Statistical modeling and risk management, the way banks do it
We apply the same technical standards used at institutions regulated by Brazil's Central Bank (BACEN) to solve problems of risk, liquidity, and decision-making, for banks, insurers, and companies that need real statistical rigor.
From raw data to a defensible decision
Statistical Modeling & Machine Learning
Building, validating, and monitoring predictive and risk models, with documented, reproducible methodology.
Model benchmarking
Technical comparison between the model in use and alternatives, performance metrics, robustness, and regulatory compliance.
ALM and IRRBB
Asset-Liability Management and measurement of interest rate risk in the banking book: liquidity gaps, duration, and stress scenarios.
Financial forecasting and scenarios
Time series, simulations, and scenarios (base, optimistic, stress) to support strategic and budgeting decisions.
Artificial Intelligence Advisory
Support for adopting AI/ML in decision-making processes: from model selection to governance and ongoing monitoring.
Support for regulatory requirements
Technical and statistical support to meet BACEN requirements and those of related regulatory bodies.
Analytics & Business Intelligence
Design and construction of dashboards, KPIs, and automated reports: the right metric, updated on its own, instead of a spreadsheet rebuilt every week.
Banks and insurers at the center, but with the door open for those who need to go further
The technical core of the consultancy was built to the standard of rigor required by banks and insurers. Companies in other sectors with advanced statistical modeling needs are welcome and assessed case by case.
Four steps, no shortcuts
Diagnosis
Understanding the real problem, the available data, and the regulatory or business context.
Modeling
Building or validating statistical and ML/AI models, with methodology documented from start to finish.
Validation
Goodness-of-fit tests, backtesting, and benchmarking against alternatives, nothing goes into production without proof.
Follow-up
Support for implementation and ongoing monitoring of results, with adjustments as the model is used.