How to Build CAM Reports Faster Using Automated Bank Statement Analysis

A Credit Appraisal Memorandum is the backbone of any lending decision. It brings together bank statement analysis, GST returns, credit bureau data, and financial ratios into a single document that tells the lender whether a borrower is creditworthy or not.

The problem? Building one manually is slow, error-prone, and not a great use of a credit officer’s time.

Most teams still start with a stack of PDFs (scanned bank statements, GSTR filings, CIBIL reports) and spend hours extracting data, building spreadsheets, running calculations, and writing up findings. A complex application with multiple bank accounts and two years of GST data can take the better part of a day. For DSAs handling high volumes, NBFCs under pressure to turn around decisions quickly, and CAs managing multiple client files at once, this is one of the clearest workflow bottlenecks in financial services.

What Goes Into a CAM Report

Before looking at how automation helps, it’s worth mapping out what actually needs to go into a thorough credit appraisal.

At a minimum, a CAM covers: income and cash flow analysis, monthly average balance trends, Fixed Obligation to Income Ratio (FOIR), check bounce history, counterparty activity, loan repayment behaviour, GST compliance and turnover, and a credit bureau summary. For MSME borrowers, the cross-verification between GST filings and bank data is often where the real picture emerges. What a business claims on its GSTR returns and what actually moves through its current account don’t always match.

Pulling all of this together, across multiple documents, formats, and date ranges, is where the time disappears.

Why Manual CAM Preparation Slows Down Credit Decisions

The core issue isn’t analyst capability. It’s that the inputs are fragmented and unstructured.

A scanned bank statement from HDFC looks nothing like one from Axis Bank. GSTR filings come in their own format. Credit bureau PDFs from CIBIL differ from those sourced through Equifax or Experian. Before any analysis can begin, someone has to extract and normalise all of that data by hand.

Then come the calculations. FOIR, average monthly balance, OD utilisation, cash flow trends. Each requires pulling numbers from multiple sources, running formulas, and checking outputs. If an applicant has accounts across four banks, you’re doing this four times over, then reconciling everything.

Transposition mistakes, missed transactions, overlooked bounce charges, any of these can skew the final assessment. Automation doesn’t solve this by replacing the analyst. It solves it by eliminating the setup work that shouldn’t require one.

How Automation Changes the CAM Workflow

Good automation doesn’t replace the credit officer’s judgment. It removes the drudge work so that judgment gets applied to what actually matters.

Step 1: From Scanned Statements to Structured Data

The first and most time-consuming step in CAM preparation is turning unstructured documents into usable data. Precisa handles this across 1,200+ bank formats covering 850+ banks, including scanned PDFs. Statements are parsed, transactions are extracted, and the data is categorised automatically.

The platform also runs authenticity checks at this stage. It flags suspicious PDFs: unexpected font changes, creator or producer mismatches, and balance computation discrepancies. This matters because document tampering is a genuine risk in manual lending processes. Catching it at the document level rather than at disbursement is a meaningful safeguard, and one that’s almost impossible to do consistently at volume without automation.

Step 2: Financial Analysis Without the Manual Work

Once the data is structured, Precisa generates the metrics a CAM report needs: the Precisa Score (a creditworthiness score from 0 to 1,000), volatility score, FOIR, monthly average balance trends, OD and cash credit utilisation, cash flow analysis, bounce check history, loan repayment patterns, and counterparty activity.

These aren’t outputs you calculate separately. They’re generated as part of the analysis, presented in an organised dashboard, and available for download in Excel format for direct inclusion in the CAM document.

In practice, this means a credit officer or DSA can review a complete financial picture in minutes rather than hours. The DSA case study makes this concrete: end-to-end manual processing was taking two hours per application. Precisa brought that down sharply, allowing the same team to handle significantly higher volumes without a drop in accuracy.

Step 3: Cross-Verification Across Multiple Data Sources

A single-source CAM is incomplete. The most reliable appraisals reconcile bank data with GST returns and credit bureau reports, because inconsistencies between these sources are often where risk hides.

Precisa’s cross-analysis feature does exactly this. It compares GST sales and purchase data against bank account activity for the overlapping period, identifies customers and suppliers that appear in both, and flags discrepancies. A borrower claiming ₹80 lakh in turnover through GST filings should show corresponding inflows through their bank. If those numbers don’t line up, it’s worth understanding why before a credit decision is made.

The credit bureau integration adds another layer. Precisa connects to bureau data sources, pulls the report, and maps loan history, Days Past Due (DPD) patterns, EMI obligations, and inquiry ratios alongside the bank statement analysis. The result is a 360-degree view of the borrower rather than a partial one.

Where Precisa Fits In

Precisa is the analysis engine that feeds the credit appraisal. It handles extraction, analysis, and cross-verification that currently happens manually, then outputs structured data that feeds directly into the credit memo. 

For teams that already have a Loan Origination System (LOS), Precisa integrates via API. For those without one, the web application works as a standalone portal. More than 1,000 clients across 25+ countries use it for credit underwriting, forensic investigations, AML compliance, and financial advisory work.

The platform also supports Account Aggregator integration, which means that for borrowers who consent, bank and GST data can be fetched directly from the source rather than relying on uploaded documents. That eliminates the document manipulation risk at its root.

The Results Teams Are Seeing

Across credit underwriting and DSA use cases, the consistent finding is the same: the analysis itself isn’t the hard part. It’s the setup and data extraction that eat time. Removing that step changes what’s possible.

A forensic audit firm using Precisa for financial investigations reduced the investigation turnaround from 30–45 days down to 25–30 minutes. The underlying capability, transaction-level analysis across multiple bank accounts, applies directly to CAM preparation. 

For NBFCs and DSAs handling MSME loan applications, faster CAM turnaround translates to more applications processed with the same team, shorter wait times for borrowers, and better accuracy because the analysis is automated rather than hand-calculated. In markets where processing speed and risk quality both matter, these are meaningful gains.

If your team is still building CAM reports manually from scanned statements, it’s worth seeing what automated analysis looks like in practice.

See how Precisa builds your CAM analysis automatically. Start with a free bank statement analysis now.