The world is evolving rapidly and what we see, and experience today were once just some random dreams or mere imagination a decade ago. The speed at which the changes are occurring around us highlights the need to keep ourselves updated. Technological changes such as the introduction of Artificial Intelligence are such a change that must be acknowledged and embraced for a better tomorrow.
AI or Artificial Intelligence is a
field of computer science that focuses on producing machines and software, proficiency
in carrying out tasks that naturally require human intelligence. While the
practicality of a complete adoption of human intelligence doesn’t seems to be
practical, in most of the cases the AI has been able to mimic the same. One
area where the application of AI can lead to significant improvements is
auditing.
There is one quote from an
anonymous person, which goes like this "Internal auditors must be creative
and innovative." The quote highlights the need of an auditor to be
adaptive to the changing circumstances and find new and innovative ways to do
the work effectively. Conventionally driven by manual reviews and sample-based
testing, the functions of internal audit are now undertaking a transformative
change with the incorporation of Artificial Intelligence (AI). AI tools are
transfiguring the way internal audits are conducted—making them more practical,
competent, and data-driven.
Just like how the world
is evolving with the introduction of Artificial Intelligence, the same is
happening in the Banking Sector. The financial ecosystem in India is becoming more
and more multifaceted, with immense volumes of data produced daily across
branches, digital channels, and back-office systems. In the growing landscape
of Indian banking, internal audit has emerged as a critical function to ensure
sound governance, operational efficiency, and regulatory compliance.
Internal auditors face
the challenge of evaluating thousands of transactions, assessing complex risks,
and warranting compliance with dynamic regulatory frameworks issued by bodies
like the Reserve Bank of India (RBI), Pension Fund Regulatory and Development
Authority of India (PFRDA), National Pension Corporation of India (NPCI) and
the Securities and Exchange Board of India (SEBI). In this context, AI arises
as an influential enabler that can support auditors in dealing with data burden,
enhancing analytical abilities, and bringing real-time insights.
AI tools can be applied effectively at various stages of the internal audit lifecycle in Indian banks:
Audit Planning & Assessment of Risk
One
of the major challenges banks face today is assessing risk within the
organization, including across their branches. With varied financial products and various trends in
financial sector the chances of risks are also becoming high. Audit plans
therefore need to be dynamic to address the risk. The audit plan must be able
to determine the chances of high likelihood of NPAs at any particular branch or
the areas or customer segments which are prone to fraud.
Advanced analytics platforms driven by machine
learning algorithms can evaluate past audit reports, transactional data,
customer profiles, and regulatory alerts to find high-risk areas. These
understandings help internal audit teams prioritize audit coverage and their
resources on critical business units/ branches/ functions that show signs of
irregularity or non-compliance.
Tools such as ACL Analytics/ Galvanize (now part of diligence) and MindBridge AI Auditor etc. help the organization in analyzing full data set and detects anomalies by flagging high risk transactions.
Data Collection and Fieldworks
Another time consuming and tiresome task in auditing
is Data Collection. Many a times the auditor loses his cool at this stage. The
unavailability of data or delayed availability of data results in the quality
of the audit report.
AI simplifies this process through Robotic Process
Automation (RPA) tools such as UiPath and Automation Anywhere. These tools help
the bank in extracting the data from CBS, create required reports, and combine
information across multiple platforms without manual intervention.
Another issue in Data collection faced by the auditor is reading and interpreting unstructured data such as emails, policies, circulars etc. and identifying the required information out of it. Bank can use Natural Language Processing (NLP) tools to extract and analyze information from such documents.
Sampling and Testing of Samples
The report of an auditor is as good as his sample size
is. Sampling thus plays an important role in auditing. If the sample is flawed,
the entire report may end up being a waste of time. The biggest issue therefore
before the auditor is to select the most appropriate and accurate sample which
can represent the audit universe. AI significantly enhances this stage by
enabling constant and all-inclusive audit testing. Instead of checking a small
sample, AI algorithms can assess 100% of transactions across the organization,
identifying irregularities and flagging doubtful activities.
Process mining tools like Celonis can reconstruct the actual flow of business processes and compare them against defined procedures. Any deviations from the expected path are highlighted for further investigation, allowing auditors to evaluate the efficiency of internal controls with greater accuracy.
Reporting and Communication
Presentation of the findings/ observations plays a
significant role in auditing. No matter how much effort you put
into finding the observation, it won’t have an impact unless it’s presented in
an appealing and effective manner. It helps the management in understanding the audit findings and aids
them in taking informed decisions.
Using Natural Language Generation (NLG) abilities, AI tools can translate composite datasets into narrative summaries, producing draft audit reports with minimal human input.
Follow-up
Follow-up is another crucial area in auditing. While
many people think that audit ends with reporting and communication, follow-up
plays a critical role in internal control. It ensures that the errors or issues
observed during the audit are addressed and not repeated in the
future.
AI enables continuous auditing by monitoring controls
and transactions on a real-time basis. When certain thresholds or rules are
breached, alerts are generated automatically for review by auditors.
While the integration of AI into internal auditing
offers multiple benefits including enhanced accuracy, scalability with respect
to handling of multiple and complex database, improved efficiency, early
detection of risks and compliance to regulatory/ statutory guidelines, it also faces
several challenges. The most important and critical of them being the data
privacy and security. Any mishap in handling customer’s sensitive data can lead
to severe consequences. The second important challenge is regulatory uncertainty.
While the world is moving towards an AI driven economy the regulatory framework
in India with respect to AI is still in its evolution stage. Furthermore, there
are instances in every area which require some human interface. Nothing can be
assessed in ones and zeros. Being a system driven application, the human
characteristics which are also important in auditing are absent in AI.
To conclude, as banking operations are becoming more
digital and data-intensive, the use of AI is not just an option but a
requirement to maintain audit quality, spot evolving risks, and ensure
regulatory compliance. While challenges persist, mainly in terms of system
readiness and auditor skills, Indian banks that advantageously invest in
AI-driven audit functions stand to benefit from robust governance, enhanced
efficiency, and greater trust in their operations.
