Model Development

A robust risk model must encompass several core components to deliver reliable and meaningful results. These components form the foundation for precise risk measurement and management in modern risk management.

🧩 Conceptual Framework:

📊 Data Foundation and Quality:

⚙ ️ Statistical Methodology:

🔄 Validation and Review:

🛡 ️ Implementation and Governance:

Integrating traditional statistical methods with modern machine learning approaches enables combining the strengths of both worlds to develop more robust, higher-performing risk models.

🔄 Complementary Strengths:

🏗 ️ Architecture Models:

📊 Methodological Integration:

🧪 Practical Examples:

Data quality forms the foundation of every reliable risk model. Without high-quality data, even the most advanced modeling techniques cannot deliver reliable results.

🎯 Core Aspects:

📋 Specific Requirements:

🔍 Quality Assurance Processes:

⚙ ️ Governance Requirements:

Selecting the appropriate modeling methodology for different risk types is a critical strategic decision in risk management.

🧩 Systematic Selection Process:

📊 Methodology by Risk Types:

🔄 Application Context:

⚖ ️ Balancing Complexity and Practicality:

Developing robust credit risk models for different exposure classes requires a tailored approach that considers the specific characteristics of each class.

🏦 Corporate Exposures:

🏠 Retail Mortgage Financing:

💳 Consumer Loans and Credit Cards:

🏭 Specialized Financing:

🏢 Commercial Real Estate:

🏛 ️ Public Institutions:

Integrating macroeconomic factors into risk models is critical for capturing systematic risks and developing forward-looking forecasts.

🔍 Relevant Factors:

🔄 Methodological Approaches:

📊 Quantification Methods:

⚙ ️ Implementation by Risk Type:

🛠 ️ Practical Applications:

9 / CECL impairment models

Developing advanced market price risk models requires deep financial economic knowledge, mathematical-statistical expertise, and practical implementation competence.

📊 Evolution Stages:

🔄 Non-linear Market Dynamics:

🔗 Advanced Dependency Modeling:

📏 Advanced Risk Measures:

🔬 Extreme Risks and Stress Scenarios:

Stress tests play a central role in modern risk modeling and form an essential complement to statistical models.

🎯 Functions and Objectives:

🔄 Typology and Methodology:

2008 crisis, COVID‑19)

⚙ ️ Framework Elements:

📊 Integration into Risk Modeling:

🔄 Continuous Development:

Effective Early Warning Systems (EWS) are essential for anticipating risks before they materialize.

🔍 Fundamental Components:

📊 Signal Identification Methods:

🔢 Indicators by Risk Domain:

⚙ ️ Business Process Integration:

📈 Continuous Improvement:

Validating complex risk models requires a systematic, multidimensional approach that considers both quantitative and qualitative aspects.

🎯 Validation Objectives:

🔍 Validation Dimensions:

⚙ ️ Methods by Model Type:

🛠 ️ Framework Implementation:

📋 Governance Aspects:

Integrating expert knowledge into quantitative risk models connects human experience and judgment with data-driven approaches.

🧠 Value Contribution:

🔄 Methodological Approaches:

⚙ ️ Knowledge Extraction Processes:

🔬 Application Examples:

The optimal balance between model complexity and practical applicability is a central challenge in risk modeling.

⚖ ️ Fundamental Principles:

🔍 Complexity Dimensions:

🛠 ️ Optimization Approaches:

💼 Practical Decision Aids:

🔄 Continuous Evaluation:

Emerging risks such as climate risks, cyber threats, or disruptive technologies pose particular challenges for risk modeling.

🔍 Particular Challenges:

🌐 Advanced Modeling Approaches:

🌍 Climate Risk Modeling:

🔒 Cyber Risk Modeling:

🚀 Technology Disruption Risks:

Developing effective models for liquidity risks requires a holistic approach considering both idiosyncratic and market-wide liquidity drivers.

🌊 Fundamental Dimensions:

📊 Modeling Approaches:

💰 Funding Liquidity Models:

📈 Market Liquidity Models:

⚡ Intraday Liquidity:

🔗 Integration Aspects:

Developing advanced AI-based credit risk models offers significant potential for improving risk assessment.

🧠 Application Areas:

📊 AI Methods:

🎯 Advantages over Traditional Methods:

⚠ ️ Challenges and Solutions:

🔄 Implementation Best Practices:

Integrating model risk management into the overall risk strategy is critical for systematically addressing risks from complex models.

🎯 Strategic Anchoring:

🏗 ️ Governance Structures:

📋 Framework Components:

📊 Risk Quantification:

🔄 Continuous Improvement:

Neural networks offer particular advantages for risk management through their ability to capture complex non-linear relationships.

🧠 Architecture Design:

📊 Data Preparation:

🔄 Training and Optimization:

🔬 Validation and Interpretability:

⚙ ️ Production Deployment:

Successful implementation and operationalization of risk models requires a systematic approach integrating technical, organizational, and cultural aspects.

🏗 ️ Implementation Architecture:

🚀 Deployment Strategies:

📊 Operational Monitoring:

🤝 Organizational Integration:

🔄 Continuous Improvement:

Proven practices in risk model development ensure both methodological quality and practical applicability.

🎯 Strategic Alignment:

📊 Data Management:

🧪 Modeling and Validation:

⚖ ️ Governance:

🚀 Implementation:

🔄 Monitoring:

The ethical dimensions of risk modeling are gaining increasing importance as models increasingly influence decisions with direct impacts on individuals and society.

⚖ ️ Fairness and Non-Discrimination:

🔍 Transparency and Explainability:

🛡 ️ Data Protection:

🌐 Societal Impacts:

⚙ ️ Governance:

🔄 Continuous Monitoring: