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Analytics for Accounting

December 18, 2018
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Analytics for Accounting

By Cheryl Lim

The MIA International Accountants Conference 2018 focused heavily on technology and transformation, including a session on data analytics for accounting.

Moderator Simon Tay Pit Eu, Executive Director, Professional Practices & Technical MIA kicked off the session by explaining data analytics in a nutshell. “Data analytics involves the acronym ICTM, which is the Inspecting, Cleansing, Transforming and Modelling of data, for three purposes: one, discovering of useful information, two, to suggest conclusions and three, to support decision- making.”

Big data analytics is especially critical as accountants evolve from just record keeping and hindsight to offering predictions and foresight to support and guide businesses, agreed Nik Shahrizal Sulaiman, Assurance Partner, Risk Assurance Services, PwC Malaysia and Surendran N Sankaran, Technical & Digital Insight, Maybank.

“While accountants and the finance function might have relied on the IT team to run analytics before, these days, accountants are capable of doing the data mining and data analytics with the aid of tools,” added Surendran, who predicted that “data visualisation or data dash boarding and what we call as infographics using pictures will be the next wave.”

He explained that Group Audit in Maybank is “trying to build an intel of analytics for the future. Instead of you coming up with the possible scenarios, the system or tool can tell you what are the potential things happening or could potentially happen using internal data as well as external data that place an impact on all factors outside of the bank.” Rather than the auditor’s traditional pure focus on compliance, effective data exploration requires an in-depth analysis involving the four Ws (What, Who, When, Why) and one H (How), with the ‘Why’ being the most important to understand the reason behind an occurrence, said Surendran.

(L – R) Simon Tay Pit Eu, Nik Shahrizal Sulaiman, Surendran N Sankaran & Prof Dr Siva Muthaly

“In essence, Big Data Analytics involves the concept of D2 and P2, where the two Ds are Descriptive and Diagnostics – describing and diagnosing a data set – and the two Ps relate to Prediction and Prescription – predicting the future, inferring from the patterns and behaviours and applying any prescriptions to remediate anomalies found,” explained Professor Dr. Siva Muthaly, Dean, Faculty of Business & Management, Asia Pacific University of Technology & Innovation.

“For practical purposes, Big Data Analytics is also being deployed to stamp out creative cyber fraud, such as the use of Benford’s Law to pick up complex split transactions, said Surendran, and to give actionable insights into areas as varied as consumer behaviours,” said Dr. Siva. Going beyond this, Nik urged accountants to use data analytics for strategic business purposes because “increasingly, many accountants are expected to give business insights in terms of how businesses can be made better rather than just providing information about transactional values. That is the real challenge because the most important thing is to give the correct insight.”

Asked whether accountants or data scientists should take the lead in data analytics, Surendran emphasised that top management needs to be in the driver’s seat. “Who drives data analytics is the key.  (There is a) stronger message if it is conveyed from the CFO downwards. Ultimately the CFO wants to see results, they want to see more of foresights and how this data could actually make more meaningful decisions.” Offering an Australian perspective, Dr. Siva noted that while the CIO handles big data analytics in collaboration with a pool of experts, the actual final decision rests with the CEO.

In order to give the right insights, completeness of data and the ability to ask the right questions are key. “Reliability of the data, data integrity, and data accuracy are among the factors that must be considered,” pointed out Surendran. “By asking the right questions, you can figure out the tools and methodology to answer the questions. Sometimes the danger of data analytics is that you have so much data, so many conclusions being produced but these are not relevant to the question at hand. So, in summary it is very important to know what you are trying to do with that question,” concluded Nik.