How to Assess Credit Scoring Models Enhanced with Alternative Data
Alright, you've changed your scorecards by using alternative data. How can you check if they work well? What metrics you should measure? Check out the article.
A survey by the largest credit bureau, Nova Credit, revealed that 59% of lenders incorporate alternative data into their credit scoring algorithms.
This practice enables them to lend to clients without a credit rating, thereby increasing consumer coverage by 23%.
In this article, we will discuss how to ensure that alternative data improves the credit scoring model and evaluate its effectiveness.
Read one of our previous materials for more details on what alternative data entails and how it can enhance scorecards.
Importance of assessing the effectiveness of credit scoring models
Assessing the effectiveness of credit scoring models is crucial for several reasons:
Risk management. By accurately assessing the likelihood of default, lenders can manage their risk more effectively. An ineffective model could lead to higher default rates.
Adaptation to changes. Regular assessment helps in adapting models to changes, for instance, the impact of a global financial crisis or a sudden economic downturn on borrowers' ability to repay loans.
Innovation and competitive advantage. Credit scoring models can gain a competitive edge by making more accurate lending decisions faster. This can lead to better customer satisfaction, lower costs, and higher profits.
The assessment of credit scoring models is fundamental to the integrity of the credit system. It supports financial stability, promotes access to loans, and ensures that the lending process is as efficient and unbiased as possible.
Criteria for assessing model improvements with alternative data
Not all alternative data provide lenders with equally high-quality information. They may vary in coverage, detail, specificity, relevance, and other characteristics.
To select high-quality alternative data for credit scoring, it is important to use only reliable sources. Below, you will find examples of criteria for alternative credit scoring models.
Accuracy and predictive power. Assess the improvement in the accuracy of forecasts made by models using alternative data. FICO, a company that creates digital solutions for lending organizations, has also done a detailed study of credit scoring models. They focused on how accurately their models can predict outcomes. The research has shown that using their scoring model FICO® Score 10 T lenders can reduce default rates by 10% on credit cards, 9% on auto loans, and 17% on mortgages.
Discriminatory power. Evaluate how the alternative data contributes to distinguishing between creditworthy and non-creditworthy borrowers. It is important to minimize false positive and false negative results. This helps avoid missed lending opportunities and funding unreliable borrowers.
Coverage. Monitor how alternative data expand the scope of analysis by reaching underserved or new customer segments. It is crucial because 1.4 billion people worldwide are unbanked, according to World Bank data. Therefore, they cannot access credit through traditional scoring models.
Specificity. Use high-quality alternative data for credit scoring that allows for individual risk assessment. Lenders can make more personalized credit decisions. For example, a customer with a high credit rating may get a higher credit limit and a lower interest rate.
Orthogonality. Data from alternative sources should be unique and complement traditional data. In our previous article, we mentioned a study conducted by FICO. Its essence was in combining traditional and alternative data. The research results showed that supplementing traditional data with information from alternative sources allowed FICO to create a more powerful scoring model.
Compliance. The use of alternative data must comply with regulatory standards and privacy laws. You should consider the requirements of the Fair Credit Reporting Act, Equal Credit Opportunity Act, and Gramm-Leach-Bliley Act.
Location. Scoring models perform differently in various local markets. Therefore, it is necessary to assess alternative credit scoring models depending on the region where they will be applied.
Metrics important for credit scoring models evaluation
Many credit score metrics let you evaluate the effectiveness of a particular scorecard. Companies do not necessarily need to use all of them.
The list of metrics is determined depending on the strategy and goals of the lending organization. Below is a list of possible metrics used in the lending industry.
Metric
Description
Impact on the lending business
Significance
False positives
Occurrences where the model incorrectly predicts a default.
Missed business opportunities.
Reducing false positives is crucial for maximizing business growth by ensuring potentially profitable lending opportunities are not unjustly declined.
False negatives
Occurrences where the model fails to identify a real default risk.
Financial losses due to unanticipated risks.
Minimizing false negatives is vital for protecting the lender's portfolio from risky loans that could result in defaults and losses.
True positives
Correct predictions where an applicant is identified as a default risk and indeed defaults.
Effective risk assessment capability.
High true positive rates indicate effective risk assessment capabilities, allowing lenders to avoid potential defaults.
True negatives
Correct predictions where an applicant is identified as not being a default risk and does not default.
Maximizing business opportunities.
High true negative rates indicate the model's success in accurately identifying safe loans, ensuring that opportunities are not missed.
Default rate
The percentage of borrowers who fail to repay their loans as agreed.
Overall portfolio risk assessment.
Provides a direct measure of the credit risk within the loan portfolio, guiding risk management strategies.
Acceptance rate
The proportion of loan applications approved.
Portfolio growth potential.
Reflects the model's alignment with business strategy.
Prevalence
The actual proportion of defaulters in the application pool.
Model calibration and effectiveness.
Helps in adjusting the model to the actual risk environment, ensuring that it is neither too strict nor too soft.
Predictive power (AUC)
The ability of the model to correctly classify defaulters vs. non-defaulters
Model accuracy and discrimination.
A higher AUC indicates a model's better performance in distinguishing between defaulters and non-defaulters.
Cost-benefit analysis
Comparison of the costs of false positives/negatives against the benefits of true positives/negatives.
Financial efficiency of the model.
Quantifies the financial impact of the model's predictions, aiding in strategic financial planning.
Lift
Improvement in prediction accuracy is provided by the model over random guessing.
Financial efficiency of the model.
Helps in measuring the effectiveness of the model in identifying default risks more accurately than without the model.
Precision-recall balance
The trade-off between minimizing false positives and maximizing true positives.
The balance between opportunity and risk management.
Important for lenders who need to prioritize between expanding their loan portfolio and minimizing default risks.
Customer Lifetime Value impact
How credit decisions affect the long-term value of the customer base.
Long-term profitability and customer relationships.
Assesses the broader implications of credit decisions on revenue, profitability, and customer engagement.
Back-testing and out-of-time testing to evaluate credit scoring model performance
There are two common tactics for evaluating a credit scoring model:
Back-testing involves using historical data to check the model's credit scoring predictions against actual outcomes.
Out-of-time testing evaluates the model's performance on a dataset from a different period than the one used to train the model.
This tactic helps to evaluate the stability and reliability of the model over time, especially in the face of economic changes or shifts in customer behavior.
Both methods aim to validate the predictive power and stability of the credit scoring model using historical data.
By employing this tactic, organizations can enhance the reliability of their credit scoring models, make informed lending decisions, and better manage their credit risk.
Back-testing and out-of-time testing stages
Historical data selection. At this stage, you need to choose the input variables used by the credit scoring model and the actual lending outcomes. These outcomes could include loan defaults, timely payments, etc.
Model application. Apply the credit scoring model to the historical dataset to predict lending outcomes.
Performance evaluation. Evaluate the accuracy of the model's predictions by comparing the forecasted results with the actual outcomes. Common metrics used for evaluation include the Area Under the Curve (AUC) for the Receiver Operating Characteristic (ROC) curve, Gini coefficient, accuracy, precision, recall, and F1-score.
Adjustments and optimization. Analyze the results of the back-testing or out-of-time testing, and if necessary, make adjustments to the credit scoring model. This may involve recalibrating the model, selecting different variables, or changing the model's parameters.
How RiskSeal improves credit scoring models
RiskSeal has extensive expertise in improving credit scoring models. To assist in this matter, we provide digital lenders with:
Data enrichment. The lenders receive unique information that they cannot get from traditional sources. By choosing RiskSeal, you can expect to receive a vast amount of data – over 300 digital signals.
Digital footprints. We analyze borrowers' activity across more than 140 social networks and online platforms. We also verify information regarding your potential clients' subscription statuses.
The screenshot below displays the actual RiskSeal indicators. We acquired them by analyzing the performance of a client's scoring model after enriching it with alternative data.
The same applies to a digital credit score: the higher the credit score is, the lower the likelihood of default.
FAQ
How does RiskSeal increase the predictive power of credit scoring models?
RiskSeal gathers extensive arrays of alternative data from reliable sources to increase the predictive power of credit scoring models. This includes information from 140+ social networks and platforms.
Clients receive over 300 digital signals.
Why is assessing the effectiveness of credit scoring models important?
Assessing the effectiveness of credit scoring models is important because it helps manage risks, adapt models to changes, access innovations, and gain competitive advantages.
In other words, it supports the credit organization's financial stability and ensures the lending process's efficiency and impartiality.
What metrics are crucial for evaluating credit scoring models, especially with alternative data?
There are numerous credit score metrics crucial for evaluating alternative credit scoring models. Each company selects those that best align with its strategy and goals. Among them are false positives, false negatives, true positives, true negatives, default rate, etc.
How do false positives and false negatives impact the effectiveness of credit scorecards?
False positives and false negatives negatively impact the effectiveness of credit scorecards.
In the first case, the model incorrectly predicts default, leading to missed profitable lending opportunities. In the second case, the model fails to identify the real risk of default, potentially resulting in loans granted to non-creditworthy borrowers.
How do back-testing and out-of-time testing improve credit scoring model performance?
Back-testing allows evaluating the correspondence of credit scoring model forecasts to actual results. Out-of-time testing assesses the model's performance on a dataset from a different period than that used for model training.
Based on the results obtained, informed adjustments can be made to the credit scoring model.
What role do digital footprints play in improving credit scorecards?
Digital footprints provide additional data for assessing the creditworthiness of potential borrowers. They have broader consumer coverage than traditional data, thus allowing lending to clients without a credit history.
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