Alternative data: The key to credit evaluation

Traditional credit evaluation based on scores and credit histories is continually transforming. Financial inclusion and the growing need for credit among people with no credit history or a limited one call for new tools to evaluate users.

This is where alternative data takes center stage. It mainly includes additional information that can reveal important aspects of repayment capacity, solvency and credit risk.

According to an Experian study, only 30% of adults in Mexico have a formal credit history, which limits their access to financing and economic opportunities.

To address this problem, alternative data is transforming the way financial institutions evaluate their customers. By offering a more complete and detailed view, it enables more informed decision-making, promoting greater financial inclusion and optimizing risk management.

What is alternative data and why is it important?

Alternative data, also known as data alternativa, is any type of information that can be used to evaluate an individual's creditworthiness but is not found in a traditional credit report.

This data covers a wide range of sources, from utility and rent payments to social media activity, online browsing habits, employment information, and more.

The relevance of alternative data lies in its ability to provide a more complete and accurate view of an individual's financial situation, especially for those who lack a formal credit history. Credit scoring models that include alternative data are 20% more accurate than traditional models.

This time we won't focus on synthetic data; however, it is worth noting that it is crucial for privacy protection, bias reduction and model validation, providing an effective tool in the field of analytics and machine learning.

By combining traditional data with alternative and synthetic data, a solid foundation is created to promote financial inclusion, improve the accuracy of credit assessment and enable innovation in financial services.

How is alternative data used in credit evaluation?

Alternative data makes it possible to develop more accurate and inclusive credit scoring models. By incorporating additional information beyond traditional data, these models give financial institutions a more complete view of their customers' credit profiles.

To put this into practice, Finvero's alternative credit score assesses applicants with no credit history using nine alternative data families and AI.

• Assess the creditworthiness of users with no or limited credit history.
• Identify high-risk users who could be missed by traditional methods.
• Offer better credit terms to users with good financial behavior.

This not only improves the accuracy of risk assessment, but also facilitates smarter decision-making adapted to individual needs, promoting accessible financial solutions:

Alternative Data for a more complete credit evaluation

According to a study by McKinsey & Company, financial institutions that use alternative data to evaluate creditworthiness report a reduction in the delinquency rate of up to 30%.

Below are examples of alternative data that enrich credit evaluation:

• Payment information: Payment history for utilities, rent, subscriptions, student loans, etc.
• Telecommunications data: Time spent with a mobile phone provider, history of bill payments, data usage and more.
• Social media data: Social media activity, interactions with friends and family, type of content shared.
• Online behavioral data: Online purchase history, browsing habits, usage of e-commerce platforms.
• Employment information: Employment data, income, job stability, type of contract.
• Demographic data: Education, income level, family situation, place of residence, etc.

How data is revolutionizing credit evaluation

With the continuous advance of technology and the growing availability of data, the use of alternative data will become increasingly relevant in the financial industry. However, to maximize its impact, it is crucial to integrate this data with Generative Artificial Intelligence.

This combination enhances credit evaluation by providing a more detailed and tailored analysis. Integrating alternative data with Generative Artificial Intelligence optimizes the process through an automated decision engine, which not only enables more effective and accurate financial inclusion but also reduces fraud risk and significantly increases the approval rate.

Benefits of a Decision Engine integrated with Generative AI and Alternative Data

• Precise segmentation: We use Alternative Data to create detailed profiles of potential customers, enabling precise segmentation and more effective marketing campaigns.



• Automated credit evaluation: Our extensive alternative database, based on digital footprint, social media, web behavior and more, allows us to run accurate credit evaluations and minimize risk and fraud.



• User permissions: We guarantee data security and privacy, as we have thorough processing and protection practices for users' data.



• Improve customer experience: Integrating alternative data and generative AI makes it possible to personalize offers and speed up responses, delivering a better-fitting and more satisfying experience for the customer.



• Reduce delinquency and fraud risk: We identify warning signs and fraud patterns to minimize delinquency and protect the loan portfolio.

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