Build custom risk models with AI and Alternative Data
March 06, 2026 - Product
Credit risk assessment has been fundamental for the growth and promotion of financial inclusion, particularly in Latin America. For decades, traditional models based on credit scores have predominated in the financial sector. However, with the advance of artificial intelligence (AI) and alternative data, the landscape is changing. Financial institutions looking to stay competitive must go beyond conventional methods and adopt customized approaches to risk assessment, powered by AI and non-traditional data sources.
To put this into practice, Finvero's alternative credit score assesses applicants with no credit history using nine alternative data families and AI.
A report by Experian in 2023 revealed that 62% of financial institutions already use alternative data to improve risk profile and credit decision-making functionalities; which shows the transformation towards more inclusive credit evaluation methods.
This article explores how these technologies can redefine credit risk assessment and improve the accuracy, inclusion and profitability of decision making.
Alternative data in credit evaluation
The term "alternative data" refers to non-financial information that can enrich credit evaluation models; from online consumption patterns, social networks, geolocation and payments for public services such as electricity, water or mobile phones. According to Forbes, by 2025 the volume of digital data in the world is expected to reach 175 zettabytes.
Los alternative data, provenientes de fuentes no tradicionales, representan un potencial para las instituciones financieras en América Latina, donde hasta el 45 % de la población adulta no tiene acceso a productos financieros formales. En México, según datos del ENIF 2021, solo el 33.1% de la población cuenta con crédito formal. El uso de estos datos permite incluir a más consumidores en el sistema financiero, además de diseñar ofertas personalizadas y ajustar tasas de interés según las características económicas y sociales del solicitante, lo que reduce el riesgo de morosidad.
The use of this data allows companies to expand their credit offering to a greater diversity of clients and minimize biases in credit evaluation. By offering a more complete view of financial behavior, they help make more accurate decisions and reduce the risk of default, in addition to facilitating product customization, optimizing regulatory compliance and enabling real-time credit assessments, improving approval rates and fostering customer loyalty.
Artificial Intelligence and Machine Learning: Dynamic risk models
La IA y el Machine Learning están redefiniendo los modelos de riesgo crediticio, con un 80% de las grandes instituciones financieras globales ya utilizando estas tecnologías, según un estudio de McKinsey & Company. Algoritmos como XGBoost y random forests han demostrado mejorar la precisión de las predicciones, reduciendo la morosidad hasta en un 25%, según un informe de Experian. El Auto Machine Learning (AutoML) permite optimizar automáticamente los modelos, incrementando su rendimiento sin intervención constante y aumentando las tasas de aprobación de crédito. De esta forma, retrasar la adopción de estas tecnologías incrementa el riesgo y las pérdidas potenciales, por lo que es crucial contar con herramientas avanzadas para manejar grandes volúmenes de datos y hacer predicciones ágiles.
"The adoption of AI and alternative data in credit evaluation has increased approval rates by 30-40% and reduced delinquencies by 20-25%, reducing operational costs by 25%. However, the use of these technologies raises concerns about privacy, which has driven the development of explainable AI (XAI), which allows us to understand how and why artificial intelligence algorithms make decisions while avoiding bias." Deloitte, 2024
The future of credit evaluation is full of innovations, highlighting the use of synthetic data. This is artificially generated data that simulates characteristics of real information, allowing customer behavior to be analyzed without compromising their privacy. Additionally, quantum computing uses principles of quantum mechanics to process data more quickly, ideal for handling large volumes of unstructured data. It is estimated that this technology could reduce optimization times by more than 50% by 2030, according to a report from AWS.
The integration of AI and alternative data in credit evaluation is setting up a model in financial risk management. As institutions adopt these technologies, it is reflected in the accuracy of credit decisions, as well as the inclusion of traditionally excluded customer segments. However, the challenge lies in ensuring a focus on transparency and ethics. It is essential that financial institutions adopt advanced tools to optimize credit performance and promote confidence in access to credit in a digital and diversified financial environment.
In this context, Finvero is the technological ally that offers an AI and alternative data platform that manages the credit life cycle, increasing the approval rate and reducing risks. Contact us to schedule a demo!
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