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.
- 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:
1. Big Data Modernization for Operational Efficiency
2. Advanced Data Visualization Capabilities
- 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.
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.
3. Intelligent Document Processing
Key Considerations for Embracing AI-driven Data Analytics in Audits
● 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
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 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.