Enhancing Scorecards with Alternative Data: A Step-by-Step Guide

To stay competitive, modern credit institutions must continuously reassess their scoring models. 

Traditional credit scoring considers only socio-demographic and financial information about the consumer. The customer provides the first type of information during the loan application process. The second can be obtained from databases of other lending institutions and credit bureaus.

Such an approach to lending is not always justified. Traditional sources provide a relatively limited set of data. The situation has fundamentally changed with the introduction of innovative technologies, such as AI and ML.

Using digital solutions based on these technologies allows combining traditional and alternative data. This helps create the most comprehensive picture of a potential borrower and reduces credit risks.

The Role of Alternative Data in Credit Scoring

Alternative data is information that lending institutions use to assess the creditworthiness of potential borrowers. For example, utility or rental payments, mobile phone usage, and any other digital footprint activities. 

You can find more details about alternative data in our previous article.

In our progressive world, around 3 billion people remain "credit invisible." Issuing credit to them using a traditional credit scoring model is impossible.

Lenders can reach consumers with no credit history by using alternative data.

Advantages of Enriching Scoring Models with Alternative Data: FICO Insights 

To illustrate the benefits of using alternative data in credit scoring, I would like to share research by FICO, an analytics software company.

FICO conducted a study that confirmed the value of incorporating alternative data sources into risk models for lending.

While traditional credit models primarily rely on application data or credit bureau scores, this study explored the addition of alternative data in predicting credit risk.

The goal was to analyze both traditional and alternative data for the personal loan portfolio. It turned out that traditional data had more value than alternative data. 

FICO was able to produce a more powerful score model by combining the traditional and alternative data characteristics. You can see it in the FICO’s diagram below.

How to Enrich the Scoring Model with Alternative Data

To incorporate alternative data into a scoring model, a lender needs to go through several stages:

1. Identify optimal alternative data sources. Research alternative data providers. Visit demos to understand the data they offer. Look if the provider has clients in your region. Pay attention to companies like RiskSeal, where you can access reliable alternative data.
2. Test a provider. Do not trust a provider solely based on documentation and demos. I advise choosing a data provider that allows you to test their services. For example, RiskSeal offers a free Proof of Concept (PoC) to check the feasibility of their method. You can assess the provider's effectiveness in detecting future defaulters and fraud.‍
3. Update scorecards. After selecting a provider, you should update your scorecards with the new data. We won’t go into detail here, as each organization does it according to its corporate processes.
4. Assess the effectiveness of enriched scoring models. The following activities will help:

• Compare the accuracy of new and old models. Evaluating key metrics will show the true positive rate, true negative rate, and overall accuracy.
• Determine false positives and negatives in both models. The fewer these are, the more reliable the model.
• Evaluate performance. Measure the time and resources required by each model for processing and screening. 
• Compliance rate measurement. Assess how well each model adheres to regulatory compliance standards. 
• User feedback. Listen to the opinions of company employees. Which model is more convenient for them to work with?

After completing all these stages, you can be confident that you are using an optimal scoring model.

Challenges of Using Alternative Data in Scoring

The use of alternative data by lending organizations may present certain challenges. Let's explore them in more detail. 

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1. Organizing diverse data sources. With the increasing diversity of data sources, businesses face the challenge of organizing them efficiently and identifying the most relevant ones for their models.
2. Integrating multiple credit scores. Combining various credit scores into a single model is complex, especially in ensuring that the integration adds value to the predictive power of the model.
3. Ensuring low correlation among scores. The effectiveness of integrating multiple scores hinges on ensuring that these scores (and their underlying data sources) have little correlation with each other. A high correlation might not add significant predictive value to the model.
4. Navigating legal and regulatory requirements. Different regions have varying regulations regarding personal data collection and use. Understanding and complying with these legal terms is crucial, especially in terms of information storage, origin, implementation limitations, and ethical usage.
5. Handling Personally Identifiable Information (PII). Ensuring that PII is managed correctly is a significant challenge. It involves obtaining consent from clients, ensuring data anonymity, and preventing PII from leaving the device.
6. Ethical use of data. Beyond legal compliance, there's also the challenge of using data ethically, which includes considerations of privacy, fairness, and transparency in how data is collected and used in scoring models.
7. Data integration and analysis complexity. The technical aspect of integrating and analyzing data from multiple sources can be complex, requiring sophisticated algorithms and processing capabilities.

Enriching Scorecards With RiskSeal

RiskSeal's credit scoring model constantly improves, using a wide variety of data. We not only consider standard credit metrics but also include unique factors like trust scores.

We regularly update our platform to stay accurate and relevant, adapting to new economic trends and consumer habits.

By integrating this model, scorecards are enriched with diverse, real-time data, leading to better credit assessments.