Arthur Rossi Ferreira

Driving data-driven collection strategies through the development of customized analytical solutions across diverse client portfolios. • Delivered tailored models with KS improvements of up to 10 p.p., improving segmentation and decision-making in both early and late-stage collections • Generated actionable insights to optimize recovery strategies and operational efficiency • Bridged the gap between analytics and business, translating models into real-world impact • Collaborated with commercial and technical teams to align solutions with client objectives

Developed and implemented data-driven credit risk strategies for credit cards and consortium products, focusing on risk segmentation and decision optimization. • Built and deployed credit policies and limit strategies using advanced analytics • Designed consortium risk models and policies (PJ/PF), achieving a 60% reduction in default rates • Leveraged Python, SAS, SQL, and Databricks for large-scale data processing and model development • Delivered dashboards and monitoring tools in Power BI to track portfolio performance and strategy effectiveness • Translated analytical models into practical credit decision frameworks

Drove the transformation of credit card impairment modeling under IFRS 9, ensuring regulatory compliance and improving risk assessment frameworks. • Led the transition from Resolution 4,966 to 2,682, redesigning the entire credit loss calculation process • Strengthened provisioning accuracy and model governance through improved methodologies • Built scalable analytical solutions using SQL, R, and AWS SageMaker • Delivered robust models aligned with regulatory requirements and business needs

Developed data-driven credit policies for overdraft (LIS) and revolving credit products, focusing on risk segmentation and portfolio optimization. • Built and refined credit decision rules and limit assignment strategies • Conducted portfolio monitoring and performance analysis across customer segments • Developed analytical models and studies using SQL, R, and SAS • Generated insights to improve approval strategies and risk control • Supported policy implementation through data-driven decision frameworks

Developed customized credit risk models for large financial institutions, supporting portfolio strategies and business decision-making. • Built tailored predictive models for different client portfolios, improving risk assessment and segmentation • Conducted analytical studies, model validation, and backtesting to ensure model performance, stability, and robustness • Developed Quod’s first-generation income prediction model for individuals, enhancing credit analysis and decision-making in data-scarce environments • Translated model outputs into actionable insights to support business strategies • Worked with large-scale datasets using R

Supported corporate credit risk management by developing analytical solutions and monitoring models used in provisioning and portfolio management. • Monitored credit risk models and ensured performance stability across corporate portfolios (PJ) • Built dashboards to provide visibility into portfolio risk, performance, and provisioning metrics • Modeled key parameters (PD, LGD, EAD) to improve accuracy of credit loss estimation • Contributed to provisioning (PDD) processes, enhancing decision-making and risk control • Collaborated with stakeholders to align analytical outputs with business needs

Contributed to the development of data-driven credit risk solutions, focusing on automation and predictive modeling. • Automated modeling workflows, increasing efficiency and reducing manual processes • Developed credit risk models to support business decision-making • Built machine learning models, expanding analytical capabilities • Developed “never paid” models, improving early risk identification and credit strategy • Supported end-to-end modeling processes, from data preparation to validation

Contributed to the development of analytical solutions for collection strategies, focusing on data structuring and machine learning applications. • Optimized SMS campaign structures for large-scale collection operations • Applied machine learning techniques to support billing and collection analytics • Performed data preparation and structuring for model development and deployment • Supported end-to-end analytical processes, from data handling to implementation

Led finance and administrative functions in a student-run consulting organization, supporting project delivery across multiple industries, with emphasis on healthcare and clinical research. • Managed budgeting, financial controls, and operational processes for consulting activities • Oversaw financial performance of projects, ensuring efficient resource allocation • Contributed to consulting engagements in healthcare and clinical trials, supporting analytical and strategic initiatives • Coordinated cross-functional teams to deliver client-focused solutions • Improved internal processes to enhance efficiency and governance