"Reducing our release time from three weeks to, in some cases, just one day was a brutal efficiency leap. We achieved a 15% increase in our credit concessions in the first quarter, all while operating under a rigorous layer of observability that gives us total control over risks."
Client (Bunge)
The operational challenge involved the need to perform credit analysis, rating calculation, and delay prediction based on a complexity of 2,000 variables, covering 8 Brazilian regions and processing the company's entire historical customer base. Beyond the large volume of business data, the project required the application of rigorous observability for generating alerts related to the infrastructure and APIs involved in this critical process.
To solve this end-to-end complexity, our engineering team developed and implemented a powerful AI/ML engine focused on behavior analysis and data integration. The technical foundation was established through the construction of a Data Lake in AWS cloud, operated with modern DevOps pipelines. As a fundamental governance pillar, the solution included the continuous provision of alerts and observability dashboards aimed at predictive risk identification.
The implementation delivered extremely high operational and financial impact results, fully automating the score and delay calculation process — a task that was previously performed manually. As a commercial reflection of this efficiency gain, the company obtained a 15% increase in credit release within the first 3 months of use. The waiting time for credit release suffered a drastic reduction: from 3 weeks to a period of 1 to 4 days, depending on approval levels. Finally, the continuous observability process enabled the immediate identification and resolution of operational risks.
It is a global leader in agribusiness and food, headquartered in the United States, with a strong presence in connecting farmers and consumers. The company processes grains and oilseeds to provide food, animal nutrition ingredients, and essential biofuels worldwide.
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