Companies from various sectors are adopting advanced data analytics technologies with Open Finance and artificial intelligence to increase credit offerings without raising default rates. These innovations allow more precise and personalized credit evaluations, helping consumers manage their finances efficiently and increase their credit limits. According to Bacen data, loans granted to individuals for the purchase of goods increased by 18% in the 12 months up to February 2024, the highest growth in the last five years.
Strategies for risk mitigation include diversifying the credit portfolio and market segmentation, key to dealing with the increasing default rates, which reached 72.54 million Brazilians in May 2024, according to Serasa. A survey by Instituto Locomotiva and MFM Tecnologia revealed that 8 out of 10 Brazilian families are in debt, with credit cards responsible 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 that assist in automating the credit decision-making process, fraud detection, personalized offerings, and correct customer segmentation, allowing for a more precise prediction of default rates and profile monitoring.
It is what Bruno Moura, director of business and marketing at klavi – a company that offers solutions based on Open Finance and Open Data, explains. “We believe that an effective risk analysis strategy should be based primarily 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 evaluates. The specialist also emphasizes that for a safe credit analysis, it is necessary to obtain and analyze a wide range of information about the potential customer, including history, income, current financial capacity, past payment behavior, and any type of data that may statistically have proven its relevance.
In addition, he reinforces that it is necessary to have good monitoring and continuous improvement of the technologies used, implementing systems to continuously monitor the credit performance of customers, the data used for analysis, constant reassessment of models, as well as updating technologies so that agility in the decision-making process remains constant. Linked to both points, it is also important to use robust statistical models like AI for behavioral analysis.
“Using only traditional sources of data (such as credit bureaus) will not improve your view of your customer and, at the same time, will not differentiate you from your competitors. Using other sources as long as following data protection rules and laws is essential to find new improvement opportunities,” emphasizes Moura.
The role of financial education in reducing delinquency
The responsibility of consumers 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 the achievement of people and companies’ conquests.
“Artificial intelligence tools that use Open Finance data are essential for this and can make a difference, ensuring that the person is correctly advised for their consumption and life profile, reducing the possibility of financial misunderstandings and at the same time, showing the consumer that if they have a healthy financial life, the entire ecosystem will benefit,” explains Moura.
According to Open Finance Brasil data, by December 2023, more than 42 million Brazilians already had active consents for data sharing between banks and financial institutions. In addition, in 2023, 15 new APIs were launched, totaling over 30 products with APIs in production, boosting 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 with maintaining low levels of default. Among the main policies are:
(1) Differentiation of the public: different people have different behaviors, so the credit policy needs to be customized for each audience, product, and service.
(2) Evaluation and monitoring of variables: given the countless data variables present in policies, we need to pay attention to quality over time, including assessing 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 used to predict default had to be replaced with new ones. Those who managed to monitor this as quickly as possible had lesser impact.
(3) Joint work with fraud, service, and collection areas: credit is an ecosystem that requires all parts to be consistent and together for a strategy; if something is not right, the impact will be felt throughout the chain.
An example of how a company can significantly increase credit availability without increasing default is by customizing offers, properly managing limits, and monitoring clients throughout the cycle.
“Imagine how many self-employed professionals there are in the country today without a significant credit history but with consistency in their income. With available credit, they would have the opportunity to grow their businesses, investing in tools and equipment that can help them grow even more. With Open Finance, it is possible to provide an appropriate limit to this person, increasing credit availability without increasing default since you know precisely the person’s financial capacity, not just their credit history which is often just beginning,” explains Bruno Moura.
With these approaches, companies hope to expand access to credit responsibly, promoting sustainable growth, and keeping default under control.
