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

But in financial services, it’s not enough to identify a clunky process and build an RPA tool for it. Rather, banks need a unified, organization-wide strategy that accounts for internal and external communication, workflow issues, business strategy, security, design practices and the realities of a world disrupted by Covid-19. These new technologies, driven by artificial intelligence, machine learning and different forms of robotic process automation (RPA), are getting better by the year. Our research indicates that a significant opportunity exists to increase the levels of automation in back offices. By reworking their IT architecture, banks can have much smaller operational units run value-adding tasks, including complex processes, such as deal origination, and activities that require human intervention, such as financial reviews. Banks have enhanced many of their customer-facing, front-end operations with digital solutions.

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, automation in banking operations 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.

What is Banking Automation?

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. New technologies and ways of working help banks transform their operations, offering human-like digital experiences while improving cost & compliance. Finally, applying analytics to large amounts of customer data can transform issue resolution, bringing it to a deeply granular level and making it proactive not reactive.

In this sense, automation is an enabler that frees up urgently needed resources. Your employees will have more time to focus on more strategic tasks by automating the mundane ones. This results in increased employee satisfaction and retention and allows them to focus on things that contribute to your topline — such as building customer relationships, innovating processes, and brainstorming ways to address customers’ most pressing issues. Successful large-scale automation programs need much more than a few successful pilots. They require a deep understanding of where value originates when processes are IT enabled; careful design of the high-level target operating model and IT architecture; and a concrete plan of attack, supported by a business case for investment. 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.

Better Risk Management

For example, one bank achieved a 20 percent efficiency improvement by applying lean in its account-closure process; a good result, but the process constituted less than 1 percent of the bank’s total operations cost and so did not move the needle. Another bank used smart workflow tools to automate corporate-credit assessments, improving productivity by 80 percent. But it failed to replicate this success in other high-potential areas and thus aggregated operations costs hardly budged. Our software platform streamlines the process of data integration, analytics and reporting by cleaning and joining the sourced data through semantics and machine learning algorithms. It simplifies data governance process and generates timely and accurate reports to be submitted to regulators in the correct formats.

Early adopters will hold a strong position while the late adopters, over time, are likely to lose competitiveness, potentially irreversibly. We work with ambitious leaders who want to define the future, not hide from it. The language of the paper have benefited from the academic editing services supplied by Eric Francis to improve the grammar and readability. You’ll need automation to achieve these objectives and make yourself stand out in the crowd. 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.

Credit Services

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. Hexanika is a FinTech Big Data software company, which has developed an end to end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Harness talent, data, and intelligence to deliver superior customer experiences and business outcomes. Despite years of digital investment, many banks’ operating models are still complex, inflexible and as costly as ever. In fact, over the last eight years, these banks have managed to reduce their costs more than those that have been slower to embark on their journey to a digital operating model.

• Automation not only enables banks to achieve operational agility and capture huge cost efficiencies, it also helps them to deliver the digital experiences that customers increasingly demand.
• This will give operations employees time to help customers with complex, large, or sensitive issues that can’t be addressed through automation.
• It concluded that only half the opportunity (measured by the automation business cases completed on each manual process) could actually be captured.
• 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.
• Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies.

By taking full advantage of this approach, banks can often generate an improvement of more than 50 percent in productivity and customer service. For its unattended intelligent automation, the bank deployed a learning automation platform. The platform helped it seamlessly integrate its own systems with third-party systems for time and cost savings. The bank’s teams used the platform’s cognitive automation technology to perform several tasks quickly and effortlessly, including halving the time it used to take to screen clients as a part of the bank’s know-your-customer process. Today, many of these same organizations have leveraged their newfound abilities to offer financial literacy, economic education, and fiscal well-being. These new banking processes often include budgeting applications that assist the public with savings, investment software, and retirement information.

Banks have failed to scale in key innovation areas

Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes. Social implications aside, the imperative to automate is here like never before. Automation not only enables banks to achieve operational agility and capture huge cost efficiencies, it also helps them to deliver the digital experiences that customers increasingly demand.

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. 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.

Other banks have trained developers but have been unable to move solutions into production. Still more have begun the automation process only to find they lack the capabilities required to move the work forward, much less transform the bank in any comprehensive fashion. 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. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com. Our Reimagine Banking Operations helps banks move past the challenges and realize the full value of digital banking.

Most traditional banks are organized around distinct 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. Siloed working teams and “waterfall” implementation processes invariably lead to delays, cost overruns, and suboptimal performance.

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. Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. 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.

Automation and digitization can eliminate the need to spend paper and store physical documents. AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns. With cloud computing, you can start cybersecurity automation with a few priority accounts and scale over time. The report highlights how RPA can lower your costs considerably in various ways. For example, RPA costs roughly a third of an offshore employee and a fifth of an onshore employee.

Banking leaders need to think about their digital futures and develop a unified vision—one that looks holistically across all lines of business and IT. They need to wed the redesign of processes and operations to maximize the impact of automation. They need to combine the strengths of RPA, AI and human intelligence, making automation a core part of their business strategy.

• They will also have deep knowledge of a bank’s systems and possess the empathy and communication skills needed to manage exceptions and offer “white glove” service to customers with complex problems.
• By bringing everything together and connecting loose ends, automation enables the banking sector to deliver the cost-saving that it needs, while simultaneously delivering value to customers.
• While this research is not specific to financial services, it underscores the value of automation and makes evident its applicability for banking.
• This will enable them to deliver more personalized and relevant customer and employee experiences while dramatically improving cost and compliance.