Credit Scorecard Development on Application, Behavioural and Collection, Probability of Default (PD) Calculation


What is a Scorecard in a Credit Decisioning?

A credit scorecard is a statistical model used by lenders and financial institutions to assess the creditworthiness of potential borrowers. It helps in predicting the likelihood that a borrower will repay a loan or credit obligation on time. The scorecard is developed through the analysis of historical data on borrowers, including their repayment history, debt levels, length of credit history, new credit, and types of credit used.

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Credit Scorecard Development on Application, Behavioural and Collection, Probability of Default (PD) Calculation

Steps to build a credit scorecard

Step Description Example
1. Data Collection Collect historical financial data of borrowers. Collect data from credit bureaus, loan applications, including payment history, credit utilization, income, employment status.
2. Variable Selection Identify relevant variables (predictors) for the scorecard. Select variables like age, income, payment history, number of open accounts, total debt, etc., based on their predictive power.
3. Data Preprocessing Clean and preprocess the data to handle missing values, outliers, and categorical variables. Impute missing values, normalize income levels, encode categorical variables like employment status into numerical values.
4. Variable Transformation Transform variables to ensure they have the right predictive relationship with the outcome. Apply logarithmic transformation to skewed variables like income, or create binary variables for categorical data.
5. Sample Splitting Split the dataset into training and validation sets. Use 70% of the data for training the model and 30% for validation.
6. Model Building Develop the statistical model using the training dataset. Use logistic regression to predict the probability of default based on the selected variables.
7. Model Validation Validate the model’s performance on the unseen validation dataset. Calculate metrics like Area Under the Curve (AUC), accuracy, precision, and recall on the validation set.
8. Score Calculation Calculate scores for each individual by applying the model. Generate a credit score for each borrower, where a higher score indicates a lower risk of default.
9. Cut-off Determination Establish score cut-offs for different risk categories. Set score thresholds for low, medium, and high risk, e.g., scores above 700 indicate low risk, between 500-700 medium risk, and below 500 high risk.
10. Implementation Use the scorecard for real-world credit decisions. Apply the scorecard to evaluate new loan applications, determining interest rates and loan amounts based on calculated risk.
11. Monitoring and Updating Regularly monitor the scorecard’s performance and update it with new data. Periodically reassess the model with new borrower data and adjust variables or weights as needed to maintain accuracy.

Types of Credit Scorecards

Credit scorecards come in various forms, each designed for different stages of the customer lifecycle: application, behavioural, and collections. These scorecards assess credit risk but differ in their focus, data used, and the decisions they inform. Below is a factual comparison in tabular format:

Feature Application Scorecard Behavioral Scorecard Collection Scorecard
Purpose To assess the risk of new applicants and decide on granting credit. To monitor existing customers’ credit behaviour and manage account relationships. To predict the likelihood of recovering debt from delinquent accounts and strategize collection efforts.
Data Used Relies on data available at the time of application such as credit history, income, employment status, and debt-to-income ratio. Uses account behaviour data like payment history, credit utilization, and changes in spending patterns. Focuses on delinquency-specific data including the duration of debt, amount owed, past payment behaviour, and response to previous collection efforts.
Example A bank uses an application scorecard to evaluate a loan application. It considers the applicant’s credit score, annual income, and employment history to decide approval and set terms. A credit card company uses a behavioural scorecard to adjust credit limits based on customers’ spending and repayment patterns over the last 12 months. A collection agency employs a scorecard that prioritizes accounts for action based on the amount overdue and the historical responsiveness of the account holder to collection attempts.
Decision Impact Determines whether to approve or reject credit applications and under what terms (e.g., interest rate, credit limit). Used for managing existing accounts, such as offering credit limit increases, adjusting interest rates, or identifying accounts for closer monitoring. Helps in prioritizing collection efforts on accounts, deciding on the intensity of follow-ups, and potentially negotiating settlements.
Timing Applied at the beginning of the customer lifecycle, at the point of credit application. Utilized throughout the customer lifecycle for ongoing risk management and relationship optimization. Activated after an account becomes delinquent and enters the collections process.

These scorecards are integral tools in credit risk management, each serving a unique purpose tailored to specific phases of the credit lifecycle. They enable financial institutions to make informed decisions, from extending credit to managing accounts and recovering debts, by accurately assessing the associated risks.

What is Probability of Default or PD?

The probability of default is a quantitative measure that indicates how likely it is that a borrower will fail to make payments as agreed upon in the terms of their credit agreements.

Probability of Default is one of the outputs of Credit Scorecard building process:

  • Predictive Purpose: The primary purpose of a credit scorecard is to predict the probability of default. Each score generated by a scorecard represents an estimated risk level of lending to the borrower. A higher score typically indicates a lower probability of default, while a lower score indicates a higher probability of default.
  • Risk Assessment: Credit scorecards use historical data and statistical models to assess risk. By analyzing factors such as payment history, credit utilization, length of credit history, types of credit used, and recent credit inquiries, the scorecard assigns a score that quantifies the borrower’s credit risk. This score is essentially a summary of the borrower’s PD.
  • Modeling Techniques: Various statistical modeling techniques, such as logistic regression, are employed to create the relationship between the borrower’s information (input variables) and the probability of default (output). These models calculate the PD by estimating the likelihood of a borrower defaulting based on their credit behavior and other relevant factors.
  • Decision Making: Lenders and financial institutions use the PD derived from credit scorecards to make informed decisions regarding loan approvals, credit limits, interest rates, and terms of credit. For example, a borrower with a low PD might be offered more favorable loan conditions than a borrower with a high PD.
  • Segmentation and Differentiation: Credit scorecards allow for the segmentation of borrowers into different risk categories based on their PD. This enables lenders to tailor their products and pricing strategies according to the risk profile of each segment.

In summary, the probability of default is a key output of the credit scoring process, and it directly influences the decisions made by lenders. The credit scorecard serves as a tool to estimate PD in a systematic and quantifiable manner, helping financial institutions manage their credit risk effectively.

How Roopya helps in developing Credit Scorecards and calculating Probability of Default or PD?

Roopya is a platform that assists lenders in credit risk assessment, including calculating credit scorecards and estimating probability of default (PD). Here’s how it helps:

  1. Data Preparation and Management:
  • Data Aggregation: Roopya collects and aggregates data from various sources like credit bureaus, bank statements, social media, and alternative data providers. This provides a holistic view of the borrower’s financial profile.
  • Data Cleaning and Preprocessing: It handles missing values, outliers, and inconsistencies in the data, ensuring its quality for model building.
  • Feature Engineering: Roopya creates new features from existing data that are more predictive of creditworthiness, like credit utilization ratio or debt-to-income ratio.
  1. Model Building and Calibration:
  • Machine Learning Algorithms: Roopya employs various machine learning algorithms like logistic regression, gradient boosting, and random forests to build credit scorecards. These models analyze borrower data and predict the likelihood of default.
  • Model Tuning and Selection: It optimizes model parameters and compares different models to select the one with the best performance based on metrics like accuracy, AUC (Area Under Curve), and calibration.
  • Regulatory Compliance: Roopya ensures models comply with local regulations and ethical guidelines for fair lending practices.
  1. Probability of Default (PD) Estimation:
  • Score Interpretation: Once a credit scorecard is built, Roopya assigns a score to each borrower based on their data. This score reflects their estimated PD.
  • Dynamic Scoring: It allows lenders to update scores in real-time as new information becomes available, providing a more accurate PD assessment.

Decision-Making Support: Roopya integrates with existing lending systems, helping lenders make informed decisions about loan approvals, interest rates, and credit limits.

Best In-class Featuresimg

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