Robotic process automation in banking industry: a case study on Deutsche Bank Journal of Banking and Financial Technology

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. RPA combines robotic automation with artificial intelligence (AI) to automate human activities  for banking, this could include data entry or basic customer service communication. RPA has revolutionized the banking industry by enabling banks to complete back-end tasks more accurately and efficiently without completely overhauling existing operating systems.

A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions. 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. This centralization is likely to be temporary, with the structure becoming more decentralized as use of the new technology matures.

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. 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.

Employing IDP to extract and process data faster and with greater accuracy saves employees from having to do so manually. Every bank and credit union has its very own branded mobile application; however, just because a company has a mobile banking philosophy doesn’t imply it’s being used to its full potential. To keep clients delighted, Chat PG a bank’s mobile experience must be quick, easy to use, fully featured, secure, and routinely updated. Some institutions have even begun to reinvent what open banking may be by adding mobile payment capability that allows clients to use their cellphones as highly secured wallets and send the money to relatives and friends quickly.

Why do banks need banking automation?

You can deploy these technologies across various functions, from customer service to marketing. 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.

For that, the customers are willing to interact with automated bots and systems too. For the best chance of success, start your technological transition in areas less adverse to change. Employees in that area should be eager for the change, or at least open-minded. It also helps avoid customer-facing processes until you’ve thoroughly tested the technology and decided to roll it out or expand its use.

IA consists mainly of the deployment of robotic process automation and artificial intelligence solutions. It enables a bank to acquire the agility and 24/7 access of fintech firms without losing any of its gravitas. 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. Increasing customer expectations, stringent regulations and heightened competition are making it more important than ever for banks to optimize and modernize their operations. Automation is helping banks worldwide adapt to organizational and economic changes to reduce risk and deliver innovative customer experiences. Various financial service institutions are striving to implement more effective automated technology that will set them apart from their competitors.

The importance of the operating model

An automated business strategy would help in a mid-to-large banking business setting by streamlining operations, which would boost employee productivity. For example, having one ATM machine could simplify withdrawals and deposits by ten bank workers at the counter. Customers are interacting with banks using multiple channels which increases the data sources for banks. The banks have to ensure a streamlined omnichannel customer experience for their customers. Customers expect the financial institutions to keep a tab of all omnichannel interactions.

And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture. 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.

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, McKinsey.com. Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. Financial services organizations are embracing artificial intelligence (AI) for various reasons, such as risk management, customer experience https://chat.openai.com/ and forecasting market trends. Similarly, Deutsche Bank saw substantial returns on investment when it embarked upon a comprehensive digital transformation journey where it deployed software to introduce both attended robotic process automation and unattended intelligent automation. 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.

Banking automation is the product of technology improvements resulting in a continually developing banking sector. The result is a significantly more efficient, dependable, and secure banking service. Automation can handle time-consuming, repetitive tasks while maintaining accuracy and quickly submitting invoices to the appropriate approving authority.

Your employees will have more time to focus on more strategic tasks by automating the mundane ones. 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. Automation helps banks streamline treasury operations by increasing productivity for front office traders, enabling better risk management, and improving customer experience. ​The UiPath Business Automation Platform empowers your workforce with unprecedented resilience—helping organizations thrive in dynamic economic, regulatory, and social landscapes.

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. Employees will inevitably require additional training, and some will need to be redeployed elsewhere. Lenders rely on banking automation to increase efficiency throughout the process, including loan origination automation banking and task assignment. You can foun additiona information about ai customer service and artificial intelligence and NLP. The advent of AI technologies has made digital transformation even more important, as it has the potential to remake the industry and determine which companies thrive. Learn about Deloitte’s offerings, people, and culture as a global provider of audit, assurance, consulting, financial advisory, risk advisory, tax, and related services.

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. This was another benefit of automation for Bancolombia, as automating repetitive and manual data-based tasks reduced operational risk by 28%.

A digital portal for banking is almost a non-negotiable requirement for most bank customers. With RPA and automation, faster trade processing – paired with higher bookings accuracy – allows analysts to devote more attention to clients and markets. 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. These dimensions are interconnected and require alignment across the enterprise. A great operating model on its own, for instance, won’t bring results without the right talent or data in place.

In the finance industry, whole accounts payable and receivables can be completely automated with RPA. The maker and checker processes can almost be removed because the machine can match the invoices to the appropriate POs. Equally important is the design of an execution approach that is tailored to the organization. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every quarter with an iterative build of long-term institutional capabilities. 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.

With the use of financial automation, ensuring that expense records are compliant with company regulations and preparing expense reports becomes easier. By automating the reimbursement process, it is possible to manage payments on a timely basis. With the use of automatic warnings, policy infractions and data discrepancies can be communicated to the appropriate individuals/departments. Invoice processing is a key business activity that could take the accountant or team of accountants a significant amount of time to guarantee the balance comparisons are right.

Automation allows you to concentrate on essential company processes rather than adding administrative responsibilities to an already overburdened workforce. Without automation, banks would be forced to engage a large number of workers to perform tasks that might be performed more efficiently by a single automation procedure. Without a well-established automated system, banks would be forced to spend money on staffing and training on a regular basis. Bank automation can assist cut costs in areas including employing, training, acquiring office equipment, and paying for those other large office overhead expenditures. This is due to the fact that automation provides robust payment systems that are facilitated by e-commerce and informational technologies.

They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. Banking automation has facilitated financial institutions in their desire to offer more real-time, human-free services. These additional services include travel insurance, foreign cash orders, prepaid credit cards, gold and silver purchases, and global money transfers. InfoSec professionals regularly adopt banking automation to manage security issues with minimal manual processing. These time-sensitive applications are greatly enhanced by the speed at which the automated processes occur for heightened detection and responsiveness to threats. Customers want to get more done in less time and benefit from interactions with their financial institutions.

How does banking automation work?

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. Systems powered by artificial intelligence (AI) and robotic process automation (RPA) can help automate repetitive tasks, minimize human error, detect fraud, and more, at scale.

Learn how SMTB is bringing a new perspective and approach to operations with automation at the center. Today, customers want to be met, courted and fulfilled through any organization that wants to establish a relationship with them. They also expect to be consulted, spoken to and befriended in times, places and situations of their choice.

Technology is rapidly developing, yet many traditional banks are falling behind. Enabling banking automation can free up resources, allowing your bank to better serve its clients. Customers may be more satisfied, and customer retention may improve as a result of this.

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. If would like to learn more about how automation can accelerate your bank’s transformation efforts, download our free ebook, The Essential Guide to Modernizing Banking Operations. Consistence hazard can be supposed to be a potential for material misfortunes and openings that emerge from resistance. An association’s inability to act as indicated by principles of industry, regulations or its own arrangements can prompt lawful punishments. Administrative consistency is the most convincing gamble in light of the fact that the resolutions authorizing the prerequisites by and large bring heavy fines or could prompt detainment for rebelliousness.

In order to be successful in business, you must have insight, agility, strong customer relationships, and constant innovation. Benchmarking successful practices across the sector can provide useful knowledge, allowing banks and credit unions to remain competitive. To put it another way, an organization with many roles and sub-companies maintains its finances using various structures and processes. Based on the business objectives and client expectations, bringing them all into a uniform processing format may not be practicable. The central team, on the other hand, is having trouble reconciling the accounts of all the departments and sub-companies.

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. If you work with invoices, and receipts or worry about ID verification, check out Nanonets online OCR or PDF text extractor to extract text from PDF documents for free. Automation may be implemented in a big wide variety of enterprise system automation projects, there are numerous well-described use instances in this space.

Book a discovery call to learn more about how automation can drive efficiency and gains at your bank. 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.

Whether it’s far automating the guide procedures or catching suspicious banking transactions, RPA implementation proved instrumental in phrases of saving each time and fees compared to standard banking solutions. Banks must find a method to provide the experience to their customers in order to stay competitive in an already saturated market, especially now that virtual banking is developing rapidly. Keeping daily records of business transactions and profit and loss allows you to plan ahead of time and detect problems early. You can avoid losses by being proactive in controlling and dealing with these challenges. Changes can be done to improve and fix existing business techniques and processes.

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. 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. 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. To begin, banks should consider hiring a compliance partner to assist them in complying with federal and state regulations. Compliance is a complicated problem, especially in the banking industry, where laws change regularly.

However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5). Core systems are also difficult to change, and their maintenance requires significant resources. What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases.

IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. Watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently. For its unattended intelligent automation, the bank deployed a learning automation platform.

They’re heavily monitored and therefore, banks need to ensure all their processes are error-free. But with manual checks, it becomes increasingly difficult for banks to do so. Insights are discovered through consumer encounters and constant organizational analysis, and insights lead to innovation. However, insights without action are useless; financial institutions must be ready to pivot as needed to meet market demands while also improving the client experience. [Exclusive Free Webinar] Automate banking processes with automated workflows. Human mistake is more likely in manual data processing, especially when dealing with numbers.

Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency. To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences. Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations.

Automation is the advent and alertness of technology to provide and supply items and offerings with minimum human intervention. The implementation of automation technology, techniques, and procedures improves the efficiency, reliability, and/or pace of many duties that have been formerly completed with the aid of using humans. 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. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets.

The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. 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.

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. Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks. Banks are already using generative AI for financial reporting analysis & insight generation. 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. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions. QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts.

Banking automation helps devise customized, reliable workflows to satisfy regulatory needs. Employees can also use audit trails to track various procedures and requests. Embedded finance can help banks serve clients whenever and wherever a financial need may arise. In recent years, AI has revolutionized various aspects of our world, including the banking industry. In this video, Jordan Worm delves into five key areas where AI is making groundbreaking impacts on banking. Deliver consistent and intelligent customer care with a conversational AI-powered banking chatbot.

• Today, many of these same organizations have leveraged their newfound abilities to offer financial literacy, economic education, and fiscal well-being.
• Your employees will have more time to focus on more strategic tasks by automating the mundane ones.
• To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences.
• Decide what worked well, which ideas didn’t perform as well as you hoped, and look for ways to improve future banking automation implementation strategies.
• The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies.
• Without a well-established automated system, banks would be forced to spend money on staffing and training on a regular basis.

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. 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. 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.

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. Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners.

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. 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. In the right hands, automation technology can be the most affordable but beneficial investment you ever make.

Although intelligent automation is enabling banks to redefine how they work, it has also raised challenges regarding protection of both consumer interests and the stability of the financial system. This article presents a case study on Deutsche Bank’s successful implementation of intelligent automation and also discusses the ethical responsibilities and challenges related to automation and employment. We demonstrate how Deutsche Bank successfully automated Adverse Media Screening (AMS), accelerating compliance, increasing adverse media search coverage and drastically reducing false positives. This research contributes to the academic literature on the topic of banking intelligent automation and provides insight into implementation and development. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.

Banks face security breaches daily while working on their systems, which leads them to delays in work, though sometimes these errors lead to the wrong calculation, which should not happen in this sector. Location automation enables centralized customer care that can quickly retrieve customer information from any bank branch. The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack.

AI-powered automation takes the intelligence of AI with the repeatability of automation. For example, AI can enhance robotic process automation (RPA) to better parse data analytics and take actions based on what the AI decides is best. One example is banks that use RPA to validate customer data needed to meet know your customer (KYC), anti-money laundering (AML) and customer due diligence (CDD) restrictions. 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.