Axiata believes analytics is key to better auditing in the digital age

IN A WORLD where 2.5 quintillion bytes of data is generated daily, sampling is no longer an efficient way to conduct audit or fraud detection.

Instead, analytics is needed to more reliably identify potential cases of non-compliance during an audit process or fraud detection, Axiata Analytics Centre (AAC) AI Head Ravi Kumar Madavaram and Analytics Consultant Siew Sanz Ng told Tech Wire Asia ahead of their presentation at the Malaysian Institute of Accountant’s Forensic Investigation and Fraud Analytics Conference.

Elaborating on the need for analytics in the audit function, Ng said that analytics can help auditors analyze whole sets data from a variety of sources, identify gaps in policy or processes, and zoom in on risk-based flags.

Overall, using analytics helps audit professionals get a big-picture overview and create actionable business insights.

“Based on flags of non-compliance/potential fraud identified, we can then determine which areas of audit that need to be prioritized based on severity and number of flags,” Madavaram explained.

Analytics is a skill that audit professionals must acquire

While there are many potential benefits to using analytics, the reality is that not many audit professionals currently use the technology to simplify their workflow and become more effective in what they do.

Ng explains that in order to become an analytical auditor, professionals need to shift their mindset and learn new skills.

In terms of mindset, auditors need to start identifying and understanding data source and data types, and think about how that data transcribes into the real world.

Further, the lens with which an auditor views their tasks need to stem from a data perspective – how to interpret data and derive insights.

In terms of skills, Madavaram emphasized that audit professionals need to acquire new skills in order to go digital.

“Some of the much-needed analytics skills include data manipulation, cleaning, analysis, and visualization as well as applications of different visual types.

“An added edge would be knowing how to navigate tech environments to locate, validate, extract, format and store data and this is more commonly known as data engineering,” said Madavaram.

Getting to the future state of audit analytics

In Ng’s words, the ideal future of analytics is to have continuous monitoring where data is analyzed in real-time to hone in on compliance failures or potentially fraudulent cases.

However, before we arrive at the ideal end-state, Ng runs through a few stages that the industry needs to get through:

# 1 | Ad-hoc analytics

Most audit teams usually start using data in a few selected audit use cases, on an ad-hoc basis.

Although this isn’t suitable, it’s definitely a start and will help audit professionals understand the value of data and the power that analytics tools can provide to them.

# 2 | Managed analytics

This is the natural evolution of the ad-hoc analysis where teams start thinking about scaling up their analytics project and bringing in more data to cover more ground, faster.

Having built the basis for ad-hoc analysis, Ng believes that the same analysis can be easily repeated by replacing the data source, albeit using a manual process.

# 3 | Continuous auditing

Managed analytics helps bring more transactions into the purview of the digital, analytically-powered audit. Once this has been proved effective, the team naturally needs to scale up the project and take it to the next level.

The shift from managed analytics to continuous auditing involves automating the manual process of data extraction and automatically feeding new data into the analytics engine.

# 4 | Continuous monitoring

Progressing from continuous auditing to continuous monitoring means that business teams can now address a concern before it becomes an audit issue.

At this stage, audit professionals unleash the full potential of analytics and help their organization better protect itself from all external and internal threats as far as fraud and transactional non-compliance are concerned.

This is also where the ownership of analytics shifts to being under the purview of business functions, explained Ng.

“To achieve continuous monitoring, a seamless integration of database components, the analytics engine, and visualization tools is important,” emphasized Madavaram.

Although audit professionals need to operate in technology-driven ecosystems, acquire analytical skills and adopt a data-first mindset, Madavaram highlights that existing knowledge of audit practices, relevant regulations, and accounting standards will continue to be relevant.

---

---

---

---