Credit Assessment


Credit analysis is the process of assessing a company’s or organization’s creditworthiness. In other words, it is the assessment of a company’s ability to meet its financial obligations. When a large corporation issues or has issued bonds, its audited financial statements can be scrutinized. Alternatively, before making or renewing a commercial loan, a bank may examine the financial statements of a small business. Regardless of whether the company is big or small, the word applies.

Credit analysis aims to give a risk rating to both the borrower and the lending facility being proposed. The risk rating is determined by measuring the likelihood of the borrower defaulting at a given trust level over the life of the facility, as well as the amount of loss that the lender will experience if the borrower defaulted. Credit analysis requires a broad range of financial analysis methods, such as ratio and pattern analysis, forecasts, and a comprehensive review of cash flows. In addition to credit background and management capacity, credit analysis requires an evaluation of collateral and other sources of repayment. Analysts try to estimate the likelihood of a creditor defaulting on its loans, as well as the magnitude of losses in the event of default. Credit spreads reflect credit analysis by financial market participants. Credit spreads are the difference in interest rates between potentially “risk-free” investments like US treasuries or LIBOR and investments with some risk of default.
A bank would consider all of these considerations before authorizing a commercial loan, with the borrower’s cash flow being the most significant. The debt service coverage ratio is a common predictor of repayment capacity. A bank’s credit analyst will assess how much money a company generates. Debt service coverage of at least 120 per cent is preferred by commercial bankers. To put it another way, the debt service coverage ratio should be 1.2 or higher to prove that there is a buffer and that the company will meet its debt obligations.

In other words, Credit analysis is the process of evaluating a borrower’s loan application to see whether the company produces enough cash flow to pay off its debts. Collecting information from the applicant, reviewing the information given, and deciding whether or not to authorize the loan are all part of the credit review process. To assess a borrower’s creditworthiness, a credit analyst employs a variety of strategies such as ratio analysis, pattern analysis, cash flow analysis, and forecasts.


Credit ratings are assessments of an issuer’s current financial creditworthiness expressed in terms of ordinal metrics. Issuers include states, businesses, and financial institutions. Rating agencies such as Fitch Ratings, Moody’s, and Standard & Poor’s issue these ratings, which can be viewed as a detailed assessment of an issuer’s ability to fulfill its financial obligations in full and on schedule. As a result, ratings play an important role in the financial markets by providing valuable information for financial planning. Agencies use a wide variety of financial and non-financial data, including domain experts’ expectations, to perform rating assessments of large companies. General guidance on rating decision-making are generally issued by rating agencies, but comprehensive explanations of rating standards and determinants of bank ratings are rarely provided (Orsenigo and Vercellis 2013). There has been a growing body of research into the development of accurate quantitative methods for automated classification according to institutions’ financial strength in the quest for more objective assessments of the creditworthiness of large corporate and financial institutions.


The credit review process can take anything from a few weeks to several months. It begins with the gathering of information and ends with the lender deciding whether or not to approve the loan application and, if accepted, how much credit to allocate to the borrower.

The main stages in the credit analysis process are as follows:


Collecting details about the applicant’s credit background is the first step in the credit review process. The lender is especially interested in the customer’s prior repayment history, corporate integrity, financial solvency, and transaction records with the bank and other financial institutions.

The lender also gathers details about the loan’s intent and viability. The lender needs to know if the project being financed is realistic and can produce enough cash flow. The credit analyst assigned to the borrower must decide if the loan amount is sufficient to complete the project and whether the borrower has a good plan in place to complete the project successfully. The bank also gathers information about the loan’s collateral, which serves as protection for the loan if the borrower defaults on its debt obligations. Lenders usually choose to have the loan repaid from the proceeds of the project being financed, and only use the collateral as a backup if the borrower defaults.


The data gathered in the first stage is reviewed to see if it is reliable and honest. Passports, corporate charters, trade permits, corporate resolutions, agreements with consumers and suppliers, and other legal documents are all scrutinized to see if they are authentic and true. To determine the borrower’s financial capacity, the credit analyst reviews financial statements such as the income statement, balance sheet, cash flow statement, and other related documents. The bank also considers the borrower’s project experience and credentials to assess their ability to complete the project successfully.

Another consideration that investor recognizes is the project’s efficacy. The lender considers the project’s intent as well as its long-term prospects. The lender needs to know if the project is sustainable enough to create enough cash flow to repay the loan and fund the business’s operating expenses. A successful project would be able to quickly obtain credit from a lender. On the other hand, if a project is up against the strong competition or is in decline, the bank may be unable to extend credit due to the high likelihood of incurring losses in the event of default. However, if the bank believes the borrower’s risk level is appropriate, it will lend credit at a high-interest rate to offset the high risk of default.


The decision-making stage is the last step in the credit analysis process. The lender decides whether the measured level of risk is reasonable or not after obtaining and reviewing the required financial details from the borrower. If the credit analyst assigned to a particular borrower is satisfied that the calculated level of risk is reasonable and that the lender would have no difficulty servicing the credit, they will send a recommendation report to the credit committee outlining the review’s conclusions and the final decision. If the credit analyst determines that the borrower’s level of risk is too great for the lender to bear, the credit analyst must submit a report to the credit committee outlining the conclusions on the borrower’s creditworthiness. The final decision on whether to accept or refuse the loan rests with the committee or other relevant approval body.


To minimize the effects of default, the following criteria are used to assess the creditworthiness of a prospective borrower.


The ability to repay is the most important aspect of all; it is the main source of loan repayment. We all estimate borrowing capacity by analyzing the applicant’s bank statements, which include cash flow, EMI repayment schedules for current loans, cash deposits in accounts, minimum and average account balances, returned checks, and the likelihood of successful loan repayment.


The amount of money a creditor puts into his or her expected personal expenses is referred to as capital, and it shows how much the other party is at risk. It is often assumed that the borrower contributes from his or her assets and assumes personal financial risk, demonstrating the seriousness with which the loan must be repaid.


Since this is an unsecured loan, post-dated checks are needed. Salary slips, ITRs, Form 16, Pan Numbers, and Aadhar Cards provide comfort in the event of non-payment, and we can begin the recovery process using these documents.


Based on the examination of records, a determination is made as to whether or not the borrower is trustworthy in repaying the loan or has the intent to repay it. Education, history, credit quality, banking habits, and residence status all offer insight into a person’s personality. We also look at social behavior to gain an understanding of career development, experience, and sentimental perspective.


Physical inspection is an essential aspect of the loan application process.


Before submitting the file to the credit appraisal, a credit report from Bureau is requested to verify credit history, and internal and external verification is completed.


The above-mentioned criteria are used to test the applicant’s documents by the loan officer. The borrower’s cumulative score is calculated based on the report.

If the total rating indicates approval, it will be forwarded to the creditor for his approval. If the ratings indicate that a review is necessary, the report will be forwarded to the Chief Risk Officer. Before providing the necessary approval, the Chief Risk Officer may discuss the situation with the CEO. If the credit score is low, the loan application will be turned down.


Increasing The Usability And Accuracy Of The Data:-

Many countries face challenges in ensuring the availability and quality of information collected due to a lack of clarification about what constitutes alternative data and how it should be handled. This is made worse by the lack of digitized public records and a digital footprint of MSME transactions, as well as data inaccessibility or poor data quality. Regulators and policymakers may counter this by issuing guidelines on how to obtain and process alternative data. This may involve using unique identifiers such as passports, as well as alternate identification methods such as social security numbers, tax identification numbers, and so on. Government agencies may also help MSMEs by digitizing their records and encouraging the implementation and availability of Open Data Systems and Standards.

Credit Information Exchange Is Being Expanded:-

Another source of concern is the lack of coverage and high minimum loan size requirements for business loan data in CRSPs. Regulators may counter this by encouraging service providers to share credit information in an open, equitable, and competitive manner, as well as lowering or removing minimum requirements for reporting financial commitments to CRSPs. The viability of creating a Credit Registry or Databank to facilitate knowledge exchange can be tested in cases of inefficient markets.

Creating A Boarder For Cross-Border Data Sharing:-

Due to inconsistent and non-standardized recognition schemes, recognizing MSMEs across jurisdictions has been difficult. To deal with this, policymakers and regulators should take a multi-level approach. They can cooperate and collaborate to establish cross-border data sharing standards and information regulations at the international level through standard-setting bodies like the Bank for International Settlements, and they can promote the adoption of a harmonized core collection of data attributes to be exchanged domestically and across borders at the domestic level through standard-setting bodies like the Bank for International Settlements. This can be supplemented by determining the viability of introducing the G20 Global Legal Entity Identifier or a version for individuals, such as ID4D, to fix cross-border data use and sharing.

Integrity, Creativity And Competitiveness Must All Be Balanced:-

As the use of alternative data increases, regulators must strike a balance between the need to promote innovation while maintaining sufficient consumer security. To keep up with the rapid speed of technological change and to better understand the benefits and risks to customers, policymakers and regulators should cooperate on the development of responsible innovation principles and explore the feasibility of implementing/ using regulatory tools for encouraging innovation, such as alternative scoring techniques, to facilitate alternative data-centric innovations.

Data Privacy, Consumer Protection And Cyber security:-

The inadequacy of current legislation, whether it be data protection laws, accountability and disclosure regimes, or regulations and laws on consent, is one of the most common concerns about the use of alternative data. Cybersecurity issues are rapidly growing, and their potential effects on global financial structures must be addressed. To address some of these issues, policymakers should ensure that alternative data is collected and processed legally and that users have the right to object to the processing of their data, as well as the ability to move their data to any other service provider.


For credit underwriting, alternative credit scoring based on digital data provides a more comprehensive view of a borrower’s creditworthiness and associated risks. Fintech lenders use a combination of traditional and non-traditional data sources to assess a borrower’s creditworthiness and repayment capacity, as well as traditional data points on the company’s financial results, such as sales and income.New data sources, such as transaction data (information from point of sale) and proxy data, such as service payments and bill payments, provide a more comprehensive view of the company. Fintech firms have essentially pioneered this model. These firms provide the digital sourcing platform, operate the alternative data scoring engine, and collaborate with a variety of banks and NBFCs to provide a variety of lending items.

Credit underwriting systems driven by advanced technologies such as artificial intelligence (AI), machine learning, and data analytics will compile and analyze a huge amount of data from various disparate sources with the aid of algorithms to extract useful insights. Depending on the nature of the companies within the overall MSME category, each of the data points can be viewed as a vector for credit assessment and allocated unique weights. Lenders use the resulting credit score to make decisions about loan funding, such as interest rates, loan amounts, repayment terms, and timelines.

Data analytics, machine learning, and automation form a solid foundation for fintech lenders’ new-age credit underwriting models, ensuring that SMEs in India’s tier-3 and tier-4 cities and towns have quicker and better access to credit. Financial services that are powered by technology Marketplaces are linking SMEs to capital markets, as well as institutional investors who want to contribute to their development, by enabling access to not only lending but also a wide range of financial management and investment services.


Small companies face financing difficulties because banks and financial institutions have found it difficult to determine their creditworthiness, preferring to finance businesses with a high CIBIL score, comprehensive audited financial statements, or assets to give as collateral. However, the issue is that the number of businesses that can meet these specifications is extremely small. Service-oriented SMEs, on the other hand, are unable to mortgage their equipment against loans, making it difficult for them to obtain financing to expand and scale-up. NBFCs have stepped in to fill the gap, but their combined size is currently inadequate to make a meaningful difference.


Nishtha Kapoor

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