Risk Headlines

They Crashed the Economy in 2008. Now They’re Back and Bigger Than Ever - source wsj.com

Key Points:

The renaissance of structured finance reveals a classic market evolution pattern where regulation intended to reduce risk has primarily redistributed it, with traditional banks now operating primarily as intermediaries while alternative asset managers capture the origination economics. While most risk officers focus narrowly on credit quality within specific asset classes, the greater strategic risk lies in the rapid concentration of capital flowing into novel sectors like data centers, creating potential correlation risks that transcend individual deal underwriting. The most forward-thinking institutions will recognize that the true opportunity doesn't lie in simply chasing yield across increasingly exotic asset classes, but in developing proprietary knowledge frameworks that identify emerging sectors early while maintaining disciplined underwriting and strategic optionality.

Some market participants note potential overheating, with one investor indicating February 2025 showed the most significant short-position gains since January 2020 (pre-COVID crash).

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Citigroup’s $81 Trillion Mistake Is A Warning Sign 'that Operational Risk Is Still A Ticking Time Bomb,’ Expert Says - source fortune.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

Big Banking Looks Up to GenAI Launching its Own LLMs - source thetechpanda.com

Key Points:

The banking industry is experiencing a pivotal shift toward AI-centric business models where major financial institutions are developing proprietary LLMs and GenAI applications not merely as experimental technologies but as strategic imperatives that simultaneously enhance customer experiences and reduce operational costs. This dual benefit of personalization and efficiency creates a compelling business case for investment, yet introduces complex challenges around data privacy, regulatory compliance, and technological dependency that institutions must navigate carefully to maintain competitive advantage.

JP Morgan and Goldman Sachs appear focused on internal operational transformation, while Wells Fargo emphasizes customer-facing applications. Citigroup's regulatory focus highlights compliance as a primary use case. The diversity of approaches suggests banks are developing AI strategies aligned with their specific business models rather than following a uniform approach.

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75% of Banks to Invest in Risk Technology Transformation Amid Unyielding Headwinds - source yahoo.com 

Key Points:

The FT Longitude and SAS survey reveals a banking industry at an inflection point in its approach to risk management. The dramatic increase in planned technology investments – with 75% of institutions boosting risk infrastructure spending compared to 51% in 2021 – represents more than incremental improvement but suggests a fundamental reassessment of risk management's strategic importance.

This shift is occurring against a backdrop of extraordinary challenges: eight bank failures since 2023 driven by interest rate and liquidity risks, ongoing geopolitical tensions, persistent inflation pressures, and increasing regulatory complexity. The collective effect has been to expose the limitations of traditional risk management approaches that treat various risk domains as separate concerns.

Carlos Diaz Alvarez's observation about the need for more granular and integrated decision-making across liquidity, capital, and credit risk points to a key evolution in thinking. Banks are recognizing that in today's interconnected environment, risk events rarely confine themselves to neat categories, requiring more sophisticated systems capable of providing holistic views. This should also be taken a step further, all risk domains must be interconnected, including operational risk and the non-financial risks that are exploding and normally receive less of a priority, witnessed by several of the largest industry fines in the previous years.

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The Rise of Artificial Intelligence at

JPMorgan - source wsj.com

Key Points:

JPMorgan's approach reveals an often-overlooked opportunity in financial AI strategy: the competitive advantage lies not in owning proprietary models but in the unique application of AI to institution-specific knowledge, workflows and customer data. While many institutions focus excessive resources on model development, JPMorgan's strategy of leveraging external models while focusing internally on integration and application represents a more capital-efficient path to value creation. The multi-tiered control framework that matches model transparency with use case sensitivity offers a sophisticated template for managing AI risk that many institutions miss with their one-size-fits-all governance approaches. The greatest overlooked opportunity, however, may be their business-led rather than technology-led governance structure, which naturally drives focus toward applications with demonstrable ROI rather than technologically interesting but commercially questionable use cases.

Regulatory News - Fines, Losses, & Rules

US Banks’ Unrealized Losses Explode by $118,400,000 in Three Months As FDIC Declares 66 Banks on ‘Problem List’ - source dailyhodl.com

Key Points:

The surge in unrealized losses to $482.4 billion represents a critical blind spot in how many institutions are evaluating their risk profiles during this interest rate cycle. While most banks are focusing on the positive narrative of improved profitability and stable regulatory metrics, they're underestimating how quickly paper losses can transform into realized losses if depositor behavior shifts. The most concerning aspect isn't the absolute value of the unrealized losses but their dramatic acceleration – a $118.4 billion increase in a single quarter suggests potentially non-linear exposure to additional rate movements.

The concentration risk is likely substantial but invisible in aggregate statistics, with smaller and mid-sized institutions potentially carrying disproportionate exposure relative to their capital bases. Institutions that proactively restructure their securities portfolios and develop transparent communication strategies about their interest rate risk management will not only protect themselves but could capitalize on acquisition opportunities as weaker competitors face increasing pressure.

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SIFMA Research Quarterly: US Financial Institutions - Financial & Regulatory Data - source sifma.org

Key Points:

The SIFMA Research Quarterly provides a comprehensive snapshot of the U.S. banking system's financial health, revealing several significant trends worthy of deeper examination. CCAR financial institutions are demonstrating extraordinary resilience and profitability with net income to common shareholders surging 174.6% year-over-year to $50.1 billion while maintaining strong capital positions with CET1 ratios at 12.6% – more than 2 percentage points above regulatory requirements. System-wide improvements in operational efficiency have reduced operating expenses by 10.8% year-over-year despite moderate loan growth of 1.5%, positioning the sector with substantial buffers to navigate potential market volatility while continuing to drive shareholder returns.

Risk Data to Geek Out On

Define Liquidity 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 Liquidity Risk, last week we covered Market 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.

Liquidity risk refers to a financial institution's inability to meet its financial obligations as they come due without incurring unacceptable losses15. It encompasses two primary types:

• Funding liquidity risk: The risk that an institution cannot efficiently meet expected and unexpected current and future cash flow needs without affecting daily operations or financial condition.

• Market liquidity risk: The risk that an institution cannot readily sell marketable assets at market price due to market disruption.

Interconnection with Other Risks

• Credit risk: Deteriorating credit conditions can trigger liquidity stress as counterparties withdraw funding9.

• Market risk: Market volatility can lead to increased margin calls on derivatives, creating liquidity pressure.

• Operational risk: Operational failures can disrupt cash flows and liquidity management processes.

Financial Institution Context Banks: 

Adaptation Across Institutions

Different financial institutions adapt liquidity risk management based on their business models:

• Banks: Focus on managing the mismatch between deposits (typically short-term) and loans (typically longer-term).

• Credit unions: Required to establish written, board-approved liquidity policies reviewed annually and updated when the credit union experiences changes requiring review.

• Investment funds: Must manage liquidity to meet potential margin calls on derivatives and investor redemptions, especially during market stress.

Governance and Framework Development

Effective liquidity risk management begins with robust governance:

1. Clear articulation of liquidity risk tolerance appropriate for the institution's business strategy and role in the financial system.

2. Senior management responsibility for developing strategies, policies, and practices aligned with the established risk tolerance.

3. Integration with enterprise risk management to ensure a holistic approach to risk.

Measurement and Monitoring Approaches

Financial institutions should establish liquidity risk metrics based on a coherent and robust methodology commensurate with their risk profile, size, and complexity:

1. Cash flow forecasting: Develop models that account for short, medium, and long-term trends and cycles affecting liquidity.

2. Stress testing: Regularly evaluate how the institution would perform under different liquidity stress scenarios.

3. Early warning indicators: Implement systems to identify potential liquidity issues before they become critical.

Strategic Liquidity Management

Institutions should implement multiple strategies to ensure adequate liquidity:

1. Diversifying funding sources: Establish multiple credit lines with different banks and explore alternative financing options to avoid dependence on a single lender.

2. Optimizing working capital: Manage inventory efficiently, negotiate better payment terms with suppliers, and implement automated systems for accounts payable and receivable.

3. Maintaining liquidity reserves: Hold a buffer of liquid assets that can be quickly converted to cash in case of unexpected needs.

4. Implementing contingency funding plans: Develop detailed response strategies for various scenarios to ensure quick action when liquidity challenges arise.

Technology and Analytics Integration

Modern liquidity risk management leverages advanced technologies:

1. Real-time liquidity monitoring: Implement systems that provide up-to-date visibility into cash positions.

2. AI-driven analytics: Use artificial intelligence to anticipate potential cash shortfalls and improve forecasting accuracy.

3. Integrated data management: Create a centralized view of firmwide interest rate and liquidity risks by integrating market information, portfolio updates, and capital returns.

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Thank you for reading,

Naeem

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