AI in Internal Audit: Benefits, Use Cases, Challenges & More

The use of AI in internal audit is becoming a reality. From banks and hospitals to IT companies and retail chains, organizations are using artificial intelligence to audit smarter and faster.

Explore with us how AI is transforming internal audits in India, the benefits, real-world use cases, industry examples, and how your business can get started — step by step.

What is Artificial Intelligence in Internal Audit?

AI in Internal Audit refers to the use of artificial intelligence tools and software to enhance the efficiency, accuracy, and scope of internal audit processes.

So, essentially AI is used in the internal audit process to make them but faster and smarter than traditional methods. 

AI assists human auditors in going through massive data sets by:

• Reading, sorting, and analyzing large amounts of data instantly
• Spotting unusual patterns or potential fraud
• Monitoring systems continuously instead of doing audits once a year

This enables auditors to focus on strategic decision-making and risk management.

Advantages of Using AI in Internal Audits 

AI is not only about making your tasks quicker, here’s a quick look at the benefits of AI in Internal audits: 

1. Faster, More Efficient Audits: By processing large datasets in seconds, AI accelerates audit cycles. Plus, it automates time-consuming tasks which frees up auditors to focus on high-value analysis.
2. Real-Time Monitoring: Instead of waiting for the year-end audit, AI monitors your data all the time. It can alert auditors to issues immediately, reducing operational or financial impacts.
3. Cost Savings: Setting up AI tools may cost money at first, but in the long run, they reduce labor, mistakes, and wasted time — saving money. 
4. Improved Risk & Fraud Detection:  AI can spot suspicious behavior, fake transactions, policy violations, or outliers through advanced analytics. It can predict emerging risks using ML models trained on historical data.
5. Scalable for Big Businesses: For bigger companies audits get harder. AI handles growing data volumes and organizational complexity without proportional increases in resources needed. 
6. Supports Compliance: With stricter laws in India (like those from SEBI or RBI), AI helps make sure you’re always in line with the rules — automatically.
7. Objectivity & Reduced Bias: AI applies consistent criteria to data analysis. This minimizes human subjectivity and strengthens audit credibility through transparent, data-backed findings.
8. Enhanced Stakeholder Reporting: It helps generate dynamic visualizations and actionable insights for clearer communication with management/boards. This supports data-driven recommendations for improving governance and controls.
9. Future-Readiness: Use of AI in internal audits prepares organizations for evolving risks by integrating AI-driven threat detection. This fosters innovation in audit methodologies, keeping up with digital transformation.

8 Important Use Cases of AI in Internal Audit in India

AI can and is being used by organizations in Internal audits for different aspects. 

Let’s take a look at  some real and practical use cases of AI in internal audit:

1. Banking & Financial Services: Fraud Detection

AI models trained on historical fraud patterns (e.g., phishing, money laundering) can be used to flag suspicious transactions. 

For example, if a customer transfers large amounts at odd hours to a flagged account, AI tools can detect this and raise an alert immediately.

Generative AI use cases in internal audit even include writing detailed summaries of unusual patterns found during transaction analysis — saving time for the audit team.

Continuous Monitoring in IT Companies

Big tech firms like Infosys and TCS need to audit thousands of system logs daily.

2. Manufacturing: Risk Scoring for Supply Chain & Operations

Factories use internal audits to manage risks like machine failure, quality control, or supply chain issues.

AI assigns risk scores based on past data, alerting teams to high-risk areas needing urgent attention.

For example: Tata Steel uses AI to predict machinery breakdowns, reducing unplanned downtime.

3. IT Companies: Continuous Monitoring

Big tech firms like Infosys and TCS need to audit thousands of system logs daily. 

With gen AI in internal audit, systems can now analyze logs in real time to detect policy violations, system failures, or unauthorized access.

This kind of continuous auditing was impossible with manual methods.

4. Pharma & Insurance: Automating Compliance Checks

In sectors like pharma and insurance, following rules from regulators like SEBI or IRDAI is a must.

AI is used to:

• Scan large batches of documents
• Cross-check entries with compliance standards
• Flag gaps in reports automatically

These are perfect examples of AI in internal audit helping firms stay compliant without hiring extra staff.

5. Telecom: Revenue Assurance & Fraud Prevention

The telecom industry deals with billions of calls, SMS, and data transactions daily.

This high volume makes it hard to track if all revenue is correctly billed and collected — a major problem known as revenue leakage.

AI in telecom internal audits helps by:

• Matching call records with billing systems
• Flagging underbilling or missed charges
• Detecting fake SIM card activations
• Identifying abnormal call behavior (SIM box fraud, etc.)

6. Retail: Inventory & Shrinkage Audits

Retailers lose lakhs of rupees every month due to inventory errors, theft, and shrinkage.

Manual stock audits are too slow to keep up with fast-moving inventory.

AI in internal audit for retail solves this with:

• Real-time inventory tracking using IoT + AI
• Identifying patterns in shrinkage across stores or SKUs
• Predicting restocking needs and preventing overstocking
• Cross-checking POS (point of sale) data with stock levels
• Detecting employee theft, return fraud, or false discounts 

For example: Reliance Retail uses RFID and AI to identify shrinkage (theft/damage) in warehouses.

7. Energy & Utilities: ESG Compliance Audits

Firms are increasingly under pressure to comply with environmental and social governance (ESG) norms. 

AI analyzes sensor data from plants to ensure adherence to ESG norms, verifying energy efficiency claims and tracking carbon emissions data.

Generative AI use cases in internal audit in this example is that it helps with drafting sustainability audit reports for stakeholders.

8.  Generative AI for Internal audit

Generative AI in internal audit can be used to create useful content and insights during audits. They are especially helpful in saving time and improving clarity during audits.

They’re used to:

• Create summaries of audit reports
• Draft audit observations
• Suggest recommendations
• Draft policy compliance reviews
• Translate technical findings into simple language for leadership
• Highlight risks based on unstructured data (like emails or contracts)

For example, tools like ChatGPT, Microsoft Copilot, or custom LLMs can help audit teams save time by auto-generating documentation or insights — making audits faster and smarter.

Challenges of Implementing AI in Indian Internal Audit

While leveraging AI in internal audit can offer transformative benefits, organizations in India face unique challenges in its adoption. Here are key hurdles:

• Data Quality & Availability

AI relies on clean, structured, and comprehensive data, but many Indian firms struggle with fragmented, siloed, or outdated/ incomplete data.

This confuses AI tools, makes analysis harder, and affects accuracy. Data cleanup is a major step before AI can be effective — and it’s often ignored.

• Outdated Systems & Integration

Issue: Outdated IT infrastructure which are common in PSUs, banks, and older manufacturing firms, limit compatibility with modern AI tools.

Plugging AI into these older systems can be technically tricky, time-consuming and risky (if systems crash or don’t match formats). 

Without smooth integration, the AI audit process breaks.

• Regulatory & Compliance Risks

India is currently dealing with evolving AI regulations (e.g., DPDP Act 2023) and sector-specific mandates (RBI, SEBI) create compliance complexity.

There is still no clarity about issues like – How AI decisions in audits should be reviewed or how audit data should be stored under new privacy laws (like the DPDP Act). 

• Skill Gaps & Change Management

AI is a new skill — and most internal audit teams in India aren’t trained for it.  This creates a huge learning gap.

Plus, upskilling teams is costly and time-consuming.

• High Implementation Costs

AI adoption requires significant investment in tools, data infrastructure, and talent. 

For small and mid-sized Indian companies, this can feel out of reach — even if it pays off in the long run.

• Cybersecurity Vulnerabilities

AI systems handling sensitive financial or operational data are targets for cyberattacks, especially in sectors like banking and telecom.

A breach in an AI-driven fraud detection system could expose customer transaction patterns to malicious actors.

Get in Touch With PKC’s Experts to Get Started

How to Get Started With AI in Your Internal Audits?

Adopting AI in internal audits requires a clear plan with phased implementation. Here’s a roadmap for the same: 

1. Mindset Shift

This is needed to ensure that the organization aligns with AI adoption and makes resources available for the same. 

Steps: 

• Evaluate data infrastructure, team skills, and leadership buy-in.
• Define clear goals 

2. Start with Data Preparation

AI performance depends on high-quality, structured data, so it becomes essential to ensure it is available. 

Steps:

• Consolidate data from different systems being used like ERP and CRM.
• Clean data and standardize formats.
• Ensure compliance with privacy regulations

3. Pilot Low-Risk, High-Impact Use Cases

Don’t try to automate your whole audit process on day one. Instead, choose one small Internal audit area that’s:

• Repetitive
• Data-heavy
• Low risk

Examples include vendor invoice checks, payroll compliance review, anomaly detection in expense reports, etc. 

4. Choose the Right Tools & Technologies

Find the right Look for tools and applications  that are built for internal audit, easy to integrate with your current systems and have vendor support.

Popular AI audit tools in India include  MindBridge, CaseWare IDEA, ACL, etc. You can also explore Indian AI startups offering industry-specific solutions.

5. Upskill Your Audit Team

Auditors don’t need to become AI engineers, but they should understand the basics of how the internal audit AI tools work and what they can and can’t do. 

You can start with basic data analytics and AI concepts or partner with training providers.

6. Integrate AI with Existing Workflows

Next is efficient integration of AI without causing disruption. Use APIs to connect AI outputs with traditional systems that you’re using.

Steps:

• Embed AI tools into current audit software 
• Redesign processes incrementally

7. Measure Success & Scale Gradually

Once your pilot project is live, use metrics like time saved per audit cycle, error reduction rate, anomalies detected, to assess its effectiveness. 

Gather feedback (including problems encountered) from the team.  If results are positive, scale the use of AI to more audit areas.

8. Address Compliance & Governance

As you grow your AI use:

• Ensure audit data is protected under Indian data privacy laws (like the DPDP Act)
• Clarify who’s responsible if AI makes a mistake
• Keep transparency in AI decisions (explainable AI)
• Document everything clearly in your audit policies.
• Regularly audit AI models for bias

AI + Human Auditors: The Collaborative Future

The future of auditing lies in synergy, not replacement. AI and human auditors will increasingly collaborate, each amplifying the other’s strengths:

AI’s Role:

• Processes vast datasets in seconds, identifying anomalies 
• Reduces errors in repetitive tasks 
• Flags risks  using historical trends

Human Auditor Strengths:

• Interprets context 
• Negotiates with stakeholders, communicates findings persuasively
• Ensures AI decisions align with regulations and organizational values

When working together, AI will handle data crunching, freeing auditors to focus on strategic analysis and advisory roles.

The future of internal audit in India will be hybrid — with AI acting as a smart assistant, and humans leading with experience and insights.

Frequently Asked Questions

1.  How is AI used in internal auditing?

AI is used in internal auditing to analyze large volumes of financial data, detect fraud and unusual patterns, and automate compliance checks. monitor transactions in real-time and flag risks based on past trends. AI speeds up the audit process and improves accuracy, especially in data-heavy industries like banking, telecom, and retail.

2. Is AI replacing human auditors in India?

No. AI supports auditors but doesn’t replace them. It handles data-heavy tasks, while humans make final decisions using judgment and experience.

3. What are examples of AI in internal audit?

Examples include: Fraud detection in banking, continuous monitoring in IT companies, shrinkage audits in retail, ESG compliance checks in energy firms, etc. 

4. Which industries in India are using AI in internal audits?

Different industries are using AI for internal audits in India including industries like:Banking & Finance, retail, telecom, healthcare, IT/ITES. etc.

5. Is AI expensive for internal audit?

Using AI in internal audits can be costly at the start, but over time, it saves money by reducing manual work and errors. There are also affordable AI tools for small and medium businesses.

6. How can AI be used ethically in internal audits?

Use AI ethically by being transparent, keeping human oversight, protecting data privacy, avoiding bias, and documenting AI-influenced decisions. This ensures trust, accuracy, and legal compliance.