Financial Risk Management Overview

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What does sentiment analysis specifically determine from input text?

The average word length in the text

The structural integrity of the text

The potential audience for the text

The sentiment of the text, such as positive, negative, or neutral (correct)

Which of the following best describes the role of text summarization in information processing?

It extracts relevant entities from the text

It enhances natural language search capabilities

It converts unstructured text into structured data

It condenses large text files into shorter versions to facilitate accessibility (correct)

Which deep learning technique is often utilized by GenAI to mimic real-world examples?

Support Vector Machines

Generative Adversarial Networks (GANs) (correct)

Reinforcement Learning

Decision Trees

How does risk management application benefit from trend prediction?

It investigates short-term trends in relation to longer-term perspectives Signup and view all the answers

What is operational resilience regulations focused on?

Ensuring banks can withstand and recover from disruptions Signup and view all the answers

Which analysis technique helps in extracting entities like locations and organizations from text?

Entity Extraction Signup and view all the answers

What is a primary use of sophisticated AI techniques in risk management?

Analyzing patterns to determine risk probabilities Signup and view all the answers

In the context of financial applications, why is AI deployment critical?

It enhances decision-making through improved data analysis Signup and view all the answers

Which method would be most effective in deriving insights from unstructured text?

Topic Modeling Signup and view all the answers

What role do embeddings play in risk management applications utilizing AI?

They assist in understanding relationships between different data points Signup and view all the answers

What is a primary benefit of deploying AI solutions in financial institutions?

Scalable computational capabilities Signup and view all the answers

Which technique is used alongside pre-trained models for fine-tuning in AI applications?

Reinforcement Learning from Human Feedback (RLHF) Signup and view all the answers

In the context of risk management, how can AI models contribute effectively?

By generating predictions and new information Signup and view all the answers

Which of the following is a method for validating AI-generated outputs?

Combining GenAI-based techniques with human judgment Signup and view all the answers

What role does prompt engineering play in AI applications?

It helps clarify instructions for desired outcomes Signup and view all the answers

How does AI assist in quantitative analysis within financial sectors?

By enhancing data interpretation with advanced algorithms Signup and view all the answers

What is a potential future use case for AI in financial institutions?

Enhanced customer interaction techniques Signup and view all the answers

What should financial institutions expect when deploying AI technologies?

Maintenance of operational resilience regulations Signup and view all the answers

What is a potential issue with low-frequency data in observation for developing models?

It may not reflect true unknown relationships under changes in behavior. Signup and view all the answers

What advantage does AI/ML have in the context of time series analysis?

They can handle virtually infinite time series through bootstrapping. Signup and view all the answers

In the context of non-parametric function estimation, what is reduced in the training sample?

Bias of the point/probabilistic prediction Signup and view all the answers

What is the goal of enhancing model representation in AI deployments for finance?

To reduce the variance of predictions in the validation sample. Signup and view all the answers

Which of the following statements about AI's role in risk management applications is accurate?

AI can handle both labeled and unlabeled data for better predictions. Signup and view all the answers

What is a key feature of the inferential paradigm of time series analysis?

It approximates non-linear prediction functions in multi-dimensional space. Signup and view all the answers

What potential limitation may arise when working with non-specified model structures?

Potential inaccuracies in predictions due to lack of structure. Signup and view all the answers

What advantage does structured data provide in time series analysis?

It allows for easier integration of expert judgment. Signup and view all the answers

Study Notes

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.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Description

Explore the key concepts and responsibilities of financial risk management within institutions. This quiz covers various aspects such as governance, ICT risks, and the impact of AI and machine learning. Gain insights into the importance of digital operational resilience and cyber risk in today's financial landscape.