In an interaction with Finance Outlook Indi, Praveen Subramanya, Managing Director of IACVS – India Chapter and Director at Wyz Appraisers Pvt Ltd., shares insights on how artificial intelligence and real-time data integration are revolutionizing SME business valuations in India, addressing long-standing challenges of incomplete records, informal transactions, and subjective methodologies while enabling faster, more reliable decision-making for lenders, investors, and M&A advisors.
Praveen is a distinguished valuation expert and financial professional with over 23 years of extensive experience spanning real estate, business valuation, and credit risk management. He is a Member of RICS, UK (Ex Global Governing Council Member), a Chartered Engineer, Registered Valuer, and Certified Business Valuation Specialist (IACVS) USA. At IACVS – India Chapter, Praveen leads strategic initiatives while also serving as Director at Wyz Appraisers Pvt Ltd., Registered Valuers. His illustrious career includes senior leadership roles at Knight Frank (India) as Director – Valuations and Advisory, Head of Risk at Edelweiss Capital, and Head – Technical (Valuations) at IIFL Group, where he managed portfolios exceeding $3 billion. He has been part of expert working groups on Valuation Standards and ESG at RICS, UK, and has contributed to guidelines for valuations with IBBI, Government of India.
How is the integration of real-time financial and operational data improving the accuracy and reliability of SME business valuations in India?
Real-time financial and operational data is somewhat of a misnomer—we don't have true real-time data except for listed stocks on the SME exchange. However, obtaining financial and operational data for the valuation date significantly improves accuracy and reliability.
Integration alone doesn't help. You need valuers following proper valuation standards, which is improving with increased data availability. AI-based software makes this integration possible, ensuring valuation standards aren't taken lightly, thereby improving reliability for SME business valuations.
When data becomes more accessible and current, valuers make more informed judgments. Better data combined with proper standards creates a foundation for accurate SME valuations. We're witnessing a shift from estimation-based to evidence-based valuations, where financial data previously gathered over weeks can now be accessed within hours.
What role does AI-driven predictive analytics play in assessing future cash flows and risk factors for SMEs, particularly in volatile or underserved sectors?
AI-driven predictive analytics works on historical company and sector data, mimicking growth or degrowth patterns in projections. When companies perform very differently than similar entities, predictive analytics currently err. But with more data, this will improve to generate reasonably accurate future cash flows. However, human oversight remains essential to validate predictions.
Risk factors differ significantly across SMEs due to varying business models. Risk factor capturing in AI-driven predictive analytics is currently an art that experienced valuers handle, but this will improve over time.
Accuracy levels for volatile or underserved sectors are relatively low now. However, India is becoming a data hotbed with multiple companies contributing information. Once AI-based predictive analytics strengthen here, where data is plentiful, tools tested in India can be used globally—it's "Make in India" for the world.
How are Indian SMEs leveraging automated data aggregation tools to overcome challenges such as incomplete financial records and informal transactions during valuation?
Data is plentiful in India but often incomplete, especially for SMEs. Informal transactions are difficult to track during evaluation. Automated data aggregation tools still have far to go before performing adequately.
However, in recent years, Income Tax, GST, and the Ministry of Corporate Affairs have been collecting financial records consistently. When you combine these multiple data points, incomplete information gets reasonably filled for valuation purposes. This data integration from multiple sources covers most valuer requirements.
Informal transactions remain challenging to track automatically. Despite government controls, Indians are innovative, and informal transactions persist. While I'm optimistic about improvement, informal transactions have existed in the country for a very long time, so the timeline remains uncertain.
Also Read: Major Challenges in Mortgage Loan Valuation Process
In what ways are AI-powered valuation models reducing dependence on traditional, subjective valuation methods and enabling more consistent, data-driven outcomes?
Renowned valuer Aswath Damodaran calls valuation a craft—part science, part art. The science part was diluted earlier due to lack of consistent, reliable data. India lacks transparent land records for transaction values, and company filings may not be consistent despite auditor oversight.
Valuers traditionally depended heavily on available data to make assumptions. With AI-powered valuation models, data availability is improving, making the valuation process more consistent and strengthening the subjective art component.
AI enables valuers to deliver more reliable valuations because data can be tested for consistency, which was previously difficult. AI-powered models are reducing subjectivity substantially, but subjectivity will remain—the art part cannot be AI-enabled. Since AI handles the science part, reliability is increasing with AI-powered valuation models.
What regulatory or compliance considerations must SMEs and valuation professionals keep in mind when using AI and real-time data for valuation under Indian financial guidelines?
Regulations are evolving, and regulators have far to go in framing consistent guidelines. Courts have rejected valuation reports using freely available AI software like ChatGPT or Perplexity. In Australia, courts require disclosing AI use in valuation reports with client agreement before sign-off. In India, Deloitte was fined and had to return fees for using AI without client expectations.
Valuers remain responsible for reports. Key considerations when using AI: First, available AI can hallucinate significantly because it hasn't completely understood numbers. Don't use free software prone to hallucination, and always verify numbers before signing off.
Second, data entered into freely available AI software has unknown storage locations. Confidentiality arrangements between clients and valuers prohibit public disclosure of client data. These protections don't exist with free software. Use proprietary software with known data storage and sources for high reliability.
Always disclose AI use, specifying where it was used and how reliability was tested. Discuss AI usage with clients before signing off. Avoid free software for reliable data. With proprietary software, check confidence scores, apply judgment, and detail everything in reports. Current guidelines are limited to international valuation standards. Valuers must self-regulate, documenting all processes and disclosing facts to avoid future trouble.
How can lenders, investors, and M&A advisors use AI-enabled valuation insights to make faster, more informed decisions in SME financing and deal-making?
Many lenders, investors, and M&A advisors already use this, and adoption will increase. M&A advisors and investors both bring money into companies. They check similar company valuations and sector growth using aggregated data to provide target company insights.
AI agents can analyze entire sectors by aggregating data in seconds—work that previously took weeks. Today, AI collects data, aggregates it, and presents it instantly for analyst review and conclusions. This time-saving means more investments and M&A activities can occur, positively affecting the economy.
Lenders use AI differently. They expect more safety than investors and M&A advisors, using AI to understand debt ratios and financial issues based on historic performance. They use it before and after funding for periodic monitoring.
The Reserve Bank of India requires monitoring all lent assets and companies, with periodic data collection and monitoring reports. AI makes these reports much faster. Banks must analyze thousands or lakhs of companies they've lent to—a difficult task without sufficient valuers or staff.
AI-enabled insights help lenders meet RBI compliance requirements and make strategic decisions. If borrowers underperform, preventive measures can prevent non-performing assets. While not completely available or reliable today, AI will become reliable soon. Lenders, investors, M&A advisors, and fund houses will increasingly use AI-enabled valuation insights for SME financing, deal-making, and monitoring. The future looks bright.
