Automation of data quality testing

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Marina Veber Director, Financial Services Advisory, PwC US October 09, 2018

It took the bot two minutes.

At most financial services firms, it would take a human at least 11 hours. This is the power of Intelligent Automation (IA).

Over the past two years, I’ve been part of PwC’s rapidly expanding IA practice. I want to share with you the use case that sparked my interest in this topic: automation of data quality (DQ) testing. First, I want to thank Jay Srinivasan and Ashish Balsaraf. They’ve helped me implement this important use case as a proof-of-concept (PoC) at one of my clients—and helped me write this blog too.

Let’s start with why this matters so much.

Automation of data quality testing - PwC

All financial institutions place a lot of emphasis on compliance with Anti-Money Laundering (AML) regulations. Among other things, this includes monitoring transactions for AML risk using a transaction monitoring system. But if the transaction monitoring data is inaccurate, the process breaks down, introducing real risk for the firms.

To address the vulnerability, many firms test the data used for transaction monitoring against six key data quality dimensions—completeness, validity, accuracy, consistency, integrity, and timeliness—either by choice or as directed by a regulator.

Until now, DQ testing has been seen as a very complicated and time-intensive exercise. Figure 1, below, illustrates four components of this exercise: planning and scoping, data collection and data loading, test execution, and review and reporting. At least for now, automation won’t address the heavy lift involved in the planning and scoping process. But by deploying IA, financial services firms may be able to make their risk prevention programs much more consistently effective. Before IA, this wouldn’t have been practical.

Figure 1: Data quality testing process - manual

You may wonder what DQ testing has to do with IA. It’s actually an ideal use case. DQ testing is a costly exercise, and existing DQ monitoring tools do not monitor against all six aforementioned DQ dimensions (for example, cross-hop testing, which falls under completeness). IA can fill the gap to conduct this type of testing periodically at a fraction of the cost of manual, incremental execution.

Figure 2, below, shows another iteration of the DQ process, using icons to indicate which steps can be automated. For the PoC, we automated steps seven and eight of the process for one product line, foreign currency, composed of 130 scripts. Manual execution of each script and documentation of results takes five minutes, at a minimum. Therefore it takes approximately 11 hours to execute all 130 scripts. In stark contrast, the PoC bot was not only able to execute all 130 scripts in under two minutes, but it was also able to compare the current results to the results of the previous run and highlight the differences in the same time.

In our testing, we saw IA achieve a 99.7% time saving. Obviously, every testing scenario is unique. But this is a clear indication that using IA to support periodic DQ testing may be a viable and economic option for identifying and proactively remediating DQ issues. This is very good news for financial services firms—and to the degree that it adds trustworthiness and reduces risk in the broader financial system, this is very good news for all of us.

Figure 2: Data quality testing process - potential areas of automation

 

This is only one of many examples. If you are curious about IA and how it could be leveraged at your firm, please reach out or drop me a comment below.

To join the conversation, visit this post on Marina's LinkedIn page.

 

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Marina Veber

Director, Financial Services Advisory, PwC US

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