Credit scorecards

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A credit score is a numerical expression representing the creditworthiness of an individual. [1] A credit score is primarily based on a credit report, information typically sourced from credit bureaus. [2]

Contents

Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. [3] Lenders also use credit scores to determine which customers are likely to bring in the most revenue.

Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government departments employ the same techniques. Digital finance companies such as online lenders also use alternative data sources to calculate the creditworthiness of borrowers. [4] [5]

Characteristics of scorecards

Scorecards are built and optimized to evaluate the credit file of a homogeneous population (e.g. files with delinquencies, files that are very young, files that have very little information). Most empirically derived credit scoring systems have between 10 and 20 variables. [6] Application scores tend to be dominated by credit bureau data which typically amounts to over 80% of the predictive power compared to 60% in the late 1980s [6] for UK scorecards. Indeed, there has been an increasing trend to minimize applicant or non-verifiable variables from scorecards, resulting in the reliance on credit bureau data.

Credit scores usually range from 300 to 850 showing the customer's creditworthiness. A customer with a high credit score shows that they are creditworthy and banks will have no problem giving them a loan. If a customer has a low credit score then banks would be hesitant to give out a loan and if they do it might be with a higher interest rate. [7]

Credit scoring typically uses observations or data from clients who defaulted on their loans plus observations on a large number of clients who have not defaulted. Statistically, estimation techniques such as logistic regression or probit are used to create estimates of the probability of default for observations based on this historical data. This model can be used to predict the probability of default for new clients using the same observation characteristics (e.g. age, income, house owner). The default probabilities are then scaled to a "credit score." This score ranks clients by riskiness without explicitly identifying their probability of default.

There are a number of credit scoring techniques such as hazard rate modeling, reduced form credit models, the weight of evidence models, linear or logistic regression. The primary differences involve the assumptions required about the explanatory variables and the ability to model continuous versus binary outcomes. Some of these techniques are superior to others indirectly estimating the probability of default. Despite much research from academics and industry, no single technique has been proven superior for predicting default in all circumstances.

A typical mistaken belief about credit scoring is that the only trait that matters is whether you have actually made payments on time as well as satisfied your monetary obligations in a prompt way. While payment background is essential, however, it still just composes just over one-third of the credit rating score. Furthermore, the repayment background is only shown in your credit history.

One of the major areas of income for banks is the lending business and these loans can be secured or unsecured. Banks would not want to give credit to customers or businesses that will not be able to repay the loan in the future. The process of scoring an applicant based on their creditworthiness determines who should get credit and by how much. This is where credit scorecards come into play which helps banks and financial situations minimize risk and less the delinquency rate.

Modelling methods

The methodologies that are used to create a credit scorecard broadly fall under two categories namely a statistical-based method and an artificial intelligence/machine learning method. [8]


Statistical based credit scoring model

Models which are usually less complex and whose output can be easily interpreted fall under this category. Simple techniques like Logistic regression, linear regression, and decision trees are some examples of simple statistical techniques. Many banks prefer this category because if a customer is denied a loan then a reason for denial needs to be given and that can be easily interpreted from these models.

Artificial intelligence/machine learning based credit scoring model

The techniques used here are broadly called black boxes in the analytics world because interpreting them is difficult. Banks generally use this type of scoring model for upselling or cross-selling different products of a bank to its customers. These techniques usually outperform the statistical-based credit scoring models but fall behind because of their interpretability issues. [8]

Types of Scorecards

Application Scorecard - This is used when a customer applies for a new loan. This type of scorecard predicts if a customer will default on the loan. Here the type of data that is used mainly comes from historical loan applications and if the customer has any existing loan then that data is extracted from one of the credit bureaus. If for example, the product that is getting launched is new then in that case data is taken is credit bureaus. This type of scorecard helps the business to make automated, accurate, and consistent decisions on whether to approve, review or decline an applicant. Some of the advantages of this type of scorecard are that the organization can automate the whole decision-making process which in turn reduces the turnaround time of the underwriting process. It also provides the business to make data-backed and accurate decisions. [9]

Behavioral Scorecard - This is used in predicting if an existing customer who has a loan is going to default. Here the data includes the customer's transactional details as well as Bureau-related information. This type of scorecard is also used as an alternate credit score for internal purposes of the institute along with the credit score obtained from the credit bureau. This type of scorecard is also used for identifying the bank's most valuable customers. [9]

Collection Scorecard - This is used to predict customers' responses to different strategies for collecting owed money. The data involved here is similar to the Behavioural scorecard. When a bank decides to lend credit to its customers or if they decide to extend credit to its existing customers, they would need to assess the probability of those customers being able to comfortably repay the loan amount. Here it is important to consider that customers' circumstances may also change over time and to maintain a good relationship with their clients, the bank would need to offer appropriate support. It is important for the organization to identify and prioritize the accounts that need collections because it plays a vital role in controlling bad debt. Deciding the frequency and channel for communication can become difficult in creating a balance in treating customers fairly. This scorecard helps in identifying customers who require less interaction. Some of the advantages of using collection scorecards are creating a streamlined and efficient collection process, the ability to offer a better customer experience without hurting sentiments, and improving recovery rates. [10]

Developments in credit scoring

Naeem Siddiqi discusses some important points regarding the increased use of scorecards and processes that were used in the past vs now. [11]

Some points that increased the use of scorecards include:

Traditional Approach

File sourcing will include a collection of all physical documents from the customer. Documents like bank statements, income tax documents, etc. would be first collected. The loan officer would take all these documents and do a judgmental evaluation. All the documents would then be manually verified. Someone from the bank would need to go to customer's specified location to verify if the person actually lives there and in this context would also discuss the credit with them. In the past, there were no credit Bureaus or credit scorecards because of which the entire process of decision making was done manually. Because of this process, the loan officer would take more time in completing loan applications which would in turn reduce the turnaround time for completion.

Present Approach

These days all the file sourcing or paperwork required needs to be submitted online on the bank's web portal. No more physical papers as everything is digital. These files are usually PDFs. Previously, income assessments were done manually by a loan officer. Nowadays, there is software that reads the customers' banks statements and automatically verifies their income. The digital customer profile is also checked instead of field visits. There can be a lot of information available about the customer because of the presence of credit Bureaus. If the customer has some kind of banking history that can be obtained from the Bureau. Credit scorecard models are used to accept or reject the customer's loan application. The customer will be able to see their decision online on the web portal itself. Since most of the decision-making process is now online, many more applications can be processed increasing the turnaround time.

See also

Related Research Articles

Credit risk is the possibility of losing a lender holds due to a risk of default on a debt that may arise from a borrower failing to make required payments. In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs. The loss may be complete or partial. In an efficient market, higher levels of credit risk will be associated with higher borrowing costs. Because of this, measures of borrowing costs such as yield spreads can be used to infer credit risk levels based on assessments by market participants.

A credit score is a number that provides a comparative estimate of an individual's creditworthiness based on an analysis of their credit report. It is an inexpensive and main alternative to other forms of consumer loan underwriting.

<span class="mw-page-title-main">Fair Credit Reporting Act</span> U.S. federal legislation

The Fair Credit Reporting Act (FCRA), 15 U.S.C. § 1681 et seq., is federal legislation enacted to promote the accuracy, fairness, and privacy of consumer information contained in the files of consumer reporting agencies. It was intended to shield consumers from the willful and/or negligent inclusion of erroneous data in their credit reports. To that end, the FCRA regulates the collection, dissemination, and use of consumer information, including consumer credit 909-306-4989 information. Together with the Fair Debt Collection Practices Act (FDCPA), the FCRA forms the foundation of consumer rights law in the United States. It was originally passed in 1970, and is enforced by the U.S. Federal Trade Commission, the Consumer Financial Protection Bureau, and private litigants.

A credit history is a record of a borrower's responsible repayment of debts. A credit report is a record of the borrower's credit history from a number of sources, including banks, credit card companies, collection agencies, and governments. A borrower's credit score is the result of a mathematical algorithm applied to a credit report and other sources of information to predict future delinquency.

A credit bureau is a data collection agency that gathers account information from various creditors and provides that information to a consumer reporting agency in the United States, a credit reference agency in the United Kingdom, a credit reporting body in Australia, a credit information company (CIC) in India, Special Accessing Entity in the Philippines, and also to private lenders. It is not the same as a credit rating agency.

Loan origination is the process by which a borrower applies for a new loan, and a lender processes that application. Origination generally includes all the steps from taking a loan application up to disbursal of funds. For mortgages, there is a specific mortgage origination process. Loan servicing covers everything after disbursing the funds until the loan is fully paid off. Loan origination is a specialized version of new account opening for financial services organizations. Certain people and organizations specialize in loan origination. Mortgage brokers and other mortgage originator companies serve as a prominent example.

A credit score is a numerical expression based on a level analysis of a person's credit files, to represent the creditworthiness of an individual. A credit score is primarily based on a credit report, information typically sourced from credit bureaus.

Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.

VantageScore is a consumer credit-scoring system in the United States, created through a joint venture of the three major credit bureaus. The model is managed and maintained by an independent company, VantageScore Solutions, LLC, that was formed in 2006 and is jointly owned by the three bureaus.

The following article is based on UK market; other countries may differ.

Probability of default (PD) is a financial term describing the likelihood of a default over a particular time horizon. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations.

Connect, formerly PRBC, is a consumer credit reporting agency, more commonly referred to as a credit bureau in the United States. It is similar to the other four U.S. credit bureaus in that it is an FCRA compliant national data repository. Connect differs in that consumers are able to self-enroll and report their own non-debt payment history, and they can build a positive credit file based on alternative data, such as timely payments for bills including rent, utilities, cable, telephone, and insurance that are not automatically reported to the other bureaus.

Credit analysis is the method by which one calculates the creditworthiness of a business or organization. In other words, It is the evaluation of the ability of a company to honor its financial obligations. The audited financial statements of a large company might be analyzed when it issues or has issued bonds. Or, a bank may analyze the financial statements of a small business before making or renewing a commercial loan. The term refers to either case, whether the business is large or small. A credit analyst is the finance professional undertaking this role.

Loan officers evaluate, authorize, or recommend approval of loan applications for people and businesses.

TransUnion CIBIL Limited is a credit information company operating in India. It maintains credit files on 600 million individuals and 32 million businesses. TransUnion is one of four credit bureaus operating in India and is part of TransUnion, an American multinational group.

In economic policy, alternative data refers to the inclusion of non-financial payment reporting data in credit files, such as telecom and energy utility payments.

Business credit reports are usually created by credit bureaus when credit grantors report information related to a business credit account. These reports are sometimes examined during the decision-making process of whether or not to grant credit to a business.

Credit Environment India is with Credit Information Companies Act getting passed by the Government of India in 2005, Credit Bureau formally got introduced to the country. CIBIL was established as the first credit bureau. TransUnion International Inc, one of the leading consumer credit bureaus in the world, is the major stake holder (27.5%) in CIBIL and provided the required platform to commence operations.

Hunter Fraud Score is a rating score launched locally in India by Experian Credit information company of India (ECICI), to detect fraud in credit applications to banks and insurance companies and help them lower their losses.

Credit scoring systems in the United States have garnered considerable criticism from various media outlets, consumer law organizations, government officials, debtors unions, and academics. Racial bias, discrimination against prospective employees, discrimination against medical and student debt holders, poor risk predictability, manipulation of credit scoring algorithms, inaccurate reports, and overall immorality are some of the concerns raised regarding the system. Danielle Citron and Frank Pasquale list three major flaws in the current credit-scoring system:

  1. Disparate impacts: The algorithms systematize biases that have been measured externally and are known to impact disadvantaged groups such as racial minorities and women. Because the algorithms are proprietary, they cannot be tested for built-in human bias.
  2. Arbitrary: Research shows that there is substantial variation in scoring based on audits. Responsible financial behavior can be penalized.
  3. Opacity: credit score technology is not transparent so consumers are unable to know why their credit scores are affected.

References

  1. ↑ Kagan, Julia. "Credit Score". Investopedia. Archived from the original on 2007-10-19. Retrieved 2021-05-24.
  2. ↑ "What Is a Good Credit Score?". www.experian.com. 2021-02-11. Retrieved 2021-05-24.
  3. ↑ "What is a Credit Score – Credit Score Range | Equifax". www.equifax.com. Retrieved 2021-05-24.
  4. ↑ Experian; Karma, Credit; Hero, Student Loan; Tretina, more Learn about our editorial policies Kat. "What Credit Score Do You Need to Buy a Car?". Investopedia. Retrieved 2021-05-24.
  5. ↑ Kagan, Julia. "Insurance Score". Investopedia. Archived from the original on 2006-03-17. Retrieved 2021-05-24.
  6. 1 2 Murray Bailey "Practical Credit Scoring: Issues and Techniques" White Box Publishing (2006)
  7. ↑ "What Is a Good Credit Score?". www.experian.com. 2021-02-11. Retrieved 2022-04-17.
  8. 1 2 "Credit scoring models: Techniques and issues". Journal of Advanced Research in Business and Management Studies. 2.
  9. 1 2 "Application Scorecards". Experian. Retrieved 2022-04-17.
  10. ↑ "Collections and Recovery Scorecards". www.experian.com.pl. Retrieved 2022-04-17.
  11. ↑ Siddiqi, Naeem, ed. (2012-01-02). Credit Risk Scorecards. doi:10.1002/9781119201731. ISBN   9781119201731.
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