Banking Surrogate is a credit assessment method used by financial institutions, especially in retail and SME lending, to evaluate a borrower’s creditworthiness based on banking transactions rather than traditional financial documents. This approach is particularly useful for individuals or businesses with limited income documentation, such as self-employed professionals, micro-enterprises, or informal sector borrowers. By analyzing parameters like Monthly average balance (MAB), Credit turnover, Debit turnover, Cheque returns, and Transactional behavior, lenders can determine the borrower’s repayment capacity and financial discipline.
This method enables banks to extend credit to a wider customer base, including those who may not have formal income proofs like ITRs or audited financials. Surrogate banking data provides alternative insights into the financial health and cash flow patterns of borrowers. It’s especially popular in digital lending, where technology is used to extract and evaluate transaction data from bank statements. Overall, banking surrogate models promote financial inclusion and support faster, data-driven loan approvals.
Functions of Banking Surrogate:
-
Alternative Credit Assessment
Banking surrogates provide an effective alternative to traditional credit assessment methods, especially for borrowers lacking income tax returns or audited financials. By analyzing bank statements, lenders can evaluate cash flows, spending patterns, and financial discipline. This function allows banks to assess the repayment capacity of self-employed individuals, gig workers, and micro businesses who operate informally. It expands credit eligibility by using real-time transaction behavior instead of relying solely on formal documentation.
-
Enhancing Financial Inclusion
Banking surrogate models support financial inclusion by enabling lenders to offer credit to segments previously excluded due to lack of documentation. People with regular banking activity but no formal proof of income—like small vendors, freelancers, and daily wage earners—can now access credit based on their banking behavior. This function empowers underserved borrowers to become part of the formal financial system, promoting equal opportunity in credit access and reducing dependence on informal moneylenders.
-
Speeding Up Loan Approvals
With surrogate banking models, loan approvals become faster and more automated. Instead of waiting for extensive paperwork, lenders analyze bank statement data digitally using algorithms. This reduces turnaround time and enables instant or same-day loan approvals, especially in personal, SME, and digital lending. By simplifying the underwriting process, this function helps banks and NBFCs scale operations efficiently, improve customer satisfaction, and respond quickly to urgent funding needs.
-
Reducing Documentation Burden
Banking surrogate models reduce the documentation burden on both the borrower and the lender. Instead of collecting income proofs, balance sheets, or tax filings, the lender uses digital bank statement analysis as a substitute for financial records. This simplifies the application process, especially for informal and first-time borrowers, who may not have professional support for documentation. The function makes credit more accessible and borrower-friendly, especially for MSMEs and individuals in the gig economy.
-
Improving Credit Risk Evaluation
By using real banking data, lenders gain insights into actual cash flow patterns, such as income regularity, spending habits, and repayment trends. These insights enhance the lender’s ability to predict default risks more accurately than traditional models. Banking surrogates help detect early warning signs like frequent cheque bounces or irregular credits. This function leads to better-informed lending decisions, reduces non-performing assets (NPAs), and strengthens the overall credit risk management framework.
-
Enabling Custom Loan Offerings
Surrogate banking allows lenders to design personalized loan products based on the customer’s banking behavior. For example, customers with high monthly credits but low average balances may be offered short-term or overdraft loans, while stable salaried individuals might receive pre-approved personal loans. This function supports product innovation and credit personalization, enabling banks and fintechs to serve diverse customer segments with data-driven, tailored offerings that match their financial behavior and borrowing capacity.
Types of Banking Surrogate:
1. Bank Statement Analysis Surrogate
This is the most common type, where lenders assess a borrower’s bank account activity over a period (usually 6–12 months). Key indicators are:
-
Average Monthly Balance (AMB)
-
Monthly credits/debits
-
Number of inward/outward transactions
-
Cheque bounces
-
Salary or business income credits
This helps estimate income flow and repayment capacity.
2. Average Monthly Balance (AMB) Surrogate
In this model, loan eligibility is based on the Average Monthly Balance maintained in the borrower’s bank account. For example, an AMB of ₹25,000 may qualify a borrower for a personal loan of up to ₹2–3 lakh. This is simple and fast, used mostly for salaried or self-employed individuals with stable savings behavior.
3. Credit Turnover Surrogate
Here, lenders assess the total monthly or annual credits in the bank account, treating it as a proxy for income or business revenue. If a small trader has ₹10 lakh in annual credit turnover, they may qualify for a working capital loan. This is especially useful for micro and small businesses that may not have formal accounting systems but show strong bank inflows.
4. Salary Credit Surrogate
Used mainly for salaried individuals, this surrogate model evaluates the consistency and amount of salary credited in the borrower’s bank account. The higher and more regular the salary inflow, the better the loan terms offered. It’s common in digital lending and pre-approved offers for instant personal loans.
5. GST + Banking Surrogate (Blended Model)
For small businesses registered under GST, banks may combine banking data with GST returns to assess both turnover and tax compliance. This dual-surrogate model increases reliability of credit appraisal, and is popular among NBFCs and fintechs offering business loans to MSMEs.
6. POS/QR Transaction Surrogate
Used for merchants and retail businesses, this model considers digital payments received via Point-of-Sale (POS) terminals or QR codes. The regularity and volume of digital transactions become the surrogate for business income. This is increasingly used for small vendors and shopkeepers with minimal banking history but strong digital collection records.
7. Wallet Statement Surrogate
In fintech and digital lending, wallet usage patterns (Paytm, PhonePe, etc.) are sometimes used as banking surrogates. Frequent transactions, utility payments, and balance levels reflect customer behavior. Though not mainstream in banks yet, neo-banks and digital NBFCs use this for thin-file customers.
Challenges of Banking Surrogate:
-
Inconsistent Transaction Behavior
Banking surrogate models rely heavily on the borrower’s bank transaction history to assess creditworthiness. However, many individuals and small businesses exhibit inconsistent cash flows, irregular deposits, or lump-sum transactions that do not accurately reflect income stability. This variability makes it difficult for lenders to determine repayment capacity. Seasonal businesses or informal workers, in particular, may show spikes and dips in activity, which could lead to misjudgment of risk, resulting in either loan rejection or inappropriate credit limits.
-
Limited Financial Data for New Accounts
If a borrower has a recently opened bank account, there may not be sufficient transaction history to assess creditworthiness using a banking surrogate model. Lenders typically require at least 6 to 12 months of banking data for reliable analysis. This becomes a barrier for first-time borrowers, newly established businesses, or those who previously relied on cash transactions. The lack of adequate data restricts eligibility and may lead to delays or rejection of loan applications even for potentially creditworthy applicants.
-
Manipulated Transactions
Some borrowers may try to artificially inflate account activity—such as making multiple small deposits or transferring funds between accounts—to appear financially stronger than they actually are. Since surrogate models depend on the quantity and flow of transactions, this manipulation can mislead lenders into granting loans to high-risk customers. Without advanced data validation and fraud detection tools, banking surrogate analysis becomes vulnerable to such practices, thereby increasing the lender’s credit exposure and risk of default.
-
Lack of Standardization in Assessment
There is no uniform methodology across banks or NBFCs for evaluating banking surrogate data. One lender may prioritize average monthly balance, while another may emphasize credit turnover or number of transactions. This lack of standardization leads to inconsistent loan decisions and credit scoring outcomes. Borrowers may be accepted by one institution and rejected by another for the same banking behavior. This inconsistency reduces predictability and transparency in the lending process, undermining customer confidence.
-
Dependence on Technology and Automation
Banking surrogate models require automated tools to extract, analyze, and interpret transaction data efficiently. However, many financial institutions, especially in rural areas, lack adequate digital infrastructure or trained personnel to implement such systems effectively. Errors in data interpretation, mismatched formats, or system failures can lead to incorrect credit assessments. Heavy dependence on technology also raises concerns about data privacy, cybersecurity, and regulatory compliance, particularly when dealing with sensitive banking information.
-
Exclusion of Cash-Based Borrowers
Many small businesses and individuals in India still operate in a predominantly cash-based environment, with minimal banking activity. Banking surrogate models, by design, exclude those without digital or formal banking trails, thereby limiting access to credit for a large segment of the population. This exclusion is contrary to the goal of financial inclusion and can deepen the divide between formal and informal borrowers. Without additional models or blended scoring approaches, these borrowers remain undervalued or invisible in the formal credit system.