Companies from various sectors are adopting advanced data analysis technologies with Open Finance and artificial intelligence to increase credit offerings without raising default rates. These innovations enable more accurate and personalized credit assessments, helping consumers manage their finances efficiently and increase their credit limits. According to Bacen data, the granting of loans for the purchase of goods by individuals increased by 18% in the 12 months up to February 2024, the highest increase in the last five years.
Risk mitigation strategies include diversifying the credit portfolio and market segmentation, which are essential to address the rising default rate, which reached 72.54 million Brazilians in May 2024, according to Serasa. A survey by the Locomotiva Institute and MFM Tecnologia revealed that 8 out of 10 Brazilian families are in debt, with credit cards accounting for 60% of overdue debts. Experts emphasize that the effectiveness of increasing credit availability lies in the complexity of risk analysis, enabled by AI tools, which assist in automating the credit decision process, fraud detection, offer personalization, and accurate customer segmentation, allowing for more precise default prediction and profile monitoring.
This is what explains Bruno Moura, business and marketing director of Klavi – a company that offers solutions based on Open Finance and Open Data. "We believe that an effective risk analysis strategy should be primarily based on a data analysis culture, in which new sources of information are constantly evaluated and old sources are regularly monitored, given that public behavior changes frequently," he/she/they assess. The specialist also emphasizes that for a secure credit analysis, it is necessary to obtain and analyze a wide range of information about the potential client, including history, income, current financial capacity, past payment behavior, and any type of data that can statistically prove its relevance.
Furthermore, he emphasizes the need for good monitoring and continuous improvement of the technologies used, implementing systems to continuously monitor the credit performance of clients, the data used for analysis, and constant re-evaluation of the models, as well as updating technologies to ensure that agility in the decision-making process remains constant. Attached to the two points, it is also important to use robust statistical models like AI for behavioral analysis.
"Relying solely on traditional data sources (such as credit bureaus) will not improve your understanding of your customer and, at the same time, will not differentiate you from your competitors. Using other sources, as long as they comply with data protection rules and laws, is essential to find new opportunities for improvement," emphasizes Moura.
The role of financial education in reducing default
Consumers' responsibility in the use of financial resources is also a crucial aspect throughout the journey. In this sense, Bruno Moura explains that financial education plays a fundamental role, being the smartest way to prove that, if well managed, credit will be vital for achieving personal and business accomplishments.
Artificial intelligence tools that use Open Finance data are essential for this and can make a difference, ensuring that individuals receive proper advice tailored to their consumption and lifestyle profiles, reducing the likelihood of financial mismatches and, at the same time, showing consumers that if they maintain a healthy financial life, the entire ecosystem will benefit, explains Moura.
According to data from Open Finance Brazil, by December 2023, more than 42 million Brazilians already had active consents for data sharing between banks and financial institutions. Additionally, in 2023, 15 new APIs were launched, totaling more than 30 products with APIs in production, driving billions of weekly calls in phase 2 of Open Finance.
Linked to financial education, the role of companies is to implement credit policies to balance granting credit with maintaining low default rates. Among the main policies are:
(1) Audience differentiation:Different people have different behaviors, so credit policies need to be customized for each audience, product, and service.
(2) Assessment and monitoring of variables:Given the numerous data variables present in the policies, we need to pay attention to quality over time, including to assess whether there has been a change in behavior and if there are impacts on the expected results. An example was the pandemic: new behaviors were created and data that previously predicted default had to be replaced with new ones, and those who managed to monitor this as quickly as possible experienced less impact.
(3) Joint work with fraud, customer service and collections areas:Credit is an ecosystem that requires all parts to be consistent and united in pursuit of a strategy; if something is not correct, the impact will be felt throughout the entire chain.
An example of how a company can significantly increase credit availability without raising default rates is by personalizing offers, managing limits properly, and monitoring clients throughout the entire cycle.
"Imagine today how many self-employed professionals exist in the country who do not have a significant credit history but have consistent income and, with available credit, could grow their businesses by investing in tools and equipment that could make them grow even more. With Open Finance, it is possible to give this person an appropriate limit, increasing credit availability without increasing their default risk, after all, you know exactly the person's financial capacity and not just their credit history, which is often just beginning," explains Bruno Moura.
With these approaches, companies aim to expand access to credit responsibly, promoting sustainable growth and keeping default rates under control.
