Risk Headlines

It’s CRO Performance Evaluation Time - source garp.org

Key Points:

This reveals a fundamental tension in the CRO role between providing decision support and preventing negative outcomes, highlighting the critical importance of proactive communication to shape perceptions of risk management value. There is an emphasis that effective CROs must not only implement robust ERM frameworks and provide risk oversight but also actively manage the narrative around their contributions to organizational success through regular engagement with the CEO and clear articulation of risk management benefits beyond simply avoiding negative events.

The CEO's critique of a CRO reveals potential misalignment in expectations about the CRO's role and responsibilities in risk prevention versus risk identification.

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BNY, America’s Oldest Bank, Signs Multiyear Deal With OpenAI - source wsj.com

Key Points:

The BNY-OpenAI partnership highlights a maturing approach to AI adoption in banking characterized by strategic platform enhancement rather than wholesale transformation. This reveals an evolution from experimental AI initiatives to integrated platforms with practical business applications, where existing infrastructure becomes more valuable through selective augmentation with cutting-edge capabilities. This approach recognizes that successful AI implementation requires both technical sophistication and operational insights about which tools serve specific business needs.

BNY will selectively deploy ChatGPT Enterprise alongside their custom Eliza platform, focusing on developers, product owners, and business leaders.

A.I. Risk / Technology Risk

AI Governance Mega-Map: Leadership, Risk Management & Regulatory Operations - source ethos-ai.org 

Key Points:

The emerging AI governance landscape requires executive-level accountability and comprehensive risk management across the entire AI lifecycle. Your bank must establish clear roles, align AI objectives with business strategy, and implement robust monitoring systems that satisfy regulatory requirements. This isn't just compliance – it's about building trust in your AI applications while mitigating potentially significant financial and reputational risks.

This is a must-read as this research distills over a thousand pages from 6 frameworks, standards, and regulations into a practical set of 44 master controls across 12 essential domains. This is absolutely essential for anyone planning a sustainable controls framework for AI governance - one that avoids drowning in a sea of overlapping requirements.

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Cyber Risk Bank CRO’s Biggest Concern: EY Survey- source fst.net 

Key Points:

Cyber risk remains the banking industry's predominant concern (73% of CROs), while geopolitical tensions have dramatically escalated as a priority risk factor. Technology modernization presents a double-edged sword with AI adoption creating significant value opportunities but accompanied by substantial risk concerns, particularly around data quality and talent gaps in cybersecurity that affect 83% of major banks.

• Technology risk has evolved beyond simple cyber threats to encompass complex interconnections between data integrity, AI governance, and legacy system vulnerabilities

• Talent acquisition in specialized domains represents a strategic risk factor rather than merely an operational challenge

• Geopolitical instability has emerged as a major risk accelerator affecting multiple domains

• The simultaneous digitization of banking services and risk management functions creates both opportunities and vulnerabilities

Regulatory News - Fines, Losses, & Rules

Financial Services Agencies Focus on Credit Risk Management - source claconnect.com

Key Points:

Federal banking regulators have designated credit risk as their principal supervisory concern for 2025, responding to unprecedented delinquency levels in auto loans, historic-high credit card charge-offs, and deteriorating commercial real estate performance particularly in urban office and multifamily sectors. Firm’s attention should be directed toward reviewing their institution's Allowance for Credit Losses methodology, stress testing frameworks, and credit concentration management practices as these will be primary examination focus areas.

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Big Data - source visualcapitalist.com

Risk Data to Geek Out On

Define Market Risk - For Financial Risk Management - riskonq .com

This week we will continue to focus on a key risk program in financial risk management, moving to Market Riks, last week we covered Credit Risk. Over the coming weeks, we will define these concepts to enhance understanding and appreciation of the vast risk management ecosystem existing in the financial sector.

Market risk management involves identifying, measuring, and mitigating losses arising from fluctuations in financial variables such as equity prices, interest rates, foreign exchange rates, and commodity prices. It encompasses liquidity risk during market stress and basis risk from correlated instrument mismatches.

1.2 Types of Market Risk

• Equity Risk: Exposure to stock price volatility (e.g., S&P 500 declines).

• Interest Rate Risk: Losses from rate changes impacting bond valuations.

• Commodity Risk: Price volatility in oil, gold, or agricultural products.

• Currency Risk: FX fluctuations affecting multinational portfolios.

• Basis Risk: Mispricing between correlated derivatives (e.g., futures vs. spot).

1.3 Interconnection with Other Risks

• Credit Risk: Counterparty defaults exacerbate market losses (e.g., Archegos margin collapse).

• Operational Risk: Inadequate systems or human errors amplify market exposures.

• Liquidity Risk: Inability to exit positions during crises compounds losses.

2. Financial Institution Context Banks: 

2.1 Adaptation Across Institutions

• Investment Banks: Manage complex derivatives and structured products, requiring real-time VaR monitoring.

• Asset Managers: Prioritize tracking error and benchmark-relative risk.

• Hedge Funds: Leverage dynamic hedging and high-frequency strategies.

2.2 Regulatory Frameworks

• Basel III/FRTB: Mandate stricter capital requirements, liquidity coverage ratios, and stress testing.

• MiFID II: Enhances transparency in derivatives trading and algorithmic controls.

2.3 Macroeconomic Drivers

• Inflation shocks erode fixed-income returns, necessitating duration hedging.

• Geopolitical disruptions (e.g., supply chain crises) elevate commodity volatility.

Regulatory Landscape

• Basel III/FRTB: Mandate stricter capital requirements, liquidity coverage ratios, and stress testing.

• MiFID II: Enhances transparency in derivatives trading and algorithmic controls.

Financial Products and Macroeconomic Impacts

• Inflation shocks erode fixed-income returns, necessitating duration hedging.

• Geopolitical disruptions (e.g., supply chain crises) elevate commodity volatility.

3. Market Risk Management Strategies

3.1 Quantitative Models

• Value at Risk (VaR): Estimates potential losses at 95%/99% confidence levels but underestimates tail risks.

• Expected Shortfall (ES): Addresses VaR limitations by averaging losses beyond the VaR threshold.

3.2 Stress Testing

• Scenario analysis simulates 2008-style liquidity crunches or COVID-19 sell-offs812. For example, a 200 bps rate hike could trigger 15% portfolio losses.

3.3 Dynamic Hedging

• Options and futures offset equity/currency exposures but incur basis risk.

• Algorithmic triggers automate hedging during volatility spikes.

3.4 Liquidity Management

• Stressed liquidity horizons (e.g., 30-day sell-off) inform capital buffers.

3.5 Technology Integration

• AI/ML: Predicts volatility clusters and correlation breakdowns27.

• Cloud Platforms: Aggregate real-time data across trading desks.

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Thank you for reading,

Naeem

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