Advanced Credit Risk Management

This course seeks to strengthen the analytical, technical and leadership skills of participants, enabling them to anticipate, measure and manage credit risks in a way consistent with institutional risk appetite, the regulatory requirements of Basel and IFRS 9, and the business strategy. It also provides tools to design, operate and improve assessment, monitoring and mitigation models, aligned with the best international practices and with the current demands of the financial industry.

Program Content

Module 1: Introduction to the Banking System and Risk Management (2 hours)

• Structure and function of the financial system.
• Banking business and capital markets.
• Local and global banking system.
• Financial products, intermediation and customers.
• Role of the risk area in Banks, the Financial Industry and the Capital Markets.
• Introduction to financial risk: definition, classification and types.

Module 2: Corporate Risk Management Strategy (2 hours)

• The central role of the Chief Risk Officer (CRO) as a strategic change agent and promoter of risk culture throughout the organization.
• Three lines of defense theory applied to risk management.
• Integrating risk with business strategy: Consistency and sustainability.
• Strategic risk maps that make it possible to identify, prioritize and manage key risks over different time horizons.
• Key risk indicators (KPIs) and their impact on financial performance.
• Incorporates risk management into strategic planning, budgeting and corporate objectives.
• Impacts of risk management on the Balance sheet, results and long-term solvency.

Module 3: Integrated Credit Risk Management (13 hours)

Part 1: Credit Origination and Assessment (4 hours)

• Introduction to credit origination. The credit cycle
• Stages of the lending process
• The role of the Risk Underwriting process
• Technical foundations in decision-making
• Credit authority and the role of Risk Committees
• Importance of data as a key input. Evolution of credit analysis.
• Proactive vs. reactive models in credit origination
• Proactive Origination:
  • Automatic approval strategies: Criteria, limits and predictive models.
  • Pre-qualified customers. Analytical tools and commercial approach.
• Reactive Origination – Retail Segment
  • Design and building of scoring models
  • Statistical validation and regulatory compliance of the model
  • Integrating the model into operational flows and decision systems
  • Success indicators: Approval, rejection and performance rates.
  • Model monitoring and recalibration: Maintaining predictive power.
  • Alignment with commercial incentives and strategic planning.

• Origination – Wholesale Segments
  • Design and application of rating models for Corporates
  • Technical validation and model performance monitoring.
  • Use of ratings in credit decisions, limits and pricing.
  • Financial analysis for Large Corporates and the corporate sector
  • Credit Committee governance: Structure, best practices and collegiate decisions.
  • Sector considerations: Real Estate, Agriculture, Financial.

Part 2: Portfolio Monitoring and Preventive Management (4 hours)

• The critical role of “Portfolio Monitoring” in financial stability.
• Credit deterioration cycle: detection, diagnosis and impact management
• The importance of early detection and continuous monitoring: governance, automation, reporting.
• Methodologies for tracking credit quality from origination: Feedback for redirecting policies and management guidelines.
• Early Warning Models and Systems for retail and wholesale portfolios. Methodologies, approaches, integration into management and expected results.
• Management of special follow-ups and specific monitoring, according to risk and exposure.
• Sector analysis: Identifying trends and impacts in specific sectors such as real estate, agriculture, among others.
• Key metrics: KPIs, KRIs and their alignment with institutional risk appetite.
• Design and use of risk matrices for prioritization in management and strategic decision-making.

Part 3: Provisions, Models, approaches and countercyclical management (3 hours)

• The role of provisions in financial stability and countercyclical management.
• Regulatory vs. accounting provisions: Differences between IFRS 9 and local regulations.
• Incurred loss vs. expected loss approach.
• Estimation models based on PD, LGD, EAD and a forward-looking approach.
• Collective vs. individual provisions: Application criteria and segmentation methodologies.
• Practices and challenges in Latin America: Progress, gaps and convergence toward international standards.

Part 4: Collections and Recovery Management (2 hours)

• Customer profiling and segmentation to define personalized and effective collection strategies.
• Differentiated collections management: preventive, early and late approach.
• Debt renegotiation and restructuring: technical and regulatory criteria
• Judicial Collections: Design of procedural strategies, optimization of time and costs.
• Portfolio sale strategies: Optimal timing, valuation and selection of counterparties.
• Classification and treatment of uncollectible loans

Module 4: Financial Regulation Applied to Credit Risk (2 hours)

• Evolution from Basel I to Basel IV: History, objectives and foundations behind prudential regulation.
• Expected vs unexpected loss: Impact on provisions and capital
• Conceptual foundations of capital management. Regulatory and economic capital
• Pillars I, II and III: Approach, objectives and supervisory logic.
• Capital calculation methods: Standardized Approach vs Internal Models.
• Regulatory implications for origination, assessment and credit policies.
• Strategic levers to optimize risk and capital: Asset allocation, exposure limits and active management of the credit portfolio.
• Credit stress tests: The importance of prudential risk management. Solvency, projections and decision-making.

Module 5: Innovation, Artificial Intelligence and Risk Management (1 hour)

• AI applied to Scoring, fraud and decision automation.
• Supervised and unsupervised models applied to the risk cycle.
• Advanced analytics and feature engineering to optimize predictive models.
• Explainable AI (XAI), governance and regulatory compliance.
• Integration of predictive models into origination, monitoring and collections.
• Industry use cases and future trends in AI applied to risk.