiency. Since their modest beginnings 50 years ago, ATMs have evolved from simple cash dispensing machines as consumer needs dictated. From “drive-up” ATMs in the 1980s to “talking” ATMs with voice instructions ’90s, now Video Teller ATMs have become more prevalent. On the back of further innovations and advancements such as integrations, mobile “cardless” access, and larger tablet interfaces, the next stage in the evolution of the ATMs may be “robo-banks” that can do what tellers do. With UiPath, SMTB built over 500 workflow automations to streamline operations across the enterprise. Learn how SMTB is bringing a new perspective and approach to operations with automation at the center.

With AI technologies like optical character recognition (OCR) and natural language processing (NLP), these processes can now be executed rapidly and accurately. Imagine a driven banking automation experience that anticipates your needs, understands your preferences, and helps you manage your finances proactively through an elegant use case of digital transformation. Welcome to the future of banking where Artificial Intelligence (AI) and automation are transforming businesses approaches by moving beyond mere digitization towards intelligent interactions for their clients. According to Quantzig’s Experts, AI-driven automated has increased customer satisfaction in banking by 42% because over 80% of banking transactions are now handled through AI driven banking automation and enhanced security. The nascent nature of gen AI has led financial-services companies to rethink their operating models to address the technology’s rapidly evolving capabilities, uncharted risks, and far-reaching organizational implications.

Customer experience

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. 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. Well, automation reduces businesses’ operating costs to free up resources to invest elsewhere. AI and RPA-powered automation can help make decisions about timing marketing campaigns, redesigning workflows, and tailor-making products for your target audience.

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.

• Banks and the financial services industry can now maintain large databases with varying structures, data models, and sources.
• From digital forms to credit analysis, automation shortens the months-long processing time.
• Accenture surveyed bank executives worldwide to understand how they view their journey to operations maturity.

As some banks experiment with this rapid-automation approach, and the impact of initial pilots resounds throughout the organization, IT and operations teams will feel pressured to integrate all end-to-end and back-office processes. All too often, however, efforts to scale up these initiatives are short lived. IT architecture teams, concerned that they will not master unfamiliar integration solutions, or that additional efforts will make the IT landscape even more complex, may react warily. Meanwhile, operations and business personnel push to automate everything everywhere as soon as possible, without proper planning and evaluation.

The automation also led to a substantial reduction in errors, as the bots executed tasks with high accuracy and adherence to the bank’s defined rules. RPA is transforming the banking industry by streamlining operations, reducing costs, improving accuracy, enhancing customer experience, and enabling banks to stay competitive in a rapidly evolving landscape. In the next section, we will explore some common use cases of RPA in banking. Hyperautomation can help financial institutions deal with these pressures by reducing costs, increasing productivity, enabling a better customer experience, and ensuring regulatory compliance.

Feel free to check our article on intelligent automation strategy for more. The next step in enterprise automation is hyperautomation, one of the top technology trends of 2023. Accenture surveyed bank executives worldwide to understand how they view their journey to operations maturity. Digitally-focused banks have benefited from market valuations that, on average, were 18% higher than less digitized peers in 2019, and 27% higher in 2020.

Kinective serves more than 2,500 banks and credit unions, giving them the power to accelerate innovation and deliver better banking to the communities they serve. AI improves customer experiences in banking by enabling personalized interactions, quick query resolution, and tailored financial recommendations. Through technologies like natural language processing and AI-powered chatbots, customers can receive instant and accurate responses, leading to increased satisfaction and engagement. The future of AI-driven automation also holds great promise in enhancing customer experiences. Virtual assistants powered by natural language processing can interact with customers through voice or text, providing instant responses to inquiries about account balances, transaction history, or assistance with financial planning. These virtual assistants can offer personalized recommendations based on individual spending habits and help customers manage their finances more effectively.

JPMorgan, for example, is using bots to respond to internal IT requests, including resetting employee passwords. The bots are expected to handle 1.7 million IT access requests at the bank this year, doing the work of 40 full-time employees. And at Fukoku Mutual Life Insurance, a Japanese insurance company, IBM’s Watson Explorer will reportedly do the work of 34 insurance claim workers beginning January 2017. Your employees will have more time to focus on more strategic tasks by automating the mundane ones. Robotic Process Automation in banking can be used to automate a myriad of processes, ensuring accuracy and reducing time.