Risk Headlines

Forecasting 2025: Risk Trends & Predictions - source garp.org

Key Points:

The risk landscape for 2024-25 is characterized by deeply interconnected challenges across cybersecurity, geopolitical, technological, and environmental domains, requiring a more sophisticated and integrated approach to risk management.

Financial institutions face heightened exposure from the convergence of traditional risks with emerging threats like AI-enabled cyber attacks and supply chain disruptions driven by geopolitical events. The combination of global debt problems, deglobalization trends, and increasing regulatory scrutiny creates a particularly complex environment requiring enhanced risk management capabilities and strategies.

The fundamental challenge facing financial institutions is the increasing interconnectivity of risks across multiple domains, where traditional siloed risk management approaches are no longer sufficient to address threats that cascade across businesses, technology, operations, and geopolitical boundaries.

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Barclays Faces Probe From U.K. Regulator Over Money-Laundering Controls - source wsj.com

Key Points:

A significant FCA investigation into Barclays' anti-money laundering controls across both retail and corporate divisions signals heightened regulatory scrutiny of historical high-risk customer oversight.

• Major AML Investigation: FCA probe into Barclays' anti-money laundering controls

• Scope: Covers both retail and corporate banking divisions

• Historical Focus: Investigation centers on oversight of high-risk customers

• Previous Issues: Recent $50M fine over 2008 crisis-related disclosure failures

• Timing: Current investigation disclosed in annual report

• Unknown Financial Impact: No provisions or estimates provided

A.I. Risk / Technology Risk

Goldman Sachs, JP Morgan ‘Lead on Documented AI Use Cases’ - source fstech.co.uk

Key Points:

The banking industry is experiencing a significant shift in AI strategy, with leading institutions like Goldman Sachs and JP Morgan driving documented use cases while demonstrating a pivot from efficiency-focused implementations to value-generating applications in risk reduction and revenue enhancement. The pressure to show tangible AI results is intensifying, with successful banks focusing on retail banking, IT security, and investment banking applications. This evolution requires a clear strategy to justify significant AI investments through measurable outcomes and competitive positioning.

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Putting AI Agents Through Their Paces - Will They Work in Banks -source bis.org

Key Points:

The BIS paper reveals critical limitations in current AI systems' ability to recognize and correct their own mistakes, a crucial capability for financial sector applications. While AI models excel at complex tasks, they struggle with basic cognitive functions and lack reliable self-correction mechanisms - raising important considerations for their deployment in banking operations. The paper proposes two implementation paths: AI as a copilot (augmenting human capabilities) or as an autonomous agent (replacing human functions), with the former being more immediately viable for financial institutions.

• The gap between AI's technical capabilities and practical reliability presents a significant operational risk

• Current AI evaluation methods may be inadequate for assessing real-world deployment readiness

• Banks need to develop new risk frameworks that account for AI's limitations in self-awareness and error correction

Regulatory News - Fines, Losses, & Rules

Wells Fargo Clears 10th Consent Order; Four Remain - source bankingdive.com

Key Points:

Wells Fargo's resolution of multiple regulatory consent orders, including the recent OCC termination, signals a change in their risk management and compliance framework, though four orders remain outstanding. The potential lifting of the $1.95T asset cap could represent a major strategic opportunity for growth and competitive positioning.

Wells Fargo case illustrates how systematic compliance failures require years of comprehensive transformation across the business, culture, leadership, and risk management systems, with regulatory relief coming gradually as improvements are validated. There is substantial capital allocated to resolve these issues which has not been reported.

• Multi-year transformation requires sustained commitment

• Regulatory relief comes incrementally with validated progress

• Cultural change is as critical as technical compliance

• Asset growth restrictions have a strategic competitive impact

Risk Data to Geek Out On

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

This week we will continue to take the key risk programs that exist in financial risk management, starting with Credit Risk. Over the coming weeks, we will define these concepts to enhance understanding and appreciation of the vast risk management ecosystem. This week we are covering Credit Risk and its core components.

Credit risk management (CRM) is the cornerstone of financial stability for banks, credit unions, and investment firms. It involves identifying, measuring, monitoring, and mitigating risks arising from borrowers’ potential defaults or failures to meet obligations. The goal of CRM is to maximize a bank's risk-adjusted rate of return by maintaining credit risk exposure within acceptable parameters. Banks need to manage the credit risk inherent in the entire portfolio as well as the risk in individual credits or transactions.

Effective NFRM is essential not only for protecting an institution's assets and reputation but also for ensuring regulatory compliance and maintaining stakeholder trust in an era of heightened scrutiny and regulatory change

1.1 Core Principles

• Risk Identification: Classifying exposures by borrower type, product, and geography.

• Risk Measurement: Using internal ratings and external benchmarks to quantify default probabilities.

• Risk Mitigation: Implementing collateral requirements, covenants, and hedging strategies.

• Continuous Monitoring: Tracking portfolio performance through key risk indicators (KRIs) and early warning systems.

1.2 Types of Credit Risk

• Default Risk: Failure of a borrower to meet contractual obligations, prevalent in unsecured lending.

• Counterparty Risk: Exposure to derivatives or securities lending transactions, where the counterparty defaults.

• Concentration Risk: Overexposure to a single borrower, sector, or geographic region, amplifying systemic vulnerabilities.

1.3 Interconnection with Other Risks

• Market Risk: Fluctuations in interest rates or asset prices affecting collateral values and borrower repayment capacity.

• Operational Risk: Inadequate internal processes leading to flawed credit assessments (e.g., data entry errors).

• Liquidity Risk: Inability to offset credit losses due to illiquid assets, exacerbating capital shortfalls. 

Financial Institution Context Banks: 

Banks as primary credit extenders, have the most direct exposure to credit risk through their:

• Corporate, small business, and retail lending

• Real estate and project financing

• Interbank lending and trade financing activities

• Off-balance sheet items like loan commitments and letters of credit

For investment firms, broker-dealers and asset managers, credit risk arises from:

• Margin lending to clients

• Securities financing transactions (repos, securities lending)

• OTC derivatives contracts

• Counterparty settlement risk on trades

• Debt instruments in investment portfolios

Insurance companies also face credit risk from:

• Bond and loan investments that back up insurance policies

• Reinsurance recoverables

• Receivables from policyholders

Regulatory Landscape

• Basel Accords:

  • Basel I (1988): Introduced minimum capital requirements (8% of RWAs).

  • Basel II (2004): Added Pillar 2 (supervisory review) and Pillar 3 (market discipline), emphasizing internal risk models.

  • Basel III (2017): Enhanced liquidity coverage ratios (LCR) and introduced leverage ratio buffers for systemic banks.

  • Dodd-Frank Act: Mandates stress testing for U.S. banks with assets >$250B

Financial Products and Macroeconomic Impacts

• Loans: Secured loans reduce loss given default (LGD) through collateral (e.g., mortgages).

• Derivatives: Central clearing parties (CCPs) mitigate counterparty risk in over-the-counter (OTC) trades.

• Macroeconomic Factors: Recessions increase default rates (e.g., 2008 crisis saw U.S. charge-offs peak at 3.5%).

Credit Risk Management Strategies

3.1 Credit Assessment Frameworks

• Internal Scoring Models: Machine learning algorithms analyze 100+ variables (e.g., cash flow patterns, social media activity) for SMEs, reducing default prediction errors by 20%.

• External Ratings: Moody’s and S&P ratings correlate with 5-year cumulative default probabilities (e.g., BBB: 2.5%, CCC: 28%).

3.2 Portfolio Optimization

• Diversification: Spreading exposure across industries reduces concentration risk (e.g., limiting sector exposure to 15% of total loans).

• Stress Testing: Simulating 2008-level unemployment (10%) reveals capital adequacy gaps, prompting preemptive provisioning.

3.3 Technology Integration

• AI/ML Applications:

  • ZestFinance: Reduced default rates by 20% using alternative data (e.g., rental payments).

  • Natural Language Processing (NLP): Scans news and earnings calls for early warnings (e.g., bankruptcy filings).

• Blockchain: Smart contracts automate collateral liquidation, cutting settlement times from days to hours

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Thank you for reading,

Naeem

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