Why Auditors Should Embrace AI

In today’s data-driven banking environment, Artificial Intelligence (AI) is no longer a futuristic concept — it’s a practical necessity. Statutory auditors are increasingly expected to navigate massive datasets, detect early warning signals, and evaluate compliance in real-time. Learning AI empowers auditors to:

- Enhance speed and accuracy in complex audits

- Automate repetitive tasks like classification, scoring, and analysis

- Gain insights from data patterns that aren’t easily visible manually

- Stay future-ready, aligned with tech-driven audit standards

In bank audits, where data volume is vast and time is limited, AI tools like Excel VBA macros and Python scripts become indispensable.

Challenges in Identifying NPAs in Bank Audits

Despite established frameworks like RBI's IRAC norms, auditors face several on-ground issues:

- Volume and fragmentation of data across systems and formats

- Missing or incorrect dates for interest payments or IRRGDT

- Unclear classification statuses (SMA, NPA, Standard) from core banking data

- Manual effort required to verify DPD (Days Past Due) across loan accounts

- Difficulty correlating loan type with sector (MSME, Agri) to apply proper audit judgment

Traditional tools are insufficient when thousands of loan accounts must be evaluated in tight audit timelines.

How We Used AI (VBA Macro) to Identify NPAs

To solve this, we deployed an AI-powered macro using Excel VBA that performs automated classification and scoring of loan accounts. Here’s the exact process:

Step-by-Step AI Audit Process

1. Input: Excel sheet with loan data including IRRGDT, STATUS, SMA_CODE, IRREGAMT, ACCTDESC.

2. Macro calculates DPD (Days Past Due) as of 31-Mar-2024 using IRRGDT.

3. Account Classification:

  - Uses STATUS if available

  - Else applies logic:

    - SMA_CODE = 1 → SMA-1

    - SMA_CODE = 2 → SMA-2

    - SMA_CODE ≥ 3 → NPA

    - Else → Standard

4. Flags Irregularities if IRREGAMT > 0.

5. Remarks Generation:

  - "🔶 NPA account" for NPA

  - "❗ EMI skipped > 3 months" if DPD > 90

  - Tag MSME or Agri loans using ACCTDESC

6. Marks Allocation:

  - 50 → NPA/mismatch

  - 40 → DPD > 90

  - 30 → MSME/Agri with DPD > 60

  - 100 → All others

7. Color Coding Rows:

  - Yellow for NPA

  - Orange for DPD > 90

  - Purple for MSME with DPD > 60

  - Red for Agri with DPD > 60

8. Output: Instant visual + data-based report with remarks and scoring.

This macro turns a full sheet of raw data into a ready-for-review analysis, saving hours of manual work.

Benefits of Learning AI in Audit

- Saves Time: Reduces effort by over 70% in loan classification and DPD analysis.

- Improves Accuracy: Consistent application of RBI logic across all records.

- Adds Value: Insights like risk scoring and visual flags elevate audit quality.

- Future-Readiness: Prepares auditors for AI tools, analytics, and RPA environments.

AI doesn't replace auditors — it amplifies their decision-making power.

Conclusion

The role of a statutory auditor is evolving. As banking grows digital and data volumes rise, embracing AI is no longer optional — it's critical. Using VBA macros or Python-based analytics, auditors can automate compliance checks, visualize high-risk areas, and report with confidence.

AI in audit is not about replacing professionals — it’s about equipping them to audit smarter, faster, and deeper.