How Traditional Models - FICO Score, Vantage Score, and Behavioral Scoring Are Calculated

?? Introduction

Credit scoring models are integral to assessing creditworthiness, ensuring that financial institutions make informed lending decisions. Models like FICO, Vantage Score, and Behavioral Scoring leverage a blend of key influential and derived variables, each assigned specific weightages to calculate scores. These variables impact credit scores by reflecting credit risk through borrower behavior, financial health, and credit utilization patterns. This paper explores how these models are calculated, the weightage of variables, their effects on scores, and recent research advancements in the field.

?? FICO Score Calculation

The FICO Score is one of the most widely used credit scoring systems, developed by Fair Isaac Corporation. It ranges from 300 to 850, with higher scores indicating better creditworthiness.

FICO Scores are calculated using a weighted combination of five key factors:

?? Payment History (35%)

?? Late Payments: Percentage of late or missed payments.

?? Bankruptcies: Number and recency of filings.

?? Defaults: History of loan defaults.

?? Credit Utilization (30%)

?? Total Credit Limit: Sum of all credit lines available.

?? Credit Used: Percentage of total credit used.

?? Length of Credit History (15%)

?? Average Age of Accounts: Age of all open accounts.

?? Oldest Account: Length of time since the oldest account was opened.

??Credit Mix (10%)

?? Diversity of Credit: Types of credit used, e.g., loans, credit cards, mortgages.

?? New Credit (10%)

?? Recent Inquiries: Hard inquiries in the last 12 months.

?? New Accounts: Number of recently opened credit lines.

?? Vantage Score

Vantage Score, developed by major credit bureaus (Equifax, Experian, and TransUnion), is an alternative to FICO, offering a range of 300 to 850. It emphasizes consistency across lenders and credit profiles.

Vantage Score uses six key factors with slightly different weightings:

?? Payment History (40%)

? Missed Payments: Frequency and severity of missed payments.

? Payment Timeliness: Regularity in meeting payment deadlines.

?? Credit Utilization (20%)

? Utilization Ratio: Ratio of total balances to total credit limits.

?? Age and Type of Credit (21%)

? Length of Credit History: Age of oldest and youngest accounts.

? Credit Mix: Balance of revolving and installment accounts.

?? Total Balances and Debt (11%)

? Current Debt: Total outstanding debt across all accounts.

? Revolving Debt: Specific focus on credit card balances.

?? Recent Credit Behavior (5%)

? Recent Applications: Number of credit inquiries.

? New Credit Lines: Credit activity within the past 6 months.

?? Available Credit (3%)

? Remaining Credit Limit: Portion of unused credit across accounts.

?? Behavioral Scoring with Key Variables

Behavioral scoring models focus on real-time transaction behavior, analyzing patterns to predict future repayment abilities. These scores are widely used for existing customers to evaluate credit line increases or additional loans. Behavioral scoring models rely heavily on derived variables and weight factors based on behavior.

? Transaction History (30%)

?? Spending Patterns: Recurring vs. non-recurring expenses.

?? Cash Withdrawals: Frequency and volume.

? Debt Repayment Behavior (25%)

?? EMI Compliance: Timely repayment of loans.

?? Overdraft Usage: Frequency and severity.

? Account Balances (20%)

?? Savings-to-Debt Ratio: Proportion of savings to outstanding liabilities.

? Activity Trends (15%)

?? Seasonality: Spending and repayment habits over time.

?? Peaks/Valleys: Unusual activity spikes or drops.

? External Factors (10%)

?? Economic Environment: Regional unemployment rates and inflation.

?? Industry-Specific Trends: Performance of associated sectors.

?? Derived (Feature Engineered) Variables for Scoring

Outlined the key factors influencing credit scoring, derived through feature engineering techniques.

??? Debt-to-Income Ratio: Measures loan affordability.

??? Credit Utilization Ratio: Indicates financial discipline.

??? Payment Consistency Index: Tracks consistency in timely payments.

??? Revolving vs. Installment Ratio: Balances between credit types.

??? Late Payment Severity: Weighted index of overdue payments.

??? Historical Default Probability: Predicted likelihood of default.

??? Income Stability Index: Evaluates income trends over time.

??? Loan-to-Value Ratio: Derived for secured loans.

??? Early Payment Trend: Detects prepayment patterns.

??? Seasonal Spending Index: Identifies seasonal financial behavior.

??? Economic Stress Indicator: External macroeconomic factors.

??? Credit Limit Growth: Measures expansion of credit lines.

??? Savings Utilization Ratio: Reflects financial buffers.

??? Active Account Ratio: Indicates portfolio activity.

??? Debt Consolidation Index: Tracks consolidation trends.

??? Cash Flow Variability: Stability of income and expenses.

??? Behavioral Risk Index: Aggregated transactional risk.

?? Scoring Models Keep Their Methodologies Confidential

Companies that develop scoring models typically keep their methodologies confidential. This is because the models are considered proprietary assets, and the companies generate revenue by selling the results. However, based on the information requested by banks and credit card companies in applications, it’s possible to identify factors that significantly influence credit scores.

?? Key factors influencing credit scores include

?? Records of bankruptcies, collections, missed payments, and foreclosures listed on customer's credit report.

?? Customer occupation and the duration of employment the current job.

?? Whether customer own or rent the home.

?? The length of time spent at customer current residence.

?? The number of credit inquiries within a specified period.

?? The ratio of used credit to available credit.

?? Customer age.

?? The length of customer credit report.

?? The duration customer credit history has been maintained in the credit bureau's database.

?? Conclusion

Traditional credit scoring models like FICO, Vantage Score, and Behavioral Scoring rely on key influential and derived variables to provide a comprehensive assessment of creditworthiness. Each variable is weighted based on its relevance to risk, impacting scores either positively or negatively. Recent research underscores the integration of big data, feature engineering, and machine learning in refining these models, enhancing accuracy and applicability across diverse financial landscapes.

Important Note

This newsletter article is intended to educate a wide audience, including professionals considering a career shift, faculty members, and students from both engineering and non-engineering fields, regardless of their computer proficiency level.