Desenvolvimento de um motor de crédito automatizado integrado com a API do Serasa: aplicação prática em ambiente empresarial

Abstract

This work proposes the development of an automated credit engine integrated with the Serasa Experian API, aiming to optimize the credit analysis and granting process in companies by reducing reliance on manual evaluations and increasing decision-making efficiency. Traditional credit analysis, although essential for mitigating financial risks, is often slow, subjective, and prone to human error. In this context, automating the process emerges as a strategic solution to speed up responses, standardize criteria, and improve the customer experience. The study addresses the fundamentals of credit analysis, highlighting the importance of variables such as credit score, payment history, time in the market, and registration data. It also analyzes existing market solutions, such as the credit engines offered by NeoWay, Quod, and Stoque, identifying their advantages and limitations. The methodology combines exploratory research, software development based on specific requirements, and practical validation through a case study at Tramontina. The proposed system follows an automated decision flow, applying parameterized rules to approve or forward credit requests for manual review. Among the evaluated criteria are: available credit limit, customer inactivity period, average payment delay, Serasa score, credit risk, and negative records. The implementation of this credit engine aims not only to accelerate the process, but also to increase decision accuracy, reduce defaults, and integrate data in a structured way into the company's internal system. [resumo fornecido pelo autor]