Why India’s Working Capital Is Still Stuck on Paper

(This is the second part of a two-part series on working capital. The first part examined how India’s working-capital system focuses on bank limits, not trade credit.)

If the central weakness in working-capital finance is visibility, the solution does not lie in new ratios or thicker rulebooks. It lies in making better use of data that already exists.

India’s B2B commerce now runs largely on digital rails. GST captures invoice-level B2B transactions. NEFT, RTGS and UPI generate timestamped payment records. Consent-based data-sharing frameworks are emerging through the Account Aggregator architecture. Yet working-capital appraisal remains largely document-driven.

For all the regulatory reviews, committees and circulars issued over the years, the core information problem remains unresolved. Financial statements used in appraisal are often six to twelve months old. Receivables appear as book entries rather than reflections of actual collection behaviour. Operating cycles are borrowed from industry averages. Risk is priced on collateral and balance-sheet strength, with cash-flow conduct treated as an estimate.

The outcome is familiar. Limits are sometimes too tight, creating operational strain. At other times they are too loose, raising diversion risk. Disputes over drawing power recur. Monitoring becomes procedural rather than insightful.

A transaction-backed approach would begin with something conceptually simple: pairing invoices with corresponding payments. Once invoice reference numbers and settlement trails are linked, each trade transaction can be traced across its life cycle.

Behaviour Signals
First, receivable and payable cycles could be calculated from observed conduct rather than estimated operating cycles. Limits could be aligned with verified turnover and collection behaviour.

Second, fraud risk would decline. Inflated receivables and inventory, circular trading and fictitious sales lose credibility when unsupported by payment behaviour. The RBI’s Annual Report for 2021 noted that the average lag between fraud occurrence and detection is about 23 months, stretching to nearly 57 months for large cases. Much of this delay reflects the ease with which receivables, payables and inventory can be manipulated within conventional reporting systems.

Transaction-level verification would shorten that lag.

Third, risk differentiation would sharpen. Firms that consistently honour payment timelines would build behavioural credit histories. Chronic delayers would carry visible reputational signals.

Over time, this would reshape incentives across supply chains. Payment discipline would no longer be a private matter between two firms. It would influence access to finance more broadly. Better behaviour would translate into stronger supplier confidence and improved bank terms.

The same data foundation could support dynamic B2B credit scoring, where creditworthiness updates continuously based on real payment conduct. This matters in an economy where delayed payments remain one of the biggest frictions in business. Firms with strong records would gain faster and cheaper access to finance.

Virtuous cycles could emerge: better discipline improves transactional liquidity, which supports timely settlement, which strengthens credit histories.

The benefits would not be confined to banks. NBFCs, fintech lenders and trade creditors could compete more effectively for reliable borrowers if transaction-level intelligence were widely usable within a consent-based framework. Cash-flow planning would improve, disputes would diminish, and financing would become more predictable and real.

Regulatory supervision could also become lighter yet sharper. Instead of reviewing voluminous statements and audit reports, supervisors could rely more on authenticated transaction signals. Compliance might become simpler, even as oversight becomes more precise.

None of this is costless.

Pairing invoices with payments is technically complex. Businesses use multiple payment modes. Settlements may cover several invoices. Partial payments, advances and credit notes complicate reconciliation. ERP systems differ widely in sophistication.

A pragmatic pathway would begin with larger firms, especially those already integrated with GST e-invoicing. Standardised APIs could embed invoice reference numbers into payment instructions. Automated reconciliation could then match GST data with bank records accessed through the Account Aggregator framework.

As infrastructure matures, coverage could expand to smaller firms through cloud-based tools. Advances in machine learning can assist in interpreting unstructured payment references and matching partial settlements. Over time, manual intervention would be confined to exceptions.

Policy attention will be crucial. Data protection frameworks must enable responsible, consent-driven sharing of paired invoice–payment data for legitimate financial purposes. The precedent exists in credit information sharing through established bureaus.

Macroeconomic Stakes
India continues to face a gap between formal credit supply and the liquidity needs of trade and industry. When working-capital assessment is backward-looking, credit either undershoots or misfires. Embedding real-time behavioural data could deepen credit inclusion without necessarily expanding risk.

It could also reduce systemic vulnerability. Long detection lags have historically exposed structural weaknesses. Transaction-backed monitoring would allow earlier anomaly detection. Liquidity stress would surface sooner. Corrective action could precede insolvency.

The broader prize is efficiency: a credit system that responds to actual payment conduct rather than estimated projections would allocate capital more precisely and optimally.

Working capital is often treated as routine banking. It is not. It is the bloodstream of commerce.

India has spent decades refining prudential norms, tightening supervision and expanding digital infrastructure. The missing link is integration — bringing invoice intelligence and payment behaviour into the heart of appraisal.

That shift would not merely tweak an existing framework. It would move working capital from paper limits to live liquidity dashboards.

Working capital does not fail for lack of credit models. It fails for lack of cash-flow visibility. When trade and payment flows become transparent, credit can finally flow where it should, on terms that reflect how businesses actually behave, not how their paperwork is arranged.