IMPACT OF GENERATIVE AI ON ENTERPRISE RISK MANAGEMENT – A STRATEGIC PRIMER FOR BANKING, CAPITAL MARKET, AND INSURANCE FIRMS

While the world is currently laden with escalating political, economic, social, and technological challenges peppered with considerable risks, and acknowledging we are in a golden age of AI with increasing concerns about the potentials dangers of AI and the need for regulations to protect the public from the risks and ensure safety, security, and privacy are responsibly woven into business and technology products and platforms, we posit the impact of Generative AI on Enterprise Risk Management for Financial Services Institutions (FSI) including banking, capital markets, and insurance firms. This paper strategically helps chief risk officers, senior risk executives, and boards better understand the impact of Generative AI in organizations using an Enterprise Risk Management framework, presents potential associated risks, and advocates practical steps to mitigate and manage the risks.

What is Generative AI?

Generative Artificial Intelligence (Generative AI, GenAI) is a powerful subset of Artificial Intelligence that can create original or imaginative content in various forms, such as text, images, videos, or other data. Unlike traditional AI models that make predictions based on supervised, unsupervised, or reinforcement learning, Generative AI models learn the patterns and structures of their input training data from large language models (LLM) that are deep neural networks trained on massive amounts of data. These models excel at natural language understanding and generation, enabling them to perform various tasks (translation, summarization, object recognition, categorization, semantic search, and orchestration) across text, speech, image, voice, and other modalities. Leading LLMs used by organizations include OpenAI, LLAMA, Hugging Face, and PaLM. Examples of consumer applications include Chatbots like ChatGPT, Microsoft Copilot, and Google Gemini, image and art generation models like DALL-E, Stable Diffusion, MidJourney and Microsoft Designer, voice generators like ElevenLabs and VALL-E, and video generators like Sora. It is important to note that Generative AI is often used and deployed in collaboration with traditional AI and ML technologies including Predictive AI.

What is Enterprise Risk Management?

Enterprise Risk Management (ERM) is a strategic approach that organizations use to identify, assess, and manage risks across the entire enterprise. It involves systematically understanding and addressing risks that could impact an organization’s ability to achieve its objectives. ERM encompasses various risk categories, including financial, credit, market, operational, liquidity, technology, legal and regulatory, and systemic risks. Let us explore each component of ERM.

• Financial Risk: The possibility of losing money on investments or business operations due to factors like market fluctuations, credit issues, or mismanagement.
• Credit Risk: The risk that a borrower may default on a debt by failing to make required payments, potentially leading to financial loss for the lender.
• Market Risk: The risk of losses in investments due to market-wide factors such as economic downturns, political instability, or changes in interest rates.
• Liquidity Risk: The risk that an entity may not be able to quickly convert assets into cash without significant loss in value, affecting its ability to meet short-term obligations.
• Operational Risk: The risk of loss resulting from inadequate or failed internal processes, people, systems, or external events impacting the day-to-day operations. Voice, image, and video impersonation facilitated by Generative AI may result in moderate to significant fraud in banking, capital market, and insurance organizations and their customers if appropriate guardrails are not put in place.
• Technical/Technology Risk: The risk associated with failures or disruptions in technology systems, including hardware, software, and cybersecurity threats.
• Legal and Regulatory Risk: Legal Risk refers to the risk of financial or reputational loss due to legal actions, non-compliance with laws and regulations, or contractual disputes. Regulatory risk refers to the potential for a change in laws and regulations to materially impact a security, business, sector, or market. This type of risk can arise from new laws or regulations that increase the costs of operating a business, reduce the attractiveness of an investment, or change the competitive landscape and market dynamics.
• Systemic Risk: The risk of collapse or significant disruption in a financial system or market, potentially leading to broader economic consequences.

These definitions provide a high-level understanding of the various risks that can impact banking, capital market, and insurance companies.

What are the Potential Risks Associated with Generative AI in Banking?

The potential risks associated with Generative AI in various banking functions within the context of deposits, lending, payments, treasury, retail banking, business banking, commercial banking, and trade finance include:

1.       Deposits:

o   Financial Risk:

• Risk: Inaccurate deposit trend predictions due to flawed AI models.
• Impact: Misaligned liquidity management strategies.

o   Operational Risk:

• Risk: Fraud associated with deposit and withdrawal processing. AI poses moderate to significant risks due to voice and video impersonation also known as deepfakes. Warren Buffet, the Oracle of Omaha, recently acknowledged that AI Scamming will be the next growth industry and illuminated some of the associated risks.
• Impact: Customer dissatisfaction, disinformation, privacy, trust erosion, and financial losses.

2.       Lending:

o   Credit Risk:

• Risk: Biased credit scoring models if used.
• Impact: Incorrect risk assessment for loan approvals.

o   Legal and Regulatory Risk:

• Risk: Non-compliance with fair lending regulations.
• Impact: Legal penalties and reputational damage.

3.       Payments:

o   Operational Risk:

• Risk: Fraudulent transactions not detected.
• Impact: Financial losses and customer trust erosion.

4.       Treasury:

o   Liquidity Risk:

• Risk: Inaccurate liquidity forecasts if used.
• Impact: Cash shortages or excess idle funds.

5.       Retail Banking:

o   Technology Risk:

• Risk: Data security breaches.
• Impact: Customer data exposure and reputational damage.

6.       Business Banking:

o   Operational Risk:

• Risk: Possible incorrect risk assessment for business loans.
• Impact: Loan defaults and financial losses.

7.       Commercial Banking:

o   Credit Risk:

• Risk: Mispriced medium to large-scale credit risks.
• Impact: Investment losses and portfolio volatility.

8.       Trade Finance:

o   Operational Risk:

• Risk: Document verification errors. AI poses moderate to significant risks due to voice and video impersonation.
• Impact: Trade transaction delays, disputes, trust erosion, and financial losses.

Generative AI introduces opportunities for efficiency and innovation, but it also brings risks related to model accuracy, bias, transparency, and regulatory compliance. Leading consulting firms have postulated that banks must carefully manage these risks to fully leverage the benefits of Generative AI and further acknowledged that Generative AI can fundamentally change risk management practices at financial institutions.

What are the Potential Risks Associated with Generative AI in Capital Markets?

A leading consultancy recently published a report based on a survey of asset managers who manage over $15 trillion in total, that revealed their views on the near-term impact of generative AI (GenAI). Two out of three asset managers said they are either planning to implement or already scaling up one or more GenAI use cases this year.

Against the preceding and similar backdrops, the potential risks associated with Generative AI in various capital market areas within the context of front office, middle office, back office, and market infrastructure operations for different financial institutions encompass:

1.       Front Office:

o   Risk: Biased Investment Decisions

• Description: Generative AI models may inadvertently glean biases from trained historical data, leading to biased investment recommendations. AI poses significant risks due to voice and video impersonation.
• Impact: Incorrect asset allocation, potential losses, trust erosion, reputational damage.

Mitigation:

• Regular model audits for bias.
• Diverse training data sources.

2.       Middle Office:

o   Risk: Operational Disruptions

• Description: Generative AI models may malfunction, affecting trade support, risk management, and intraday book management.
• Impact: Trade errors, settlement delays, and financial losses.

Mitigation:

• Rigorous testing and validation of AI models before deployment. Testing Generative AI models involves several methods such as evaluation of Large Language Models (LLM) and benchmarking different Generative AI models to ensure they are reliable, unbiased, and perform well across various scenarios.
• Real-time monitoring for anomalies.

3.       Back Office:

o   Risk: Settlement Failures

• Description: Generative AI models may misinterpret settlement instructions, leading to failed trades.
• Impact: Financial losses, regulatory penalties, and operational inefficiencies.

Mitigation:

• Enhanced reconciliation processes
• Manual intervention for critical settlements.

4.       Market Infrastructure Operations:

o   Risk: Market Manipulation

• Description: Generative AI can create realistic-looking market data or orders, potentially leading to market manipulation.
• Impact: Distorted market prices, investor losses, and regulatory investigations.

Mitigation:

• Enhanced surveillance systems.
• Regular audits of trading patterns.

5.       Risk Management:

o   Risk: Model Uncertainty

• Description: Generative AI models lack interpretability, making it challenging to understand their decision-making process.
• Impact: Misaligned risk assessments, unexpected model behavior.

Mitigation:

• Model explainability techniques.
• Scenario analysis for risk assessment.

6.       Legal and Regulatory Risk:

o   Risk: Non-Compliance

• Description: Generative AI models may violate regulatory requirements or legal constraints.
• Impact: Fines, legal disputes, and reputational damage.

Mitigation:

• Legal reviews of AI models.
• Compliance checks.

Generative AI offers immense potential but requires careful management to mitigate risks. Financial institutions must strike a balance between innovation and risk control to fully leverage its benefits.

What are the Potential Risks Associated with Generative AI in Insurance?

 Likewise, the potential risks linked with how Generative AI impacts various risk categories within the insurance industry span:

1.       Policy Writing:

o   Financial Risk:

• Risk: Inaccurate policy pricing due to flawed AI models.
• Impact: Underpricing or overpricing policies, affecting profitability.

o   Legal and Regulatory Risk:

• Risk: Non-compliance with insurance regulations.
• Impact: Regulatory fines and reputational damage.

2.       Underwriting:

o   Credit Risk:

• Risk: Biased underwriting decisions.
• Impact: Incorrect risk assessment for policy issuance.

o   Operational Risk:

• Risk: Loss resulting from inadequate or failed internal systems or processes. AI may lead to medium to significant risks due to voice, image, and video impersonation.
• Impact: Delays in policy issuance and customer dissatisfaction, trust erosion, and financial losses.

o   Market Risk:

• Risk: Errors in interpretation and prediction of market volatility if widely employed without guardrails.
• Impact: Increased losses in investments due to incorrect market information or summary.

3.       Claims:

o   Operational Risk:

• Risk: Incorrect claims processing due to flawed AI models. AI poses significant risks due to voice, image, and video impersonation.
• Impact: Delayed claims settlement and customer dissatisfaction, trust erosion, and financial losses.

o   Legal and Regulatory Risk:

• Risk: Non-compliance with claims handling regulations.
• Impact: Legal penalties and reputational damage.

o   Technical Risk:

• Risk: Complexities and potential errors with implementing AI systems.
• Impact: Increased costs and customer dissatisfaction

4.       Investment:

o   Market Risk:

• Risk: AI-driven investment decisions based on flawed market predictions.
• Impact: Portfolio losses and reduced investment returns.

o   Legal and Regulatory Risk:

• Risk: Non-compliance with investment regulations.
• Impact: Regulatory fines and legal disputes.

5.       Reinsurance:

o   Financial Risk:

• Risk: Inaccurate reinsurance pricing.
• Impact: Underestimation of reinsurance costs, affecting profitability.

o   Operational Risk:

• Risk: People or system errors during reinsurance negotiations if widely employed.
• Impact: Delays in securing reinsurance contracts.

6.       Expense Ratios:

o   Operational Risk:

• Risk: Inefficient expense management due to flawed AI models. AI poses significant risks due to voice, image, and video impersonation.
• Impact: Increased operational costs and reduced profitability, trust erosion, and financial losses.

7.       Loss Ratios:

o   Operational Risk:

• Risk: Incorrect loss assessments. AI poses significant risks due to voice, image, and video impersonation.
• Impact: Underestimation or overestimation of claims liabilities, trust erosion, and financial losses.

Generative AI introduces opportunities for efficiency and innovation in insurance operations, but it also brings risks related to model accuracy, bias, transparency, and regulatory compliance. Striking the right balance between innovation and risk control is crucial for successful adoption.

What are the Practical Steps to Mitigate and Manage the Generative AI Risks?

• We believe the COSO ERM Framework, first published in 2004 by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) and updated in 2017, and endorsed by the AICPA-AIMA and IIA, is the best way to help organizations identify, assess, respond to, and monitor Generative AI risks in a systematic manner. The framework consists of five interrelated components:
• Governance and Culture: Establishing the right tone at the top and fostering a risk-aware organizational culture for Generative AI.
• Strategy and Objective-Setting: Aligning risk appetite for Generative AI with strategic goals and defining clear objectives.
• Performance: Executing strategies while considering risk implications for banking, insurance or capital markets.
• Review and Revision: Regularly assessing risk management processes and adjusting as needed.
• Information, Communication, and Reporting: Ensuring effective communication about risks across the organization.

Financial Service Institutions may use the COSO ERM Framework to evaluate risks of Generative AI related to their operations, projects, and initiatives; integrate Generative AI risk management into decision-making processes; enhance Generative AI governance and accountability; and improve overall performance by addressing Generative AI risks proactively.

How can Banking and Insurance Firms Manage Generative AI Risks?

Using the COSO framework, we delve into some practical approaches to apply preventive, detective, and compensating controls to the risks posed by generative AI in banking and insurance companies.

1.       Financial Risk:

o   Impact:

• Inaccurate risk assessments due to flawed AI models can lead to inadequate capital reserves and mispriced insurance policies if employed.
• Financial losses due to incorrect risk predictions.

o   Controls:

• Preventive: Rigorous model validation and diverse training data.
• Detective: Real-time monitoring for unexpected model behavior.
• Compensating: Manual override when model outputs seem inaccurate

2.       Credit Risk:

o   Impact:

• Biased AI models or training data can perpetuate discriminatory practices (e.g., redlining) or exclude certain customer segments if employed.
• Legal and reputational risks.

o   Controls:

• Preventive: Diverse training data and fairness metrics and use of Responsible AI frameworks.
• Detective: Regular bias audits.
• Compensating: Adjust model outputs to reduce bias.

4.       Market Risk:

o   Impact:

• Generative AI models may introduce volatility in financial markets due to unexpected behavior or sudden shifts.
• Misaligned trading strategies or investment decisions.

o   Controls:

Preventive:

• Rigorous testing and validation of AI models before deployment. Testing Generative AI models involves several methods such as evaluation of Large Language Models (LLM) and benchmarking different Generative AI models to ensure they are reliable, unbiased, and perform well across various scenarios.
• Scenario analysis to assess potential market impacts.

Detective:

• Real-time monitoring for unusual market movements.

Early warning systems for abnormal trading patterns.

Compensating:

• Manual intervention to override AI-driven decisions during extreme market conditions.
• Diversification of investment portfolios.

5.       Operational Risk:

o   Impact:

• Fraud caused by Generative AI can disrupt critical processes (e.g., claims processing, underwriting). AI-generated deepfakes such as videos, audio, photos, and text are difficult to differentiate from authentic media and can lead to fake identities, market manipulations, or fraudulent insurance claims. Voice, image, and video impersonation facilitated by Generative AI may result in moderate to significant fraud in banking, capital market, and insurance organizations and their customers if appropriate guardrails are not put in place.
• Financial losses, reputational damage, and customer dissatisfaction, trust erosion, and trust erosion.

o   Controls:

• Preventive: Robust infrastructure, redundancy, and regulatory guardrails. Mitigation strategies include use of blockchain to secure and tamper-proof record keeping especially in insurance claims, use of robust digital signature mechanisms to verify the authenticity of documents, images, voice, and video, use of AI detection algorithms to detect deepfakes in images, voice, and videos, use of behavioral biometrics that include voice and video analysis, and use of contextual clues that traces the origin of the media and reverse image, voice, and video search.
• Detective: Real-time monitoring for anomalies or system failures. In addition to the preceding preventive controls, use of feature-based approaches that employ face and lip movement analysis, pupil dilation detection, blink pattern analysis, micro-expression detection, use of forensic analysis of noise patterns, compression artifacts, and meta data such as creation date, camera type, and GPS location, deep learning models including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Variational Autoencoders (VAEs) to distinguish between real and fake images, voice, and videos,
• Compensating: Ability to revert to manual processes during system downtime. Understand that deepfake detection is an on-going battle and may require multiple techniques, constant monitoring, and staying updated with advancements.

6.       Technology Risk:

o   Impact:

• System failures, data breaches, or cyberattacks can disrupt operations and compromise sensitive information.
• Reputational damage and legal consequences.

o   Controls:

Preventive:

• Robust cybersecurity measures to protect AI infrastructure.
• Regular vulnerability assessments.
• Implement effective controls in the infrastructure-as-code templates and models.

Detective:

• Intrusion detection systems and real-time monitoring.
• Incident response plans.

Compensating:

• Backup systems and disaster recovery protocols.

7.       Legal Risk:

o   Impact:

• Violation of data privacy laws, intellectual property rights, or regulatory requirements.
• Legal disputes and financial penalties.

o   Controls:

Preventive:

• Legal reviews of AI models and data usage.
• Compliance with GDPR, CCPA, DORA, and other relevant regulations.

Detective:

• Monitoring for legal compliance.
• Regular legal audits.

Compensating:

• Legal agreements and contracts to address liability and accountability.

8.       Liquidity Risk:

o   Impact:

• AI-driven liquidity management decisions can lead to cash shortages or excess idle funds.
• Disruptions in payment processing or investment strategies.

o   Controls:

Preventive:

• Robust liquidity forecasting models if Generative AI is employed.
• Stress testing scenarios if Generative AI is employed.

Detective:

• Real-time monitoring of liquidity positions.
• Early warning indicators.

Compensating:

• Contingency plans for liquidity emergencies.
• Access to emergency funding sources.

9.       Systemic Risk:

o   Impact:

• Widespread failures or vulnerabilities in AI systems.
• Market instability and loss of investor confidence.

o   Controls:

Preventive:

• Collaborative risk assessments across institutions.
• Regulatory oversight and stress testing.

Detective:

• Monitoring interconnected AI systems.
• Early warning indicators for systemic risks.

Compensating:

• Coordinated response plans among financial institutions.
• Contingency measures during crises.

Additional Strategies

A leading consultancy noted additional strategies to standardize Generative AI operations in an enterprise that may reduce risks.

• Establish AI governance, data governance, and talent models that readily deploy cross-functional expertise for collaborate knowledge dissemination including natural language processing, reinforcement processing, prompt engineering, business, product leaders, legal, and regulatory experts.
• Ensure process alignment for building Generative AI to support the rapid and responsible AI end-to-end experimentation, validation, and deployment.
• Define a catalog of production-ready, reusable, and pluggable Generative AI services and solutions or use cases) across a range of business scenarios.
• Establish a secure, Generative AI-ready tech stack that supports hybrid-cloud deployments for unstructured data, vector embedding, ML training, execution, and pre- and postlaunch processing.
• Integration with enterprise-grade foundation models and orchestration across open and proprietary models
• Introduce automation of supporting tools, including machine learning operations (MLOps), data, and processing pipelines, to accelerate the development, release, and maintenance of Generative AI solution.
• Define a road map detailing the timeline for launching various capabilities at scale that aligns with the organization’s broader business strategy with appropriate guard rails and change management.
• Focus on human-in-the-automated-loop reviews to ensure the accuracy of model responses when possible.
• Ensure that everyone across the organization is aware of the risks inherent in gen AI, publishing dos and don’ts and setting risk guardrails.
• Update model identification criteria and model risk policy (in line with regulations such as the EU AI Act) to enable the identification and classification of gen AI models and have an appropriate risk assessment and control framework in place.
• Develop Generative AI risk and compliance experts who can work directly with frontline development teams on new products and customer journeys.
• Revisit existing know-your-customer, anti–money laundering, fraud, and cyber controls to ensure that they are still effective in a Generative AI-enabled world.
• Establish or revise data governance models and frameworks in concert with AI data governance models and frameworks.
• Revise organizational talent and structure including AI champions as necessary for effective and efficient change management adoption for this new tech.

It is worth noting that a comprehensive risk management approach involves preventive, detective, and compensating controls to effectively manage generative AI risks in banking and insurance. Also controls have to be designed and operated effectively.

How Can Capital Market Firms Manage Generative AI Risks?

Next, keeping with our COSO framework, we explore yet more ways to apply preventive, detective, and compensating controls to the risks posed by generative AI for Capital Markets, including Investment Banking, Asset Management Firms, Wealth Management Firms, Private Equity, and Hedge Funds:

1.       Financial Risk:

Impact: Inaccurate risk assessments due to flawed AI models can lead to inadequate capital reserves and mispriced investments. Financial losses due to incorrect risk predictions.

Controls:

• Preventive: Rigorous model validation and diverse training data. Scenario analysis to assess potential impacts.
• Detective: Real-time monitoring for unexpected model behavior. Regular stress testing.
• Compensating: Manual intervention when model outputs seem inaccurate. Diversification of investment portfolios.

2.       Credit Risk:

Credit Risk:

Impact: Biased AI models can perpetuate discriminatory lending practices or exclude certain borrowers. Legal and reputational risks.

Controls:

• Preventive: Diverse training data and fairness metrics. Clear governance around model development.
• Detective: Regular bias audits. Monitoring for discriminatory outcomes.
• Compensating: Adjust model outputs to reduce bias. Transparent communication with affected clients.

3.       Market Risk:

Impact: Generative AI models can introduce volatility due to unexpected behavior or sudden shifts in market conditions if employed. Misaligned trading strategies or investment decisions can occur if Generative AI is used by Hedge Funds to analyze large data sets, predict market movements, assist in asset allocation or stock selection summarize research, or used by Investment Banks to generate investment ideas or craft personalized strategies. Deloitte predicts that the top 14 global investment banks can boost their front-office productivity by as much as 27%–35% by using generative AI.

Controls:

• Preventive: Rigorous testing and validation of AI models including scenario analysis to assess market impacts before deployment. Testing Generative AI models involves several methods such as evaluation of Large Language Models (LLM) and benchmarking different Generative AI models to ensure they are reliable, unbiased, and perform well across various scenarios.
• Detective: Real-time monitoring for unusual market movements. Early warning systems for abnormal trading patterns.
• Compensating: Manual intervention during extreme market conditions. Diversification of investment portfolios.

4.       Liquidity Risk:

Impact: Generative AI models can introduce liquidity risks by making investment decisions based on synthetic data or artificially generated scenarios. Illiquid assets may be mispriced or overvalued due to inaccurate model outputs.

Controls:

• Preventive: Rigorous stress testing of AI models under various liquidity scenarios. Regular validation of liquidity risk models.
• Detective: Real-time monitoring of liquidity positions. Early warning indicators for liquidity stress.
• Contingency plans for liquidity emergencies. Access to emergency funding sources.

5.       Operational Risk:

Impact: AI related fraud or internal process failures can disrupt critical processes (e.g., trade execution, settlement, risk management) if employed without guardrails. Financial losses, reputational damage, and regulatory penalties. Voice, image, and video impersonation facilitated by Generative AI may result in moderate to significant fraud in banking, capital market, and insurance organizations and their customers without appropriate guardrails.

Controls:

• Preventive: Robust infrastructure, redundancy, regulatory guardrails, and rigorous testing and validation of AI systems. Mitigation strategies include use of blockchain to secure and tamper-proof record keeping especially in insurance claims, use of robust digital signature mechanisms to verify the authenticity of documents, images, voice, and video, use of AI detection algorithms to detect deepfakes in images, voice, and videos, use of behavioral biometrics that include voice and video analysis, and use of contextual clues that traces the origin of the media and reverse image, voice, and video search.
• Detective: Real-time monitoring for anomalies or system failures. In addition to the preceding preventive controls, use of feature-based approaches that employ face and lip movement analysis, pupil dilation detection, blink pattern analysis, micro-expression detection, use of forensic analysis of noise patterns, compression artifacts, and meta data such as creation date, camera type, and GPS location, deep learning models including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Variational Autoencoders (VAEs) to distinguish between real and fake images, voice, and videos,
• Compensating: Ability to revert to manual processes during system downtime and business continuity planning. Understand that deepfake detection is an on-going battle and may require multiple techniques, constant monitoring, and staying updated with advancements.

6.       Technical/Technology Risk:

Impact: AI models may malfunction, leading to incorrect investment decisions or operational disruptions. Cybersecurity threats targeting AI systems.

Controls:

• Preventive: Robust cybersecurity measures to protect AI infrastructure. Regular vulnerability assessments.
• Detective: Intrusion detection systems and real-time monitoring. Incident response plans.
• Compensating: Backup systems and disaster recovery protocols.

7.       Legal Risk:

Impact: Legal challenges related to AI model outputs, data privacy, and compliance. Reputational damage and regulatory fines.

Controls:

• Preventive: Legal reviews of AI models and data usage. Compliance with relevant regulations (e.g., GDPR, DORA, EU AI Act).
• Detective: Monitoring for legal compliance. Regular legal audits.
• Compensating: Legal agreements and contracts to address liability

8.       Regulatory Risk:

Impact: Non-compliance with financial regulations due to AI model behavior. Regulatory fines and reputational damage.

Controls:

• Preventive: Clear governance around AI model development. Regular compliance checks.
• Detective: Monitoring for regulatory violations. Regulatory reporting mechanisms.
• Compensating: Remediation plans for non-compliance.

9.       Systemic Risk:

Impact:

• Widespread failures or vulnerabilities in AI systems affecting the entire financial ecosystem.
• Market instability and loss of investor confidence.

Controls:

• Preventive: Collaborative risk assessments across institutions and regulatory oversight and stress testing.
• Detective: Monitoring interconnected AI systems and early warning indicators for systemic risks.
• Compensating: Coordinated response plans among financial institution and contingency measures during crises.

Remember, a comprehensive risk management approach involves preventive, detective, and compensating controls to effectively manage generative AI risks in capital markets. Also controls have to be designed and operated effectively.

Call to Action

As you embrace the new realities and prepare for adoption while navigating the turbulent geopolitical and economic terrain in this new era of AI, don’t moderate your digital or innovation strategy for uncertainty’s sake. You may be asking how Generative AI will impact your company and your customers, and what it means for you, strategies to adopt, and what solutions can be enabled responsibly, swiftly, and securely. Microsoft has established a set of principles for responsible AI, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.  Furthermore, Microsoft proposed a five-point blueprint to govern AI while the NIST released the AI Risk Management Framework (AI RMF 1.0) along with companion NIST AI RMF Playbook. To discuss or learn more about how Microsoft can help you manage and mitigate the impact of Generative AI on your banking, capital market, or insurance operations and navigate with an Enterprise Risk Management lens, please comment or contact me. We shall continue to explore this topic and apply the learnings to federal agencies and other industries in upcoming posts. Stay tuned.