May 16, 2024

AI and Alternative Data: Transforming Credit Risk in Latin America’s Booming Banking as a Service Market

AI and Alternative Data: Transforming Credit Risk in Latin America’s Booming Banking as a Service Market

The financial landscape in Latin America is evolving rapidly, driven by the digital transformation that has reshaped the way businesses and consumers interact with financial services. In March, Fintech Nexus reported that Latin America’s Banking as a Service (BaaS) market is projected to surpass $2 billion, a significant milestone for the nascent sector. This growth is fueled by the region’s increasing embrace of digital payments and online banking, catalyzed by the COVID-19 pandemic. As fintech firms and non-bank enterprises race to expand their digital banking capabilities, the need for innovative credit risk solutions has never been greater.


In this dynamic environment, risk executives at banks and fintech companies face a unique challenge: how to accurately assess creditworthiness and manage risk while expanding their customer base to include underserved populations. Traditional credit data, often limited and outdated, is insufficient for this task. Instead, a larger universe of alternative credit data combined with AI-driven models is essential to power credit risk decisions and reach underbanked and informal workers.



The Growing Demand for Alternative Credit Data in Banking as a Service


The demand for alternative credit data in Latin America is rising as fintech companies and banks seek to enhance their risk assessment capabilities. Traditional credit scoring methods, which rely heavily on formal credit history, exclude a significant portion of the population. According to the World Bank, approximately 45% of adults in Latin America do not have a bank account, and many more lack a formal credit history. This presents a substantial challenge for financial institutions aiming to expand their customer reach.


Alternative credit data, which includes non-traditional information such as utility payments, rent, phone bills, and even social media activity, provides a more comprehensive view of an individual’s financial behavior. This data can be particularly valuable in assessing the creditworthiness of individuals with limited or no formal credit history. For instance, regular payment of utility bills can indicate financial responsibility, while patterns in social media activity can offer insights into an individual’s stability and reliability.


The Role of AI-Driven Models in Credit Risk Assessment


While alternative credit data provides valuable insights, it is the power of AI-driven models that truly transforms this data into actionable credit risk assessments. AI algorithms can analyze vast amounts of data from diverse sources, identifying patterns and correlations that would be impossible for human analysts to detect. These models can then generate comprehensive risk scores, enabling financial institutions to make more informed credit decisions.


AI-driven models are particularly effective in identifying creditworthy individuals among underbanked and informal workers. By considering a wide range of alternative data points, these models can create a detailed profile of an individual’s financial behavior, even in the absence of traditional credit history. This allows banks and fintech companies to extend credit to a broader customer base, driving financial inclusion and expanding their market reach.


Case Study: The Impact of Alternative Data and AI in Credit Risk Management


Consider the case of a leading fintech company in Brazil that implemented an AI-driven credit risk assessment model incorporating alternative credit data. Before adopting this approach, the company struggled to accurately assess the creditworthiness of informal workers and individuals with limited credit history. By integrating data from utility bills, mobile phone payments, and social media activity, the AI model was able to generate detailed risk scores for these individuals.


The results were remarkable. The company saw a 25% increase in the approval rate for credit applications from underbanked individuals, while maintaining a low default rate. This not only expanded their customer base but also contributed to financial inclusion by providing access to credit for individuals who had previously been excluded from the formal financial system.


The Path Forward for Risk Executives


For risk executives at banks and fintech companies, the path forward is clear. Embracing alternative credit data and AI-driven models is essential to navigate the challenges of credit risk assessment in Latin America. By leveraging these technologies, financial institutions can accurately assess the creditworthiness of a broader range of individuals, including underbanked and informal workers, thereby expanding their customer reach and driving financial inclusion.


The importance of this approach is underscored by the ongoing regulatory developments in the region. In 2024, Brazil’s central bank will introduce regulations for Banking as a Service (BaaS) as part of its ambitious financial innovation agenda. These regulations aim to create a dedicated framework for the sector, facilitating the integration of alternative credit data and AI-driven models into the financial system.





The future of credit risk management in Latin America hinges on the ability of financial institutions to embrace alternative data and AI-driven models. These technologies offer the potential to transform credit risk assessment, enabling banks and fintech companies to accurately evaluate the creditworthiness of underbanked and informal workers. As the region continues to experience rapid digital transformation, the adoption of these innovative solutions will be critical to driving financial inclusion and expanding customer reach.


At 1datapipe, we understand the importance of leveraging AI-powered customer analytics to empower financial institutions and fintech companies. Our solutions help risk executives accurately assess income and accelerate customer approvals, particularly for underbanked populations and informal workers. To learn more about how our AI-powered analytics can benefit your organization, contact our team for a detailed consultation.


Are you ready to embrace the future of credit risk management and unlock the potential of alternative data and AI?