Financial Risk Management Overview

International Master in Financial Risk Management

• The program focuses on the role of risk management in financial institutions, considering ICT and security risks.
• The program is offered by the Politecnico di Milano Graduate School of Business.
• The program has partnerships with various organizations like FT, QS University, The Economist, Bloomberg, alongside various accreditation bodies like AACSB, EQUIS, AMBA, and EFMD.

PwC, Risk, Capital & Reporting Team

• Romina Vignotto, Alessandro Vistocco, and Ludovico Villani are part of the Risk, Capital & Reporting team at PwC.
• Contact information for each individual is provided.

Agenda

• The role of Risk Management in the Financial Industry
• Artificial Intelligence and Machine Learning
• Digital Operational Resilience
• Cyber Risk

Risk Management Responsibilities within a Financial Institution

• Governance:
  • Identify material risks
  • Link material risks with key risk indicators.
  • Support the Board of Directors in the identification of risk appetite thresholds & tolerances.
  • Support capital & liquidity planning.
  • Support the crisis management process (recovery & resolution).
• Measurement:
  • Develop models to measure risks for prudential or managerial purposes (Pillar 1, Pillar 2 models), or for accounting purposes (ECL under IFRS9).
  • Define qualitative approaches for risks where quantitative ones are not feasible. -Validate measurement models & risk parameters.
• Monitoring & Control:
  • Verify risk appetite defined by the Board.
  • Execute early warning systems for signals of deterioration.
  • Execute 2nd level controls to detect risks.
• Reporting:
  • Represent risk-driven information.
  • Ensure that monitoring and control outcomes are reaching relevant company functions.

Over the Last Decade

• The role of risk management for financial institutions is constantly evolving, driven by factors like:
  • The evolution of Competent Authorities and the extension of the regulatory framework.
  • Internal complexity of the institution, reflected by its business model, geographical extension and product innovation.
  • Progressive digitalization and externalization of processes.
  • Technological and digital innovation, affecting data handling and risk management processes and tools.
  • Macro-economic factors, such as low vs. high interest rates, and impacting the financial institutions' profit & losses.

One of the Main Drivers of Change

• Technological and digital innovation is a main driver of change in Risk Management.
• Key drivers enabling the impact on Risk Management :
  • Availability of Big Data (e.g. paper-based information, web & geolocation/psychometric technologies)
  • Availability of programming languages for data processing.
  • Availability of improved data storage technologies.
  • Contributions from FinTech for data and methodologies (e.g. ESG scores and cybersecurity scores).
  • Evolution of malware techniques to attack systems.

One of the Main Drivers of Change - Implications

• Models improved through new data and programming languages.
• Model risk is increased due to new data and models, and needs to be managed for proper interpretation.
• New material risks (e.g. cyber and third-party risks) need to be considered.
• Risk control frameworks need to adapt to new risk types.
• Data quality is critical for accurate risk analysis.
• Corporate reporting needs enhancement through risk metrics.
• Steering & decision-making capacity is increased.

Overview of the Main Impact Drivers

• This section summarizes the implications of various impact drivers on Risk Management activities. (Note: The chart details are quite broad and no specific measurements are detailed).

Regulatory Background (Circ. 285/13 Bank of Italy, 40° agg.)

• The scope includes banks, parent companies of banking groups, investment firms (SIMs), and parent companies of investment firm groups.
• Key modifications concerning the internal control framework, ICT systems, and business continuity.
• The regulation emphasizes EBA guidelines for ICT and security risk management.
• Dates for regulatory compliance and reporting requirements are listed.

Set of an Internal Function Focusing on the Control of ICT and Security Risks

• Banks should establish a function within the second line of defense (2nd LoD) to oversee and manage ICT & security risks.
• This function can be a new, dedicated function or integrated into existing Risk and Compliance functions.

Artificial Intelligence and Machine Learning

• Discusses applications and flaws in traditional modeling related to financial institution operations.

AI/ML more conveniently infer the generating mechanism of stochastic processes

• Discusses the use of "virtually infinite" time series to infer the generating mechanisms of stochastic processes.
• Describes various methods and techniques.

Data Preparation & Sampling

• Covers topics including data enrichment, parsing of text, data transformation and data sampling (bootstrapping and k-fold cross-validation techniques).

Model design and parametrization

• Discusses choices for parametric vs. non-parametric techniques in model design and their assumptions.

Model Estimation

• Various Machine Learning (ML) algorithms are covered for credit-risk modeling (linear regression, logistic regression, perceptron etc.).

Model Enhancement

• Techniques (Gradient boosting and others) are discussed for improving model accuracy and reducing bias and variance.

Selection of the Final Learning Method

• Importance of generalisation, using test data to evaluate model performance.

Improve the Risk Differentiation by enriching the model with transactional modules

• Details data sources for enriching models with transaction data for better risk differentiation.

Automate the fundamental analysis of the financial market

• Explains how automated fundamental analysis can aid risk assessment on various market factors.
• Describes the automated insight into market trends and concerns.

Increasing depth of research to capture emergent and dominant trends in financial markets

• Identifies the phases of researching the financial market including Speech to Text, Topic Modelling, Sentiment Analysis, and Text Summarization.

Exploring the Role of Generative AI in Revolutionizing Banking Risk Management

• Discusses the use cases of generative AI in banking, such as code generation, document drafting, risk data analysis, and regulatory analysis
• Highlights key aspects of generative AI, like deployment, validation and fine-tuning.
• Outlines key challenges, including data privacy, costs, hallucinations, and skills in implementing the technology.

Digital and Operational Resilience

• Discusses the importance of Digital and Operational resilience.

Digital Operational Resilience Act (DORA)

• Discusses the regulatory framework of DORA, its pillars, and timeline.

Cyber Risk

• Discusses the framework, guidelines, and various threats related to cyber risks.

Cyber Risk Capabilities

• Discusses the various capabilities required for robust cyber risk management.

Mapping between Cyber Risk scenarios & capabilities

• Maps between various cyber-risk scenarios and relevant capabilities.

Cyber Risk Methodological Building Blocks

• Describes a structured approach for calculating and evaluating cyber risk.

Cyber Risk assessment methodology

• Explains how to combine cyber risk components to measure cyber risk.

Cyber Risk Indicators

• Provides a selection of key indicators for measuring cyber risk.

Third Party Risk Management Framework

• Explains the different steps & aspects of a Third Party Risk Management (TPRM) Framework.

Managing Third Party Dependencies

• Discusses considerations around managing the dependency on third parties to mitigate risks.

Traditional Journey towards TPRM Framework

• Shows a flow diagram of the process and describes the aspects involved.