What is Scorecard Development and How to Build Credit Scorecard on Application, Behavioural and Collection


What is a Scorecard?

A scorecard in credit risk modelling is a statistical model used by lenders to evaluate the creditworthiness of potential borrowers. It helps in predicting the likelihood of whether a borrower will repay a loan or not. The scorecard is developed through the analysis of historical data on borrowers and their loan repayment behaviour. This model assigns scores to various attributes or characteristics of the borrower, such as income level, employment history, credit history, debts, and other financial indicators.

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What is Scorecard Development and How to Build Credit Scorecard on Application, Behavioural and Collection

For example, a simple credit scorecard might assign points based on factors like:

  • Credit History: The number of years you’ve had credit, the types of credit accounts (credit cards, mortgages, car loans, etc.), your payment history, and any defaults or late payments. A longer, positive credit history might score higher than a short or negative history.
  • Debt Levels: Your current debt levels, including credit card balances, student loans, and other debts compared to your income, known as your debt-to-income ratio. Lower ratios generally score higher.
  • Income and Employment: Stable employment and a higher income can contribute positively to your score, reflecting your ability to repay.
  • New Credit and Inquiries: The number of recent credit account applications and new credit accounts. Applying for many new accounts in a short period can lower your score.
  • Use of Credit: How much of your available credit you’re using, particularly on revolving accounts like credit cards. Lower utilization rates are usually better for your score.

Each factor is weighted differently depending on how predictive it is of future default. For instance, credit history and debt levels might be weighted more heavily than new credit inquiries.

Lenders use credit scorecards to make lending decisions, set interest rates, and establish credit limits. They can be customized for different types of credit products and for specific risk appetites of the lender. Credit scorecards are a critical tool in the financial industry for managing credit risk, ensuring responsible lending, and helping consumers access credit.

How to build a scorecard. Step by Step guide.

This process outlines the creation of a logistic regression-based credit scorecard, a common method used in the industry.

Steps Description Input Output Example
1. Define Objective Establish the goal of the scorecard. Business requirements Objective statement Objective: To predict the likelihood of a borrower defaulting on a loan within the next 12 months.
2. Collect Data Gather historical data on borrowers. Loan applications, repayment records, demographic information Raw data set Data on atleast 10,000 loan applications, including age, income, existing debt, repayment history.
3. Clean Data Prepare and clean the data for analysis. Raw data set Cleaned data set Removed records with missing values in critical fields, corrected erroneous entries.
4. Feature Selection Identify relevant variables for the scorecard. Cleaned data set List of selected features Selected features: Age, Income, Debt-to-Income Ratio, Number of Late Payments in the past year.
5. Data Splitting Split the data into training and validation sets. Cleaned data set Training and validation data sets 70% of data allocated for training, 30% for validation.
6. Model Development Develop the statistical model. Training data set Developed model Logistic regression model developed using selected features.
7. Model Validation Validate the model on a separate data set. Validation data set Validation results Model accuracy and performance metrics evaluated on the validation set.
8. Score Calculation Calculate scores for each borrower. Developed model, borrower data Scores Each borrower assigned a score from 300 to 850 based on their likelihood of default.
9. Scorecard Calibration Adjust and calibrate the scorecard. Scores, model performance data Calibrated scorecard Adjustments made to score cutoffs and weights to improve prediction accuracy and meet business
10. Implementation & Monitoring Implement the scorecard and monitor its performance. Calibrated scorecard, ongoing borrower data Performance reports Scorecard implemented in loan decision process, regularly monitored for accuracy, and updated as

Types of Credit Scorecards

There are primarily 2 types of credit scorecards:

  • Application Scorecards are used at the point of credit application to evaluate the risk of new applicants. They help lenders decide whether to extend credit and under what terms (e.g., interest rate, credit limit).
  • Behavioural Scorecards are utilized for managing the ongoing relationship with the customer. They can help in identifying opportunities for offering additional credit or services, as well as in early identification of potential default.
  • Collection Scorecards focus on accounts that have already shown signs of financial stress or have become delinquent. These scorecards help in efficiently managing collections by prioritizing accounts more likely to pay and determining the most effective collection strategies.

A table summarizing the main types of credit scorecards, such as Application, Behavioural, and Collection scorecards is a follows:

Type Purpose Data Input Typical Output Example
Application Scorecard To assess the risk of a new applicant and decide whether to grant credit. Applicant’s credit history, income, employment details, debts, and other financial information. A score that predicts the likelihood of the applicant defaulting on the loan. An applicant with a stable job and a good credit history might receive a high score, indicating a
low risk of default.
Behavioural Scorecard To monitor existing customers’ credit usage and behaviour over time to adjust credit limits and
Account performance data, including payment history, credit utilization, account balances, and
changes in financial status.
A score reflecting the current credit risk posed by the customer, based on their credit behaviour. A customer who consistently pays on time and keeps balances low may have a high behavioural score,
suggesting they are a low risk for future credit.
Collection Scorecard To prioritize and manage delinquent accounts effectively by assessing the likelihood of repayment. Information on overdue payments, account age, historical payment behavior, and customer
communication responses.
A score indicating the probability of recovering the owed amount from a delinquent account. An account with a high collection score indicates a higher likelihood of successful collection based
on factors like previous partial payments or responsive communication.

How does Roopya helps to develop and build Scorecard?

Roopya helps develop credit scorecards in several ways, primarily through its Credit risk analytics solutions. Here’s how:

Data-driven Approach:

  • Standardization: Roopya standardizes credit data from various sources, making it easier to analyse and use for model development. This reduces inconsistencies and improves model accuracy.
  • Feature Engineering: Roopya utilize various statistical methods like Weight of Evidence (WOE) to transform variables into formats suitable for building models.
  • Machine Learning Integration: Roopya leverages machine learning algorithms like logistic regression to build scorecards predicting borrower behaviour, like the probability of default (PD).

Credit Risk Analytics Solutions:

  • Credit Scorecard Development: Roopya offers tools and expertise to create scorecards for different loan types (personal, mortgage, etc.) and customer segments.
  • Portfolio Analysis: Roopya helps analyse the risk profile of your entire loan portfolio to understand overall risk exposure and make informed decisions about diversification and capital allocation.
  • Regulatory Compliance: Ensure your lending practices comply with regulations like Basel Accords by incorporating them into the scorecard development process.

Additional Benefits:

  • Increased Efficiency: Roopya automates many tasks in credit scorecard development, saving time and resources.
  • Improved Accuracy: Roopya’s standardized data and advanced analytics lead to more accurate scorecards, enabling better lending decisions.
  • Financial Inclusion: Roopya’s platform helps expand access to credit for underserved segments by standardizing data and making credit assessment more accessible.

Overall, Roopya provides a comprehensive suite of tools and expertise to help lenders develop robust and efficient credit scorecards, leading to better risk management and financial inclusion.

Best In-class Featuresimg

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