Synopsis: In this article, we delve into the wide variety of credit scoring models, including their transition from traditional methods to more data-driven, innovative methods involving AI/ML. 

The improved access to diverse data sources paired with higher computing power has changed the way lenders assess borrowers. Over time, credit scoring models have become sophisticated, evolving from conventional statistical techniques involving regression and discriminant analysis to artificial intelligence (AI) and machine learning (ML)-led models. 

Even the application of credit scores—the primary determinant of a borrower’s creditworthiness–has expanded beyond the decision-making of extending a loan; financial institutions factor in credit scores to price their financial products and services, solicit prospective customers, determine minimum capital maintenance requirements, and enhance relationship management. 

The advent of fintechs, the greater demand for anytime, anywhere finance, and the need for enhanced credit access have amplified the variety of credit scoring models, resulting in efficiency gains, improved customer experiences, and deepening financial inclusion. 

Below, we elaborate on what credit scoring is and the various models used to perform it. 

What is Credit Scoring? 

Credit scoring is a statistical calculation to determine a borrower’s creditworthiness. It predicts the probability of a loan applicant or an existing borrower defaulting on payments or becoming delinquent. Lenders sanctioning business loans, mortgage finance, and consumer credit are heavily reliant on credit scores to make these decisions. 

While technically, credit scores can be scaled to any numerical range, in India, credit scoring companies have restricted their scores to 300-900. Having said that, the higher the credit score, the more creditworthy the borrower is. In other words, lenders prefer loan applicants with 750+ credit scores, as the likelihood of non-payments and defaults is minimized. Check your CIBIL score for free on the Protium App. 

As a credit score is indicative of a borrower’s credit risk, lenders use it to perform risk-based pricing, thus basing their interest rates and loan terms on it. This, in turn, affects their relative profitability.  

What are the Types of Data Used in Credit Scoring Models? 

As credit scores provide a quick and effective way to evaluate borrowers, lenders include a variety of data to develop and maintain their credit scoring models to ensure a strong credit underwriting framework. Traditionally, loan providers would consider the historical payment performance, including defaults and outstanding amounts, the length of credit history, collateral, guarantees, and the amount, type, and maturity of the loan sought to build a credit score. 

To substantiate this, one of the four leading credit bureaus in India, TransUnion CIBIL (Equifax, Experian, and CRIF Highmark are the remaining three), uses four major factors in its credit scoring models: 

• Payment History: Credit scoring companies maintain and update monthly payment records related to bills and loan installments. Their credit scoring models keep track of the payment dues and the frequency and recency of payment misses and delays over the past 24 months. Late payments reduce credit scores, with a single late EMI payment resulting in a loss of over 100 points.  

• Credit Utilization Ratio (CUR): It is a measure of the proportion of credit used by the borrower against their stated limits. High CUR signals high credit usage and repayment burden, negatively affecting credit scores. Individuals and businesses with a 30-40% CUR tend to have better credit scores. 

• Credit Mix: Credit bureaus mark a variety of loan types as positive for an applicant. Keeping a history of availing of secured loans, such as home loans, auto loans, business loans, and unsecured loans, including personal loans and credit cards of different durations, positively affects credit scores.   

• Number of Hard Enquiries: Lenders also gauge the borrower’s credit burden from the number of inquiries made by other lenders. This plays a minor role in credit scoring as credit service providers (CSPs) tend to look over the loan applicant profile at the time of approving business loans and credit cards. 

However, the advances in digitalization have enriched the variety of data, enabling lenders to develop more predictive models that provide deeper insights into the applicant, which is particularly crucial for new to credit (NTC) customers without a thick credit file. Alternate data, such as real-time granular transactional data, mobile data, social media interactions, biometrics, and psychometrics, is being increasingly used by new-age fintechs to better evaluate a borrower’s creditworthiness. 

For instance, VantageScore 4.0, developed by Equifax, Experian, and TransUnion, deploys machine learning to evaluate consumers with thin credit histories. Consequently, this credit scoring model has reported a 2.4% performance improvement, with its ability to capture defaulting accounts increasing by 6% over previous credit scoring models. 

Traditional vs Modern Credit Scoring Models 

Historically, the foundation of the majority of credit scoring models was formed by past payment history. These traditional models were developed using linear regression, decision trees, logit modeling, and other statistical analysis methods using limited structured data. Below, we outline two such traditional credit scoring models. 

Linear Regression: Regression analysis involves one of the easiest approaches to predicting and explaining credit risk and the probability of default. In regression-based credit scoring models, the labeled structured data (target outcome) is projected on a set of independent variables to identify parameters that minimize the sum of squared residuals.  

Discriminant Analysis: A variation of the regression analysis model, discriminant analysis has several sub-types, with the simplest model entailing the labeling of data into default and non-default categories of applicants. It was initially applied to systemic data to identify firms that were at risk of going insolvent based on their financial ratios.  

However, as more and more people transact online due to smartphones and cheap data penetration, financial institutions have been transitioning to the use of additional, semi-structured, and unstructured data in their credit scoring models. They use open banking transactions and mobile digital data to capture a more holistic and granular view of prospective borrowers’ creditworthiness. This data is processed using AI and ML-driven algorithms, reducing reliance on prior payment records and opening doors to affordable credit for previously financially excluded sections of society. 

AI-based credit scoring models are heavily dependent on natural language processing and deep learning. These models detect complex patterns from vast sources of data, make predictions, and improve them based on previous outcomes, also known as “learning from experience”. For instance, if a lender has already labeled some data points “in default” and “not in default”, the AI will learn the general rule and similarly classify other data observations.  

Two popular modern credit scoring models are deep neural networks and clustering.  

Deep Neural Networks: These models train the algorithm to recognize data patterns through several layers of processing instead of organizing data through predefined equations. To illustrate, based on the previous layer’s output, the model is trained again to generate new outputs, enabling it to detect nonlinear patterns in unstructured data. 

Clustering: In this descriptive credit scoring method, the aim is to classify data into groups with significant differences between distinct groups. For instance, a clustering algorithm may look for a cluster for a borrower who is difficult to assess. On finding the appropriate cluster, its average default assessment can be applied to calculate the borrower’s probability of default. 

Empowering Growth and Inclusion through Credit Scoring Models 

The growing adoption of innovative credit scoring models holds immense potential to improve access to affordable credit, advance financial inclusion, and propel economic growth. By processing diverse data, automating computations, and enhancing consumer experiences, lenders can benefit from improved process efficiencies while catering to a larger borrowers’ cohort, ensuring last-mile access. However, lenders must ensure that their credit scoring models continue to provide fair and transparent outcomes without perpetuating data biases or endangering data security and privacy.