Bank Automation- How Automation is Changing the Banking Industry

Robotic Process Automation RPA in Banking and Finance Industry

automation banking industry

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. Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit). We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized.

It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. EPAM Startups & SMBs is backed by EPAM’s Intelligent Automation Practice implementing RPA and cognitive automation solutions to aid in digital banking transformation. For example, manual invoice processing may result in operational lags in accounts payable. Financial institutions use RPA to automate invoice processing, including verifying, receiving, and paying invoices.

A number of financial services institutions are already generating value from automation. 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. 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.

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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. Handling loans and credits got much smoother with some help from banking automation and AI chatbots. AI chatbots can dive into a centralized data pool to quickly fetch the information needed for loan and credit processing.

When deciding which banking processes can be automated, it might turn out that the same process can be understood and executed differently depending on who you ask. In addition to real-time support, modern customers also demand fast service. For example, customers should be able to open a bank account fast once they submit the documents. You can achieve this by automating document processing and KYC verification.

A small error made by the customer or the bank could slow this process down even more and add a lot of complications. By implementing RPA, banks can streamline and accelerate the process, addressing any bottlenecks if it gets interrupted and deliver a fantastic customer experience. 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 remaining institutions, approximately 20 percent, fall under the highly decentralized archetype.

In the right hands, automation technology can be the most affordable but beneficial investment you ever make. Banks and the financial services industry can now maintain large databases with varying structures, data models, and sources. As a result, they’re better able to identify investment opportunities, spot poor investments earlier, and match investments to specific clients much more quickly than ever before. Another common obstacle to RPA adoption is the generally slow pace of technological development among enterprises in the banking industry. While it’s one of the most data-driven sectors, it’s still lagging behind in digital transformation.

​​Banking automation, spearheaded by AI and AI chatbots, has emerged as a game-changer in personalizing customer interactions, optimizing operational efficiency, and fostering a more inclusive and global banking environment. From simplifying customer onboarding to enhancing fraud detection and improving employee experiences, the impact of these technologies is profound and multifaceted. 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. They manage vendors involved in the process, oversee infrastructure investments, and liaison between employees, departments, and management. End-to-end service automation connects people and processes, leading to on-demand, dynamic integration.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months.

Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals. It implemented RPA in its policy issuance process, and this resulted in significant time savings and the elimination of human errors. Additionally, compliance officers spend almost 15% of their time tracking changes in regulatory requirements. So, let’s dive into the AI chatbots and learn why these chatbots are the best automation tools in banking. Automate repetitive tasks and enable your teams to focus on what’s essential for your business. Organizational misalignment and process standardization were included by Deloitte in the Robotics and Cognitive Automation report among the top challenges to implementing RPA in banking.

Creating reports for banks can require highly tedious processes like copying data from computer systems and Excel. In addition, before moving to the next period, banks must procure accurate financial statements at the end of each month. RPA in financial services reduces this process to just a few minutes, which otherwise usually takes weeks. RPA software can be seamlessly integrated within the bank’s existing tech stack, which allows the bank to pull data from various systems to inform decision-making, define processes, and identify opportunities for improvement. So, let’s break down why this shift towards automation is happening and how AI-powered automation and chatbots are helping banks navigate complex tasks, get a grip on human language and even recognise emotions. Timesheets, vacation requests, training, new employee onboarding, and many HR processes are now commonly automated with banking scripts, algorithms, and applications.

Today Self-serve support in banking doesn’t have to mean endlessly waiting for the right IVR options in the myriad of complicated paths set on them. AI-powered automation is setting a new standard for customer empowerment, providing a seamless and intuitive way to manage their banking needs independently. AI chatbots offer real-time, personalized assistance for various queries, from checking account balances to navigating complex transactions. This shift enhances customer autonomy and convenience and significantly streamlines banking operations, making it more efficient and user-friendly for everyone. You can foun additiona information about ai customer service and artificial intelligence and NLP. It’s the secret sauce that turns casual browsers into dedicated customers and those customers into enthusiastic brand advocates. They’re not just there to answer your queries; they’re there to understand you.

For example, Article 22 of the GDPR states that data subjects have the right not to be subjected to a decision that was based on automated processing. It’s hard to tell whether assessing a person’s credit rating using algorithmic card scoring really fits the standard laid out in the article. The majority of banking platforms and core systems running today were developed a long time ago. For example, 43% of banks in the United States use COBOL, a programming language invented in the 1950s. It doesn’t matter what kind of development technology is used at your bank. Banks are using RPA across a broad range of departments, starting from operations and sales to finance and human resources.

Automation and robotics to improve processes in banking

These are mainly large institutions whose business units can muster sufficient resources for an autonomous gen AI approach. 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. Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction.

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Change management and mindset shift are two critical goals that need to be addressed before embracing any technology – and RPA is no exception. But in order for this strategy to be properly executed, change management is critical. This is a part of a holistic approach to building acceptance of this technology. Moreover, RPA reduces the time required for customer verification, mapping customers with details from different sources, and customer onboarding. This reduces the waiting time and increases the efficiency of redressal to help banks improve their customer relationships.

As a part of the fourth industrial revolution, it seems inevitable that RPAs will inevitably revolutionize the financial industry. Banks are faced with the challenge of using this emerging technology effectively. They will need to redefine the relationship between employee and systems and anticipate how best to use the new freedom RPA affords its people. They’re harnessing these tech advancements to streamline operations and redefine banking efficiency. It’s a significant shift towards managing banking operations with peak performance and minimal fuss. Once you’ve successfully implemented a new automation service, it’s essential to evaluate the entire implementation.

Customers want to get more done in less time and benefit from interactions with their financial institutions. Faster front-end consumer applications such as online banking services and AI-assisted budgeting tools have met these needs nicely. Banking automation behind the scenes has improved anti-money laundering efforts while freeing staff to spend more time attracting new business. Leveraging process mining and digital twins can help banks to gain process intelligence and identify back-office processes to automate. AI and NLP-enabled intelligent bots can automate these back-office processes involving unstructured data and legacy systems with minimal human intervention. A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture.

Automation in banking is the behind-the-scenes superhero for the financial world. It’s about leveraging innovative software and cutting-edge tech to make banking operations smoother and faster. Imagine cutting down on all that manual work – no more endless data entry, account opening marathons, or transaction processing headaches. It gives the green light to efficiency, and accuracy, and saves some serious cash.

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. 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.

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. Automation is the focus of intense interest in the global banking industry. Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities.

Commercial banking

For example, a broker statement can be easily automated to generate a detailed report that allows identifying anomalies. With RPA, banks can automate up to 90% of compliance-related tasks, saving a lot of time and money for their teams. Robotic Process Automation (also called RPA) is a type of business process automation technology based on software robots, also known as bots or digital workers. The bots are configured to integrate with human activities and execute business processes directly in the user interface. Robot Process Automation is a type of enterprise automation extensively used by banks and financial services organizations today.

And it is also a great example of how banking has always been an innovative industry. There is also a high error margin if a single record is incorrectly entered, and it will affect payment. Because of this, RPA bots can automate the tracking, sending, and creation of invoice payments.

Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications. As a virtual workforce, this software application has proven valuable to organizations looking to automate repetitive, low-added-value work. The combination of RPA and Artificial Intelligence (AI) is called CRPA (Cognitive Robotic Process Automation) or IPA (Intelligent Process Automation) and has led to the next generation of RPA bots. It has been transforming the banking industry by making the core financial operations exponentially more efficient and allowing banks to tailor services to customers while at the same time improving safety and security.

Pick the cases that offer the greatest potential and don’t end up incurring too many costs. According to a 2020 PwC report, 81% of banking executives are overwhelmed by the current technological change that requires constant restructuring of business processes. One of the innovations that are impacting the banking industry is enterprise automation forming the core of many digital transformation strategies today.

  • This evolution is not just about efficiency and cost savings; it’s about redefining the banking experience for customers and employees alike.
  • UBS implemented RPA in order to process the unprecedented spike in the number of loan requests that all investment banks faced after the Swiss Federal Council let commercial companies apply for loans with zero interest during the pandemic.
  • 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.
  • Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018,
  • What’s truly remarkable is how these chatbots adapt to various linguistic nuances, ensuring that every customer, irrespective of their language proficiency, feels understood and valued.
  • We integrate these systems (and your existing systems) to allow frictionless data exchange.

For example, when visiting a website, we often get a message from the company in a pop-up chat window. These messages are preprogrammed and sent by special robots that are designed to answer the most common inquiries and questions. Since little to no manual effort is involved in an automated system, your operations will almost always run error-free. Working on non-value-adding tasks like preparing a quote can make employees feel disengaged. When you automate these tasks, employees find work more fulfilling and are generally happier since they can focus on what they do best.

​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. First, as the data show, automation, by reducing the cost of operating a business, may free up resources to invest in other areas.

automation banking industry

Over the past decade, the transition to digital systems has helped speed up and minimize repetitive tasks. But to prepare yourself for your customers’ growing expectations, increase scalability, and stay competitive, you need a complete banking automation solution. We have found that across industries, a high degree of centralization works best for gen AI operating models. Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult. Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results.

A few customers also mentioned that their banks are missing the mark on providing seamless experiences, the kind of personalization they want, and cutting-edge innovation. This is a wake-up call for banks to step up their game with automation technologies. To get the most from your banking automation, start with a detailed plan, adopt simple-but-adequate user-friendly technology, and take the time to assess the results.

You can deploy these technologies across various functions, from customer service to marketing. Banks’ traditional operating models further impede their efforts to meet the need for continuous innovation. Most traditional banks are organized around distinct Chat PG 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.

The importance of the operating model

They have become the digital version of customer support and emerged as a new way to interact, offering personalized, prompt and efficient assistance on the text and voice-based channels of their choice. Everything runs like a well-oiled machine when banks automate these kinds of tasks. Banking automation amps up customer satisfaction, making sure that every interaction with their bank is smoother and more reliable. As RPA and other automation software improve business processes, job roles will change. As a result, companies must monitor and adjust workflows and job descriptions.

Implementing RPA is in the interest of all banks that want to streamline their processes, become faster in responding to customer queries, and reduce their operating costs. 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. 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.

Revolutionizing the banking industry with automation isn’t just about working harder but smarter. Banks are now turning to AI-powered automation and chatbots, not just for routine tasks but to ramp up efficiency with minimal effort significantly. This shift is about optimizing operations and building a rock-solid, smooth-running business.

By leveraging their ability to process vast amounts of data quickly, banks are not just detecting potential fraud but are proactively safeguarding the financial integrity of banks and the security of customer transactions. InfoSec professionals regularly adopt banking automation to manage security issues with minimal manual processing. These time-sensitive applications are greatly enhanced by the speed at which the automated processes occur for heightened detection and responsiveness to threats.

automation banking industry

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. They use RPA automation to help key in, move, and transform data across systems to conduct financial analysis, execute repetitive manual processes, and generate valuable reports.

QuickLook is a weekly blog from the Deloitte Center for Financial Services about technology, innovation, growth, regulation, and other challenges facing the industry. The opinions expressed in QuickLook are those of the authors and do not necessarily reflect the views of Deloitte. Since their modest beginnings as cash-dispensing services, ATMs have automation banking industry evolved with the times. Interestingly, as ATMs expanded—from 100,000 in 1990 to about 400,000 or so until recently—the number of 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.

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. 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.

This leads to massive cost savings, boosting profitability and improving the business’s overall margins. With a vision of ‘Leading the Future of Banking’, UnionBank wanted to leverage technology to provide an omni-channel banking experience for its customers. They were looking to elevate customer experiences by eliminating long wait times to reach customer support over calls by deploying an AI chatbot on two channels (Website and Facebook Messenger).

According to Deloitte, some emerging banking areas where generative AI will play a key role include fraud simulation & detection and tax and compliance audit & scenario testing. For more, check out our article on the importance of organizational culture for digital transformation. It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead.

Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks. Feel free to check our article on intelligent automation strategy for more. 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. It can be difficult to implement uses of gen AI across various business units, and different units can have varying levels of functional development on gen AI.

DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see /about to learn more about our global network of member firms. In the early RPA adoption stages, we help to assess your organization’s readiness, draft a tailored action plan, walk you through design and planning stages, and then go on to implement the end-to-end engineering solution.

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). Banks and other financial institutions must ensure compliance with relevant industry and government regulations. Robotic process automation in the banking industry can strengthen compliance by automating the process of conducting audits and generating data logs for all the relevant processes. This makes it possible for banks to avoid inquiries and investigations, limit legal disputes, reduce the risk of fines, and preserve their reputation. The key to getting the most benefit from RPA is working to its strengths.

If they become more efficient, this will have a ripple effect on many other industries that use their services. By implementing RPA, banks make their processes faster and more efficient. This allows brands to answer customer questions quickly and let their human employees focus on mission-critical tasks and serve their customers more efficiently. This is the essence of the potential of Robotic Process Automation in retail banking.

Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach. They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up. This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team. Discover how leaders from Wells Fargo, TD Bank, JP Morgan, and Arvest transformed their organizations with automation and AI.

Implementing robotics process automation in financial services dramatically reduces or eliminates the need for human involvement in mundane and repetitive tasks. This greatly reduces the likelihood of human errors together with unconscious bias and subjectivity that could contribute to skewed decision-making or increase risk. One of the most significant methods that banks and other financial institutions can adopt is robotic process automation (RPA) to boost productivity and increase efficiency while also reducing costs and errors. Modern banks and financial institutions have evolved from being mere transactional hubs to becoming comprehensive financial educators. Leveraging AI chatbots, they now offer a range of services including economic education, financial well-being, and literacy programs.

An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution. The Banking and Financial industry is seen to be growing exponentially over the past few years with the implementation of technological advancements resulting in faster, more secure, and reliable services. To remain competitive in an increasingly saturated market – especially with the more widespread adoption of virtual banking – banking firms have had to find a way to deliver the best possible user experience to their customers.

Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time. That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. 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. Automation helps banks streamline treasury operations by increasing productivity for front office traders, enabling better risk management, and improving customer experience.

Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage. 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. 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.

  • The bot streamlines purchase order entry, vendor verification, expense compliance audit, and payment reconciliation.
  • This reduces the waiting time and increases the efficiency of redressal to help banks improve their customer relationships.
  • These smart systems take the reins on repetitive, manual tasks, ensuring accuracy and freeing bank staff to focus on more complex, strategic work.
  • As RPA and other automation software improve business processes, job roles will change.
  • We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets.

Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. Banks and financial institutions that operate nationwide or globally comply with several tax regulations.

automation banking industry

Banks leverage RPA to create more defined workflows and link their inventory portal together. An RPA bot can track price fluctuations across suppliers and flag the best deal at pre-set time intervals. The RPA tool generally includes an intuitive and simple user interface (UI) and out-of-the-box capabilities. This means the staff does not need to configure or code the solution manually. Additionally, results are typically presented in an actionable and digestible form.

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. 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. You’ll have to spend little to no time performing or monitoring the process.

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