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
- 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
● Confirming the Reliability of Training Data
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
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.