How AI-powered Data Analytics Can Redefine Audit Processes: An Intelligent Operating Model

In this age of the big data revolution, most modern enterprises are using intelligent technologies like AI to leverage data far more efficiently and effectively towards process excellence and enhancing business operations.
A survey by New Vantage has drawn inferences that 97.2 % of organizations are investing in big data and AI initiatives.

One such business area of significant evolution in terms of digital transformation, particularly the adoption of AI, is auditing. This fast-evolving data-driven business culture is changing how companies keep a track of their operations and audit their financial status. Traditional audit practices that involve paper-based or legacy methods of evaluating a company’s performance and financial statements are now becoming obsolete. And auditing is no longer about verifying financial statements and adhering to regulatory standards alone. It has extended to analyzing a company’s performance, identifying weaknesses, anticipating future risks, and suggesting solutions to enhance business operations and reputation.

To meet today’s extensive auditing requirements of enterprises, audit entities are turning towards building intelligent data-centric operating models.

Let’s take you through more details on how a data-driven business model helps audit companies streamline operations.

Data Analytics and AI - Redefining Audit Processes

Traditional audit methods are now struggling to keep pace with the current-day auditing that demands faster, real-time, and more accurate outcomes. This demand has triggered a wave of digital transformation of auditing platforms, improving the auditing workflows and making the lives of auditors a lot easier. These new-age audit platforms have also led to higher expectations from audit practitioners as well as end customers to further improve the turnaround time and accuracy of the audit process. This has called for a better understanding of data behind the complex audit processes, workflows that warrant automation, and also a mechanism that allows for actionable intelligence to auditors. Given the diverse nature and volume of data, an AI-driven audit platform is the need of the hour.
Audit companies all over the world are catching up with this growing trend of leveraging AI-powered data analytics for the efficiencies it brings in analyzing and understanding a company’s data.
  • AI-driven data analysis allows audit firms to examine a larger volume of transactions and the veracity of every transaction, unlike traditional audits that can verify only a limited volume of transactions through sampling methods.
  • It empowers auditors by giving a deeper understanding of the company – acquiring ample evidence on transactions, identifying material misstatements in the general ledger, assessing operational risks, predicting business outcomes, and ensuring intelligent decision-making.
  • Audit firms can forecast future trends through accurate representations, whereas legacy audit processes may often overestimate or underestimate future insights that damage market confidence, and cause loss of potential investors.

An Intelligent Auditing Solution Powered by Data Analytics and AI

An AI-powered auditing platform is a perfect solution for solving audit complexities today. These advanced capabilities give more room for auditors to focus on advisory services.

Auditors can pick an off-the-shelf solution that is built based on various standards, e.g. Generally Accepted Accounting Principles (GAAP), and various other industry standards, in case they do not have access to data scientists and computer engineers to build custom AI solutions. They can deploy the best solution after extensively analyzing their business requirements.

This way, auditors can be relieved from the process of collecting, formatting, and processing structured, semi-structured, and unstructured data and focus more on analyzing the audit results to contribute towards strategic decision-making. Here’re 3 key benefits of Data Analytics and AI in auditing:

Data analytics & AI in Audit

1. Big Data Modernization for Operational Efficiency

Legacy or outdated data platforms come with a set of limitations such as data silos, lack of security, inability to handle diverse and large volumes of data in the form of text, images, and voice, and high maintenance costs.
A modern data platform built on-prem or on the cloud enables Big Data Modernization – ingesting data from several source systems in a client’s enterprise and allowing better data consolidation and centralization. This, in turn, increases the possibility of enterprise-grade data being available in one place and reduces data silos.
Big Data Platforms like Hadoop, Spark, and cloud data platforms like AWS, Azure, and GCP, enable large volumes of data to be ingested and processed through superior data transformation tools like Spark, AWS Glue, Azure ADF, and GCP Data flow. These platforms are capable of handling high velocity of data and hence make clean data available for consumption at a faster rate.
The audit data available in big data platforms can be democratized to several consuming audit applications, thereby making quintessential data available for audit practitioners just by a click of a button. These platforms support the need for practitioners to “self-serve” audit data in the format and structure they want. This way, the auditors can extract real-time actionable insights, drive decision-making, reduce the audit turnaround time by 30%, and deliver high-quality audit service.

2. Advanced Data Visualization Capabilities

Enterprises very often encounter the risk of errors, manipulation, and deviation in audit data.  Data visualization helps identify and assess such risks effectively through dashboards – audit quality dashboards (to measure auditor’s deliverables), audit platform dashboards (to monitor backlogs, defects, and progress of individual members and the team), and sprint performance dashboards (to evaluate audit team’s performance by obtaining a single view).
A good data platform empowers auditors with the visualization of large volumes of audit data housed in big data platforms in an easy, consumable way. It can help auditors:
  • Understand clients’ transaction trends
  • Locate anomalies in transactions
  • Evaluate different types of transactions
  • Drill down each transaction or a group of transactions to understand practitioner behavior
  • Observe and analyze client needs better
  • Understand audit performance better and thereby enabling faster audit turnaround time.
For instance, auditors may find it strenuous to identify any unusual activity or errors in the journal entries of organizations that handle large numbers of transactions. With data visualization, audit teams can monitor the number of transactions recorded by each accounting clerk and spot differences in the entries processed by each of them.

Another Use Case:

Enterprises can calculate their return on investment (ROI) based on historical patterns, repetitive transactions, and activities. And there are instances where they don’t achieve the desired results. In such cases, forecasting, an element of data visualization, help auditors identify the accounts that caused the differences between forecasted and actual ROI.

Further, deploying modern visualization platforms like Apache Superset, Power BI, and Tableau provides advanced data visualization capabilities and allows real-time monitoring of audit engagement processes and workloads of auditors. This way, auditors can make relevant decisions spontaneously.
These graphical representations of data also increase operational efficiency by evaluating auditors’ performance.

3. Intelligent Document Processing

Verifying client documents is a major part of the audit process that has predominantly been manual. But this manual document scanning and paragraph matching with financial statements done by auditors can be time-consuming and error-prone
AI utilizes Named Entity Recognition (Natural Language Processing technique) and helps auditors automatically scan through client documents, identify, match, and extract relevant details or sections from the document based on the key phrases provided by auditors. Named-entity Recognition helps recognize and classify significant pieces of information from large client documents based on a set of benchmarks and factors such as business risks and leadership changes
Auditors can search, identify and extract relevant content contextually from the client documents – service agreements, legal contracts, regulatory filings, phone transcripts with investors, meeting minutes, annual reports, and more – through a scalable SaaS (Software as a Service) solution that leverages machine learning and NLP. Based on these recommended contextual sections from the documents, the auditors can take further action.
With the AI and ML model, auditors can also get additional information from sources like voice recordings, emails, and contracts and need not just rely on supporting evidence provided by the clients.
Therefore, auditors can be relieved from the process of manually reading and extracting details from client documents and reduce the time taken to understand the documents while ensuring accuracy.

Key Considerations for Embracing AI-driven Data Analytics in Audits

Although AI-driven data analytics can accelerate audit processes, it may come with certain bottlenecks that need to be addressed intelligently by the auditors. Some of the things to bear in mind are:

● Ensuring Client Data Privacy

The audit practice predominantly runs on trust. The most important thing that auditors need to consider when implementing AI is the privacy of client data. An AI-powered auditing platform constantly requires a lot of good quality data to self-learn and drive intelligent decision-making during similar scenarios in the future. The Platform, therefore, requires access to the client’s confidential data that may not be easily provided by clients if there is no secure practice to safeguard the data. So, to properly govern client data and protect it against data breaches, auditors need to take proactive security measures. Implementing a robust data anonymization process to anonymize Client PII (Personally Identifiable Information) before data ingestion can help eliminate this concern. This process complies with client privacy as there is no concern about the AI model ingesting Client PII information and causing any breach of trust.

● Confirming the Reliability of Training Data

The fact that AI is trained on datasets to function properly raises another concern. What would happen if the datasets are not accurate and not reliable? AI may not generate the intended results and deliver inaccurate predictions if the training data is not of good quality. Auditors need to take proactive steps – validating input data, keeping an audit trail, and so on – to ensure that captured data from client systems are reliable and accurate. Otherwise, AI will become dysfunctional.

A Case in Point:

For example, an AI tool is designed to classify client documents as financial data or human resources data. But if a major percentage of the training data is financial data, there is every chance that the AI would wrongly predict the majority of the documents as financial data most of the time.

● Delivering Explanations for AI results

AI can deliver accurate predictions and desired results. However, auditors need to develop the required processes to monitor their AI audit tool, to explain why the tool has identified certain transactions as “anomalous” and “unusual” and justify AI’s results. But what comes as a challenge here is that AI might have taken a combination of factors to arrive at the respective conclusions, and this can be quite complex. Although AI delivers accurate results, the auditors would still need to apply their professional judgment to validate these conclusions.
And this requires auditors to extend their expertise beyond auditing and accounting and familiarize themselves with data analysis, IT, and more.
Wrapping up, Data Analytics and AI help accelerate and fine-tune audit processes. However, it is necessary to perceive these solutions as accelerators and not as a replacement for auditors. AI-powered audits require little human intervention around repetitive and rules-based tasks and allow auditors to focus more on high-risk transactions. This makes the entire audit process efficient, reducing the auditors’ workload, operational costs, and back-and-forth communication between clients.
We at KANINI, help audit companies drive their digital transformation efficiently by building robust data platforms and AI solutions. Reach out to us to learn more about our data analytics and AI services for audits.

Anand Subramaniam

Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.

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