Podcast
Questions and Answers
What characteristic of operational risk scenarios influences the choice of distribution for modeling?
What characteristic of operational risk scenarios influences the choice of distribution for modeling?
- Normal distribution properties
- Heavy-tailed nature (correct)
- Uniformly distributed risks
- Predictable outcomes
Which property of the Lognormal distribution makes it particularly suitable for operational risk modeling?
Which property of the Lognormal distribution makes it particularly suitable for operational risk modeling?
- It requires complex calibration methods.
- It is a sub-exponential distribution. (correct)
- It has infinite parameters for flexibility.
- It is valid only in non-financial sectors.
What is a primary reason for using the Lognormal distribution in banking regulation compliance?
What is a primary reason for using the Lognormal distribution in banking regulation compliance?
- It generates standard deviations.
- It limits parameter variability.
- It requires many parameters to understand the risks.
- It complies with sub-exponential distribution requirements. (correct)
What is the maximum number of parameters required for calibrating the Lognormal distribution?
What is the maximum number of parameters required for calibrating the Lognormal distribution?
Which of the following describes a limitation of using other distributions compared to the Lognormal for operational risk modeling?
Which of the following describes a limitation of using other distributions compared to the Lognormal for operational risk modeling?
What does the Advanced Measurement Approach (AMA) primarily focus on for quantifying Operational Risk?
What does the Advanced Measurement Approach (AMA) primarily focus on for quantifying Operational Risk?
In the context of operational loss, what does $X_i$ represent in the aggregate loss formula?
In the context of operational loss, what does $X_i$ represent in the aggregate loss formula?
What is assumed about the claims when applying the Advanced Measurement Approach?
What is assumed about the claims when applying the Advanced Measurement Approach?
How is risk-capital calculated under Solvency II principles?
How is risk-capital calculated under Solvency II principles?
What statistical method is commonly used to combine frequency and severity in operational risk analysis?
What statistical method is commonly used to combine frequency and severity in operational risk analysis?
What is typically the time frame for quantifying the distribution of frequency in loss events?
What is typically the time frame for quantifying the distribution of frequency in loss events?
What does the formula for aggregate loss represent?
What does the formula for aggregate loss represent?
What characteristic of claims is considered when analyzing operational risk within risk management frameworks?
What characteristic of claims is considered when analyzing operational risk within risk management frameworks?
What does the variable $p_{W C}$ represent in the context provided?
What does the variable $p_{W C}$ represent in the context provided?
How is the shape parameter $σ$ determined according to the model?
How is the shape parameter $σ$ determined according to the model?
What does the equation $µ=ln(y_0)-σϕ−1(p_{W C})$ calculate?
What does the equation $µ=ln(y_0)-σϕ−1(p_{W C})$ calculate?
What does Monte Carlo simulation help analyze in cyber risk scenarios?
What does Monte Carlo simulation help analyze in cyber risk scenarios?
In the worst case risk assessment, how often is the loss expected to occur?
In the worst case risk assessment, how often is the loss expected to occur?
What is implied by 'frequency' in the context of risk assessment models?
What is implied by 'frequency' in the context of risk assessment models?
What aspect of the model is crucial for understanding cyber risks effectively?
What aspect of the model is crucial for understanding cyber risks effectively?
Which of the following best describes the goal of the cyber risk model architecture presented?
Which of the following best describes the goal of the cyber risk model architecture presented?
Flashcards
Operational Risk Quantification
Operational Risk Quantification
Using models, like the Advanced Measurement Approach (AMA), to estimate operational risk losses by combining frequency and severity of events.
Frequency in Operational Risk
Frequency in Operational Risk
The number of loss events in a specific period, often a year. It is a key factor in estimating operational risk.
Severity in Operational Risk
Severity in Operational Risk
The magnitude, or size, of a single loss event in operational risk.
Advanced Measurement Approach (AMA)
Advanced Measurement Approach (AMA)
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Aggregate Loss
Aggregate Loss
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Monte Carlo approach
Monte Carlo approach
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Value-at-Risk (VaR)
Value-at-Risk (VaR)
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Risk Capital
Risk Capital
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Why is Lognormal used for Operational Risk?
Why is Lognormal used for Operational Risk?
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What are the three points for Lognormal calibration?
What are the three points for Lognormal calibration?
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What are typical impact, 99.5% percentile, and 99.9% percentile?
What are typical impact, 99.5% percentile, and 99.9% percentile?
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How do these three points help calibrate the Lognormal distribution?
How do these three points help calibrate the Lognormal distribution?
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What are the parameters of the Lognormal distribution?
What are the parameters of the Lognormal distribution?
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Worst Case Event
Worst Case Event
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Percentile of Worst Case Event (pWC)
Percentile of Worst Case Event (pWC)
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Shape Parameter (σ)
Shape Parameter (σ)
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Amount of Worst Case Event (y0)
Amount of Worst Case Event (y0)
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Cumulative Function of a Standard Gaussian (ϕ())
Cumulative Function of a Standard Gaussian (ϕ())
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Cyber Risk Quantification
Cyber Risk Quantification
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Monte Carlo Simulation
Monte Carlo Simulation
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Expected and Unexpected Loss
Expected and Unexpected Loss
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Study Notes
Graduate Management School
- POLIMI Graduate Management School
- International master in financial risk management (İMİFRİM)
Digital Risk Management in the Insurance Sector
- Digital risk management in the insurance sector is presented at the POLIMI Graduate Management School
- Valeria Scacco, Partner, Actuarial Services- PwC, presented the topic
- The presentation covered the role of risk management, governance, non-financial risk, and the Insurer's stance on non-financial risk.
- Including digital/cyber risk
Agenda
- Digital risk management in the insurance sector
- Governance in the regulatory context - Pillar I: general principles and standard formula; Pillar II: process overview, ORSA process
- Non-financial risks (approaches and the role of risk management): Operational risk and digital risk methodologies for measurement
- Scenario-based approach (SBA) for operational risk (digital)
- Procedure for identification, selection, analysis, and aggregation
- The insurer's stance on non-financial risks including digital risk: cyber risk
- Definition
- Impact
- Cyber risk scenarios; modelling, and insurance coverage -Interactive demo
Speaker
- Valerio Scacco (Partner-Actuarial Services, PwC)
- Actuary, PhD
- Multiple years of experience in risk management in the insurance sector. -Modeling of solvency capital requirements in the Solvency II framework
- Participation in implementation and validation projects in operational risk.
Solvency I and II
- Solvency I: had limitations in relation to risk management, capital requirements based on technical factors
- Solvency II: Defines a new regime; emphasis on risk management and internal controls, places greater emphasis on the quality of risk management and soundness of internal controls, based on principles instead of rules
- Defines capital requirements as a result of forward-looking valuation. Takes into account financial, technical, counterparty and operational risks -Introduces a joint risk management approach for assets, liabilities. Consists of three pillars:
- Pillar I (SCR)
- Pillar 2 (ORSA)
- Pillar 3 (QRTs). Risk Management is part of Pillar 2
Regulatory Context
- First-level measures: Solvency II Directive, Omnibus II Directive
- Second-level measures: Delegated Acts, amendments to delegated acts, first sets of ITS
- Third-level measures: First and second sets of guidelines -IVASS regulations, Review of regulations, and letters to the market
SII - Pillar I: General Principles
- Assets and Liabilities are valued at fair market values
- Technical reserves correspond to the current amount insurance companies would have to pay if they immediately transferred insurance obligations
- Methods for valuing include Mark to Market and Mark to Model approaches
SII - Pillar I: Standard Formula
- Methods for calculating SCR including scenario testing approach (SA) and Factor-based approach (FBA)
- Modules and submodules (Market, Health, Default, Life, Non-life, Intangible) for Risk assessment including loss distribution and analysis
- Modules include: Market Risk, Credit Risk, Underwriting Risk (Non-Life, Life, Health), and Operational Risk.
SII - Pillar II: ORSA Process and Policy
- ORSA policy: Policies for assessment of current and future risks and solvency, evidence for risk assessments, internal reports, and ORSA reporting to the Supervisory Authority
- Risk measurement processes and procedures used for ORSA
- Risk profile of the company, approved risk tolerance levels, and the total solvency requirement
- Frequency of qualitative analyses( stress tests, sensitivity analyses, reverse stress) and data quality standards
- Minimum contents for ORSA Report including risk profile, explanation of current and forward-looking assessments, and linking assessment results to other capital management strategies
SII - Pillar II: ORSA Policy Structure
- Introduction, Risk Governance, Risk Management System, Current & Forward-looking assessment, Stress Test and Scenario Analysis, ORSA and Capital Allocation, ORSA Report
- Processes and procedures for ORSA, link between risk profile and approved risk tolerances, qualitative and quantitative analyses, and data quality
SII - Pillar II: ORSA Report
- Company risk profile and strategy
- Explanation of current and forward-looking assessments including time horizon of 3+ years, data type and quality, and assessment results
- Detailed illustration of conclusions drawn from assessment and related actions; including connections to capital management and business planning
- Additional elements (Contingency plans, management actions), linking to Standard Formula (Adequacy)
Pillar I vs Pillar II
- Methods for calculating capital requirements; Standard Formula (Pillar I) for individual risk modules, ORSA (Pillar II) for assessing all risks, quantitative/qualitative risk assessment, probability levels, considering the firm's risk profile
SII - Pillar II: Risks Not Included in Pillar I
- Liquidity risk
- Group risk
- Reputational risk
- Climate risk
Non-financial risks - Operational Risk (Digital Risk)
- Operational risk
- Arises from failures of internal processes, human resources, systems, or from external events
- Digital risks
- Often associated with operational risks
- Methods for quantification, calibration, and modeling operational risk, including the Standard Formula Approach -Capital Requirement for operational risks is equal to min (0.3BSCR; Op) + 0.25 Expul
Non-financial risks - Digital and Operational Risks
- Methods for modeling and calibrating severity and frequency distributions, including loss data approach (LDA), scenario-based approach (SBA), use
- of lognormal distributions
Model Output
- Output tables and graphs for each scenario
- Visualization with risk intelligence tools
- Loss probability and aggregate exposure distributions that take into consideration risk dependencies
- Allocate overall risk exposure using model blocks as risk source intelligence tools
Cyber Risk
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Definition: Risk arising from electronic data use, transmission, technology tools, and cybersecurity incidents
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Cyber risk is considered as external fraud within risk-types
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Cyber risks considerations and exposures -Focus on IoT, cloud, and IT
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Coverage for cyber risks in different sectors (Finance, Public Sector, Wholesale & Retail, etc.)
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Key findings of a survey of PwC
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Qualitative & Quantitative factors considered in pricing cyber-covers
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Silent/non-affirmative risks
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Case study / interactive demo
Thank You
- Contact Information
- Legal Notes
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Description
This quiz explores the characteristics of operational risk scenarios and the application of the Lognormal distribution in modeling. Key topics include the use of the Advanced Measurement Approach (AMA) and principles under Solvency II for quantifying operational risk. Test your understanding of these concepts in the context of banking regulation compliance.
