ClearScape Analytics™ accelerates model development for credit risk portfolio

Increasing a customer base and personalizing credit offers demand managing credit risk profiles using analytics

Sicredi is the first and largest financial cooperatives institution in Brazil. This institution consists of credit unions across the country offering credit, credit cards, checking and savings accounts, certificates of deposit (CDs), investments, payment services, social security, and more. Its market presence includes over 7M clients, managing over R$311B ($64B USD) in assets and R$192B ($39.5B USD) in credit. By valuing relationships and offering its financial solutions to improve the quality of life of its members and society, Sicredi is focused on sustainable growth.

Like other global economies, Brazil is facing economic pressures, such as inflation, slow gross domestic product increases, wage stagnation, and unemployment. These pressures create challenges for financial institutions tasked with predicting and managing delinquency rates of consumer credit offerings.

“Our gross domestic product (GDP) is increasing, but it’s increasing at a slow pace. The result is an increasing delinquency rate in the financial market. To control delinquency, especially in credit, is really hard,” shares Matheus Daniel Pierozan, senior credit risk analyst at Sicredi.

For Sicredi to maintain sustainable growth, it must continue to grow its customer base and increase personalized credit offers while managing payment delinquency risk. Data from multiple sources must be integrated and harmonized to gain a greater understanding of customer profiles and evaluate a customer’s credit risk.

“Our customer base is increasing around 13% a year and our credit portfolio is increasing around 33% a year in the last five years. This is a big increase, and we need to control the delinquency,” Pierozan elaborates. Controlling delinquency affects profit and growth.

Sicredi’s rapidly growing customer base means nearly 40% of its customers are new since 2019. Delinquency rates are at risk of increasing when credit risk models are inaccurate or when data scientists can’t model on all available data.

“Because we have new customers, they bring together new types of behaviors. One of the main problems of credit risk modeling is that you need to update your models often. You need a history period to train models and capture those behaviors,” explains Pierozan. 

Many of the biggest challenges in credit risk management involve analytics and data. Credit risk analysts often struggle with accessing the right data. Information on new customers can be limited to existing credit scores, income, net worth, outstanding loans, and other liabilities, while existing customers have prior institutional history. To create appropriate personalized credit offers that minimize delinquency risk for new customers, credit risk analysts need large amounts of data. Unfortunately, data quality and consistency can pose a challenge. Moreover, adapting to market changes means models must continually be updated to reflect outside credit risk factors.