Robotic process automation – An intelligent choice for user acceptance testing in the financial world

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The much-needed speed-to-market of software makes it necessary for the testing community to evaluate smarter methods of automation. Scalability, platform independence, and the ability to mimic humans makes robotic process automation(RPA) critical for the testing community. Read on to learn about how RPA can be leveraged for user acceptance testing in the financial services world.

The problem is not that testing is the bottleneck. The problem is that you don’t know what’s in the bottle. That’s a problem that testing addresses.” Michael Bolton

For many years, the focus of automation was on automating test cases for functional testing and system testing using traditional automation tools, such as unified functional testing (UFT), Selenium, and others. Today, with the proliferation of digital technologies, agile, and DevOps, it is critical to automate the entire lifecycle – from requirements to user acceptance testing (UAT).

UAT is the final stage of software testing and occurs before a customer accepts the new application. It is in this phase that traditional automation techniques are not very effective, and it is most relevant to leverage robotic process automation (RPA).

In simple terms, robotic process automation (RPA) is a technique to automate business processes or human tasks. While this technique is in the nascent stage for the testing community, it is fast becoming an important one according to the 2018 World Quality Report (WQR), where 54% of respondents stated that they will explore leveraging this technique in the coming year.

RPA makes UAT and end-user testing more effective than before, especially for end-to-end business process scenarios in retail, commercial banking, and capital markets where the user acceptance test plays a critical role.

Mentioned below are five key reasons for this:

  • Task-based automation technique: Traditional automated techniques are based on programmatic instructions. RPA, on the other hand, replicates user actions and processes. Apart from automating tasks, RPA tools with cognitive abilities can intelligently take decisions to perform actions just like a user would.
  • Platform independence: Traditional automation techniques depend on the architecture under test. RPA, on the other hand, is platform-independent. A typical user-acceptance test scenario spans multiple systems. Using assisted and unassisted RPA techniques one can automate a business process spanning multiple technologies, for example web browsers, mobile applications, and mainframe-based applications easily.
  • Scalability: In traditional automation, the automation engineer needs to write programmatic instructions to achieve parallel execution. This also necessitates procuring physical machines with strong processing capabilities in test environments.

On the other hand, using unassisted automation techniques in RPA, one can easily run task-based scenarios on several virtual machines.

  • Maintainability: Traditional automation frameworks are harder to maintain. A change in a module leads to changing object identifiers in multiple scripts whereas with RPA tools the tester only needs to update the business flow.
  • Workforce skill: Programming capability is a must for traditional automation. RPA scenarios created through flow charts using simple drag and drop techniques make it easier for business users to leverage this technique in automating user tasks.

For these reasons, five areas where RPA technology is proven and most beneficial to implement in UAT in financial services are:

  1. Payments reconciliation: This includes validation of time-consuming repetitive scenarios such as check processing, transaction matching, and reporting.
  2. Trade execution: This encompasses validation of critical, real-time scenarios spanning multiple systems, such as clearing of trades, reconciliation of trades, and resolving discrepancies.
  3. Know your customer: This refers to validation of high-volume scenarios such as onboarding customers, authentication of customers, and risk assessment.
  4. Anti-money laundering: This indicates user-acceptance testing of highly complex, rule-based, and data-intensive scenarios such as risk assessment, fraud analytics, and customer and transaction screening.
  5. Claims payment: This refers to testing of highly rule-based scenarios such as claims reviews, investigation, adjustment, remittance, and denials.

In summary, the much-needed speed-to-market of software makes it necessary for the testing community to evaluate smarter methods of automation. Scalability, platform independence, and the ability to mimic humans will soon make RPA a must have technique in the testing community.

To learn more or discuss RPAs, feel free to connect with me at:

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