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Survey Shows Digital Transformation Driving Process Discovery, Task Mining Implementation

Financial services firms, including both banks and credit unions, are one of the largest users of process discovery and task mining tools to improve organizational processes and propel digital transformation, according to a new survey conducted by Blueprint Software Systems.

In truth, this finding should probably come as nobig surprise to anyone in the financial industry. Banks and credit unions, after all, were among the earliest adopters of robotic process automation (RPA) to automate the repetitive, rules-based tasks required to execute routine business processes such as accounts payable, accounts receivable, statement preparation, and customer onboarding. And where RPA is present, the use of process discovery and task mining solutions to identify and document current state operations and existing processes typically follows.

While the use of these tools may not surprise anyone in the industry, the disconnect between the benefits they are expected to produce prior to implementation and benefits they actually provide after deployment may be shocking. The survey indicates, for example, that while most banks and credit unions ranked task automation delivery and waste reduction as the highest anticipated benefits of process discovery, enhanced compliance and improved task visibility and execution were the benefits most survey participants actually achieved.

The survey also shows that despite the high rate of adoption and benefits realized, process discovery has its share of pain points and barriers to entry. Issues surrounding privacy and security, for example, were noted by only one in five organizations prior to implementation. That figure rose to more than 30 percent following deployment. Similarly, while just 23 percent of participants anticipated challenges with the time required to manage and monitor task discovery tools before using them, that number rose to 30 percent after those tools were installed.

While the reasons for these discrepancies vary somewhat from one organization to the next, a primary reason likely resides with the very nature of process and task mining tools. Both are highly technical solutions which depend on artificial intelligence (AI) to discover processes and tasks. Process mining tools use AI to search through event logs to uncover and document an organization’s business processes.

Task mining tools are similar, although their focus is on discovering the tasks and sub-tasks that are needed to execute each process. They also sit at the desktop level, recording the interactions (mouse clicks, keyboard inputs, etc.) that the user performs, taking screenshots of every interaction to infer what the user is doing, and documenting the task.

While both process and task mining solutions are able to collect a great deal of data, that may, in fact, also be their biggest weakness. In practice, they actually collect too much data and introduce too much noise by casting a wide net and identifying every variation for how each project or task is executed. They then use machine intelligence to merge all of that information back together so that it can be scrubbed to generate an accurate and precise picture of what’s actually happening at the organization.

All of this activity may create an untenable situation for many banks and credit unions. Without the proper resources, such as data scientists on the scene who are capable of making sense of all that data, process and task mining tools may well create a whole new set of challenges for the institution. Both tools are also expensive and, at least in the case of task mining, create privacy issues since the software essentially watches and records what users are doing at their workstations.

For these and other reasons, many banks and credit unions are looking to inject more humanity into their process discovery efforts. This is often accomplished by starting process discovery not with technology, but by the people who already know and understand the processes that define how the organization functions. These process owners and subject matter experts can readily help in identifying and mapping out the higher-level processes on which the organization depends, completely eliminating the overwhelming volume of data that process mining tools produce.

From there, institutions can turn to human-driven task capture solutions to identify the details of low-level tasks, such as the actions, parameters, screenshots, and applications with which each task interacts. While similar to task mining tools in their objective, task capture tools don’t rely on machine-based technology that drops agents onto employees’ computers and produces an overwhelming amount of data. Instead, task capture tools must be manually triggered by an employee when they execute a task. From there, they record each interaction, taking screenshots of each step along the way, and then map the data produced into a process editor where it can be further modified and optimized to determine the viability of automating the task.

Task capture solutions also can be used to identify the various applications, business rules, regulatory and compliance constraints, and security protocols (critical dependencies) connected to and impacting the organization’s processes and tasks. It is these critical dependencies which provide the context needed to understand why tasks are executed in a certain way which, in turn, can be used to improve both compliance and the quality of automation initiatives.

The obvious difference between these task capture tools and the process and task mining solutions is the human element. By allowing workers to be involved in process discovery from the start and have some control over the software that is used, banks and credit unions can largely avoid both the overwhelming volume of data and the privacy issues that have plagued mining implementations. They can also better align their process discovery expectations with reality in order tospeed their digital transformation through automation.

Charles Sword is the Chief Revenue Officer at Blueprint Software Systems and is responsible for all aspects of market development for Blueprint’s Enterprise Automation Suite, a powerful digital process discovery, design and management solution that enables enterprise organizations to capture, identify, design, and manage high-value automations with speed and precision in order to scale the scope and impact of their RPA initiatives. Charles is a recognized expert in emerging technologies with over 20 years of experience delivering high impact, strategic solutions for Global 2000 organizations and is passionate about delivering technology that helps teams to rapidly optimize, automate, and digitally transform their organizations.For more information, visit https://www.blueprintsys.com/

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