Importance of Data Analytics for Conducting External Audit

Banks, Insurance agencies and retail sector units have been using data analytics for analysis, model and design their industry for almost a decade. Audit firms have also started practising this to analyse and verify smaller audit firms for increasing audit quality. Lately, large audit firms have changed their methodology by taking data analytics for 100% verification of smaller audit firms.

The established concepts that made verification possible in the earlier days like,

• Testing 100% of transactions
• Risk Management Service
• Control testing

Now it is the time of data analysis in giving a bigger picture to the audit firm as to what is happening with the clients’ accounts.

Due to the evident results and adaptation by most of the firms, even though it is a new area for auditors, firms are starting to explore data analytics in auditing. This is not limited to external audit and can be used in various assurance engagements and analyse various transactions and decide other verification steps.

Usage in External Audit

Data analysis is used in recognising and finding transactions that don’t fit the normal patterns. Such transactions indicate or might have a larger chance of being a material misstatement or even indicate fraud. These data analytics are so accurate that they might replace auditors in the future.

But these solutions don’t take auditors out of the picture, and they free up the auditors to analyse the results and determine the further actions to be taken and what those actions should be.

Benefits of Data Analysis

Let’s see a few benefits of data analysis from the firm’s perspective,

1. Using data from any source
2. Bring Data Analysis into Audit Workflow
3. AI and Machine Learning Applications
4. Tailored Analytics
5. Testing entire Data-Sets

When auditors have a data analytics tool, they can provide more time for their clients.

1. Using data from any source

Recently, ever since the pandemic, accounting firms are under pressure to provide more value to their audit customers. However, it can be hard to develop data insights spread across multiple platforms, files, systems and solutions.

Data analytics software helps you integrate data across multiple sources so that auditors can perform analysis quickly and efficiently, providing higher quality insights and value to their clients. It also lets you extract data from any source.

2.  Bringing Data Analysis into Audit Workflow

Using data analysis has not been a part of any audit and assurance services. Auditors traditionally relied separately on additional data specialists. This resulted in more audit time, costs and no visibility of the tests that are performed.

Data analysis helps to simplify engagements by bringing automated systems for testing into the audit workflow and use that reports for future audit evidence.

3. AI and Machine Learning Applications

Analytics software helps use AI to replicate human auditors and make data analytics work like human auditors. Machine learning capabilities adapt the algorithm to provide the most accurate results depending on the data available.

By using AI and Machine learning, analytics software can check transactions and trial balance entries in a data set and provide further actions that should be taken. This gives out the few areas of concern initially for further examination.

4. Tailored Analytics

Conducting tailored or deep analytics often requires more time, more energy than most clients are willing to spend. Automated tools allow auditors to dig deep into data without hiring extra staff or personnel.

Fraud detection can often be very difficult with traditional audit practices due to the availability of a large amount of data at hand. Data analysis allows numerous tests to be tailored based on the characteristics of each entry.

5. Testing entire Data-Sets

Traditionally, data has been analysed by sampling a data set from traditional spreadsheets and conclusions formed based on those samples and the auditor’s know-how of that entity.

This creates a chance of potential error since the entire data is not analysed, and the conclusions based on the auditor’s knowledge of the entity, there is potential for error. Data analysis software’s test the entire data set, and any transactions that do not follow the normal pattern will be flagged as “Unusual Days.”