Why Process Automation Fails at Most Banks


In the current market environment, banking institutions face an unprecedented set of challenges. Our Amazon Age has conditioned their customers to transact whenever and however they want, and to expect immediate gratification. The pandemic has re-written all the rules regarding employment, with people re-examining their career and life goals, with employees wanting to work remotely, and with increased difficulty in recruiting, compensating and retaining qualified staff to meet higher market expectations. Banks are now facing a new generation of FinTech competitors offering a broad range of services and a high degree of convenience, and without the overhead that burdens brick & mortar institutions.

There has never been a time in which operational excellence is more important, in terms of the success and survival of banks. This has given rise to a compelling set of reasons why intelligent process automation is the pathway to increasing profitability, building customer loyalty, and gaining advantage over existing and future competitors.

If the challenges have never been greater, and the solution is self-evident, what’s preventing banks from leveraging the power of process automation to solve their most pressing business challenges – from personalizing engagement to automating service and streamlining operations?

Overcoming the Limitations of RPA

There are several reasons why so many banks are failing to apply process automation effectively, or at all. One basic reason is that they lack an understanding of what type of process automation is most appropriate for specific applications. Robotic Process Automation (RPA), a technology that has been available for some time, is a good example of this.

The underlying objective of RPA is to replicate human steps; recording what a user does, and then performing those same tasks in the same order, using different input data. This type of so-called “swivel” activity involves capturing data from one system, then swiveling your chair to enter that data into another system. The first generation of RPA product has served as an effective technology for automating highly repetitive tasks with minimal IT support.

The major shortcoming of RPA, however, has been that the technology’s simplistic approach to decisioning has made it unsuitable for complex tasks, or for scaling any function. Similar to business process reengineering, orchestration difficulties grow as the volume of RPA bots increase. Another shortcoming is that as the user interface of systems changes, re-recording of data is required, and exception handling requires programming expertise. Notably, regulators are seeking an understanding of why decisions are made, and audit trails often do not exist with RPA implementations.

In summary, RPA is limited to user interface-based automation that records exactly what a user does; and banks should not expect it to deliver any more than that.

Moving Beyond RPA’s Limitations

Banking leaders seeking to move beyond the limitations of RPA must first start with a clear articulation of the function they intend to automate. Key performance metrics and a feedback loop need to be identified, both to measure outcomes and understand whatever modifications will be required to achieve the targeted metrics. Additionally, a workflow engine must be available, in addition to whatever data is relevant to the function.

The primary capability necessary for bankss to move beyond the limitations of RPA is an effective case management platform, which allows for all relevant information to be easily captured and located in one place. There is no need to move from one system to another, or to deal with different APIs, because the case platform eliminates those steps. The case platform also allows for tasks and workflows to be defined, for all actions to be tracked, and for reports to be generated that facilitate informed decision-making. With case management, functions can be automated from intake through resolution, with complete documentation.

Case Platform-Driven Automation Example: Dispute Resolution

One example of how case management can be applied effectively to automate decision-making involves resolution of customer disputes. Regardless of how the dispute-related information is received, whether directly or through a third party, at some point it will need to be dispositioned by the bank’s operations staff.

Using a case management platform, here is how the automated resolution process would proceed:

  • In the dispute intake process, the customer uses an intelligent form from an online portal. If the value of the dispute is less than the established threshold value, such as $25, and if there is no history of abuse, then a provisional credit is issued immediately.
  • Integration with the online system allows for minimal data entry for the customer, while simultaneously auto-populating information so that compliance requirements are addressed.
  • Responses to a set of well-defined questions regarding the details of the transaction (Did you make the purchase? Did you receive the item?, etc.), are captured either by email or a call center representative. Either self-service forms are used, or a rep / teller is guided through the information capture process.
  • All communication with the customer is automated, and progress can be tracked by clicking a button on an online portal.
  • The dispute is triaged and the appropriate workflow is initiated by a digital analyst. If the dispute is related to a debit transaction, then Reg-E compliant steps are also taken where a provisional credit is provided within the stipulated timeframe.
  • Should a charge-back need to be initiated with the processor, the digital assistant will monitor responses and automate the next steps required once responses are received.
  • All correspondence can be sent to the customer, as well as daily summary reports sent to appropriate users within the bank.
  • Based on all available information, the dispute analyst makes a determination regarding disposition of the matter, and initiates the chargeback process with the merchant, if warranted.
  • All documentation regarding the dispute and the steps in the decisioning process is recorded in the case management platform.

What’s important to understand in this simple example involving dispute resolution is that case platform-based automation can be applied by banks to a broad range of essential operational applications, including financial fraud mitigation, AML and regulatory compliance, loan processing, onboarding, and KYC, and addressing contact center service requests.

Perhaps more importantly, case platform-based automation can be applied to operational applications involving a significantly higher degree of complexity. This is possible because all essential case-related data resides in a single system, allowing Artificial Intelligence and Machine Learning techniques to be applied; addressing whatever level of decisioning and workflow automation is required.

Overcoming the Implementation Challenge

A growing number of banks understand the limitations of RPA, and appreciate the advantages of a case platform to deliver automated solutions. However, they are often discouraged from taking steps to implement an intelligent automation strategy based on their perceptions of the time, expense and disruption that might be involved in any transition. This tendency to remain with “the devil that you know” is understandable, but may also be the reason why those institutions will end up on the losing end of the digital transformation in financial services.

There are viable ways for banks to manage the inherent risks involved in any transition to automation of key operational functions. Given the current staffing challenges and limited internal resources, many banks have explored partnerships with organizations offering case management platforms, as initial step toward intelligent automation of key operational functions.

Those institutions that have succeeded in partnering with case platform providers understood that:

  • Software solutions must be combined with deep financial services industry expertise from the solution provider, for the software to be effective;
  • With RPA, only a superficial process is implemented, with no intelligence related to the underlying issues that serve as the basis for decisions made by the solution provider’s domain experts;
  • The solution provider must understand the technology and the process, and also have experience in the specific operations that are being automated, whether that involves fraud, disputes, loans, compliance, etc.;
  • The solution provider must have the flexibility and expertise to customize the case platform to accommodate a bank’s unique processes without significant incremental expense;
  • The solution provider must possess a track record of maintaining the highest levels of data security;
  • The solution provider must be willing, based on the bank’s needs, to either bridge the resources gap over a defined period and hand off the automation capability; or to serve in a longer-term partnership role as an ad hoc member of the bank’s staff.

Whether a bank partners with a qualified solutions partner to implement intelligent automation, or undertakes that task internally, operational excellence should be an enterprise level priority. “Back office” functions are no longer table stakes. They are now the keys to improved productivity, cost savings, customer satisfaction and loyalty, and to competitive advantage.

About Author:
N. Venu Gopal is Chairman of New York-based Quinte Financial Technologies, a company that enables financial institutions to gain competitive advantage through operational excellence. Mr. Gopal has nearly 40 years of experience as a technologist and successful entrepreneur. In addition to his role at Quinte, Mr. Gopal also serves as Chief Executive Officer at two other successful software companies, including Aithent, Inc. and Rhoads Online Institute. Mr. Gopal holds undergraduate and graduate degrees from the Tokyo Institute of Technology, and an MBA from Columbia University.