CAM Report Structure: The Financial Data Points That Strengthen a Credit Decision

A Credit Appraisal Memorandum (or CAM report) is the internal document a credit team prepares before recommending or rejecting a loan. Its job is to tell the credit committee, with documented evidence, whether a borrower can repay. Every section in a well-built CAM exists to answer some part of that question, and the sections that tend to fail a credit decision are the ones relying on incomplete or single-source data.

NBFC credit in India is expected to grow at a compound annual growth rate of 15 to 17% between fiscal 2024 and 2027, according to CRISIL MI&A. That growth puts pressure on credit teams to process more applications without relaxing underwriting standards. The CAM is where that tension gets resolved, and the data points you include, or miss, determine which way the decision goes.

The Data Layers Every CAM Report Needs to Be Credible

A reliable credit appraisal memorandum draws from at least three independent data sources: bank statement analysis, GST returns, and credit bureau data. Each answers a different question about the borrower’s position. Together, they either corroborate each other or they reveal a gap, and that gap is often where the risk actually sits.

Treating any single layer as sufficient is where CAMs go wrong. A borrower can show strong GST turnover figures while maintaining a chronically low bank balance. A bank statement can show stable inflows while a credit bureau report reveals undisclosed loan obligations that push the actual FOIR well past a safe threshold. A single-source appraisal cannot catch that.

Bank Statement Data: Cash Flow Over Declarations

The bank statement is the most operationally honest document in a loan file. It is harder to manipulate than a profit-and-loss statement, harder to inflate than a GST return, and it captures behaviour rather than declarations.

The data points from bank statement analysis that carry the most weight in a CAM:

Monthly Average Balance

Not the closing balance on a single date, but the maintained average across the entire statement period. A borrower who shows a strong balance in the most recent month but erratic averages over the preceding 12 months presents a fundamentally different risk picture than one with consistent balances throughout.

Cash Flow: Inflow Against Outflow, Month by Month

The CAM should record total inflows, total outflows, and net cash flow for each month in the statement period. Sudden spikes in inflow that do not correspond to visible business activity in the GST data warrant an explicit note in the appraisal.

Loan Obligations From the Transaction Data Itself 

Most credit assessors check the bureau report for existing loans. Fewer cross-reference it against the bank statement. Recurring debits to named lenders, NEFT payments at regular intervals, and ECS mandates all appear in the transaction history and can reveal informal or undeclared credit obligations that the bureau report does not show. These matters for FOIR calculation more than the declared EMI figure the borrower provides.

Bounce Activity and OD/CC Utilisation

These two metrics together give a reasonable picture of financial stress. A borrower with occasional bounces during a period of low average balance reads differently from one with frequent bounces despite an active overdraft facility. 

GST Return Data: Verifying the Business’s Declared Turnover

For MSME lending and business loan applications, GST data provides the most reliable independent check on declared turnover. Where a borrower claims ₹1.5 crore in annual sales, the GSTR-1 and GSTR-3B filings should show corresponding figures. Where they do not, the CAM needs to document that discrepancy before any recommendation is made.

The specific data points from GSTR analysis that belong in the credit appraisal:

Net and Gross Turnover, Month on Month. 

Not just the headline annual number but the monthly trend line. A business with two strong months and ten weak ones presents a different repayment risk than one with stable monthly billings throughout the year.

GST Compliance Rating. 

This reflects how consistently and punctually the entity has filed its returns. A borrower who habitually files late, even if they do eventually file, is giving the lender a useful signal about how they manage financial obligations generally. 

Cyclic or Intra-Organisation Transactions

GST data can reveal whether a borrower’s declared turnover includes significant sales back to related entities. Circular billing between group companies is a known method of inflating revenue figures for the purpose of meeting loan eligibility thresholds.

Buyer and Supplier Concentration

A business whose entire turnover flows through one or two customers is more vulnerable to income disruption than one with a distributed client base. The CAM should flag high dependency on a small number of counterparties as a credit risk, not just a business observation.

Credit Bureau Data: Reading Repayment Behaviour Over Time

The bureau report answers the question the bank statement cannot: how has this borrower behaved with other lenders?

Days Past Due (DPD) history is the most diagnostic figure here. A single DPD incident in a five-year record is different from a pattern of DPDs across multiple lenders in the past 24 months. The CAM should record both the count and the recency of DPD incidents, not just their presence or absence. 

Enquiry ratios also deserve attention. A borrower who has submitted six loan applications in the past three months but converted only two of them is either facing repeated rejections or carrying debt across multiple lenders simultaneously. Both possibilities affect the credit decision.

Settlement records and written-off accounts carry weight that a headline credit score does not always reflect. An account settled for less than the outstanding amount means a lender absorbed a loss. That history should appear explicitly in the CAM, not buried in a footnote.

Cross-Verification: Where Discrepancies Actually Appear

A CAM report that presents bank data, GST data, and bureau data in separate sections has documented the inputs. A CAM that cross-references them has found the actual insight.

The reconciliation between GST-declared sales and bank-received inflows is the most diagnostic comparison available. Where those two numbers align reasonably, the declared business activity is supported by transaction evidence. Where they diverge significantly, either the turnover is overstated in the GST filing or cash collections are happening outside the declared account. Both scenarios require explanation before the recommendation is finalised.

The same logic applies to EMI obligations. A ₹25,000 monthly EMI declared by the borrower alongside ₹42,000 in recurring outflows traceable to lenders in the transaction data means the declared FOIR is understated. The credit committee should be working from the bank statement figure, not the declared one.

How Precisa Handles CAM Report Analysis

Pulling data from three sources and cross-referencing them manually is where most credit teams lose time. Precisa automates this across all three layers:

• For bank statement data: Calculates monthly average balance, OD/CC utilisation, overdrawn days, and sanction limit breaches automatically across the full statement period.
• For GST data: Fetches GSTR-1 and GSTR-3B either from uploaded documents or directly from the GSTN server, then generates a compliance rating from the filing timestamps. This is the same compliance data that belongs in the CAM.
• For credit bureau data: Maps DPD history alongside active loan data, inquiry patterns, and settlement records in a single consolidated view.

The cross-analysis output reconciles bank inflows against GST-declared turnover for the overlapping period, catches discrepancies that would otherwise require manual calculation across multiple spreadsheets, and flags counterparties that appear in both sources. That reconciliation is what a CAM recommendation should be built on.

See What Your CAM Report Is Missing

Most credit teams discover their biggest data gaps after a loan has already gone wrong. Precisa’s cross-analysis runs bank statement data, GST returns, and bureau data against each other before that happens, and the first three analyses are free. Try Precisa free.