A recipe for banking operations efficiency

Discover smarter self-service customer journeys, and equip contact center agents with data that dramatically lowers average handling times. 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 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.

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. 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.

Full banking automation coverage

Automation simplifies this process, ensuring that all data is consistent and up to date, thereby saving considerable time and reducing the risk of errors. For centuries, banks demonstrated expertise in keeping, lending and saving money. This included how banks stipulated interest rates for lending, identified creditworthy cohorts and facilitated banking transactions. Applying business logic to analyze data and make decisions removes simpler decisions from employee workflows. Plus, RPA bots can perform tasks previously undertaken by employees at a faster rate and without the need for breaks.

Happy employees create a virtuous experience loop that includes happy customers. So, at the start of any large-scale transformation project or effort to deploy automation in a particular vertical, it’s crucial to bring your people with you. In addition to RPA, banks can also use technologies like optical character recognition (OCR) and intelligent document processing (IDP) to digitize physical mail and distribute it to remote teams. During the pandemic, Swiss banks like UBS used credit robots to support the credit processing staff in approving requests. The support from robots helped UBS process over 24,000 applications in 24-hour operating mode. Reskilling employees allows them to use automation technologies effectively, making their job easier.

Best Practices For Leveraging Automation In Banking M&As

The revolution in banking M&As, driven by technological advancements, promises a future where banks are more resilient, efficient and prepared for the challenges of an ever-changing financial world. 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.

• These pressures spread IT teams too thin, diverting their attention from the largest areas of opportunity.
• 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.
• In the future, these technologies may offer customers more personalized service without the need for a human.
• For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative.

Banks need to reverse this dynamic and make customer experience the starting point for process design. To do so, they need to understand what customers want, and how and when they want it. Instead of a major cost center, operations of the future will be a driver of innovation automation in banking operations and customer experience. There are many business processes where AI and RPD are already helping financial institutions become more innovative, and plenty of ground remains to be broken. But banks also face numerous challenges when embarking on an automation project.

For example, you can add validation checkpoints to ensure the system catches any data irregularities before you submit the data to a regulatory authority. They’ll demand better service, 24×7 availability, and faster response times. Using automation to create a cybersecurity framework and identity protection protocols can help differentiate your bank and potentially increase revenue.

Instead of a bank addressing an error or customer problem only when it reaches a certain scale or frequency, software can find errors that happen to even just one customer, such as a fee that’s been miscalculated or a double payment to a credit card. The customer can then be alerted about the mistake and informed that it has already been corrected; this kind of preemptive outreach can dramatically boost customer satisfaction. Banks could also proactively reach out to customers whom predictive modeling indicates are likely to call with questions or issues. For instance, if a bank notices that its older customers have a tendency to call within the first week of opening an account or getting a new credit card, an AI customer service rep could reach out to check in. As a result, robotic process automation, cognitive automation (CA) and business process management (BPM) technologies have become key competencies and objectives for most banks.

To provide a quantitative example of the return on investment (ROI) of automation, Forrester research recently analyzed several companies’ transition projects for paper-based workflows. Their findings showed that automation “increased productivity, lowered costs, and created better customer experiences and more engaged employees.” All told, the organizations analyzed saved $6.8 million over three years. You can make automation solutions even more intelligent by using RPA capabilities with technologies like AI, machine learning (ML), and natural language processing (NLP). According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry.