The regulatory landscape for life insurers is shifting significantly as regulators extend their focus to Non-Bank Financial Institutions (NBFIs), including insurers. Regulatory expectations that were initially directed at banks are increasingly being applied to life insurers, emphasizing the need for robust model risk management (MRM), credit risk management, and liquidity risk management.
Life insurers are encouraged to learn from the banking sector’s experience in implementing these regulatory frameworks to better prepare for the upcoming requirements. This article delves into the key areas of concern, lessons learned from banks, and actions insurers can take to align with emerging regulations.
Context: Growing Regulatory Expectations for Insurers
Over the past few years, regulators have expanded various initiatives, initially targeted at banks, to include insurers. These include:
- Recovery and resolution regimes specifically tailored for insurers.
- Stress and scenario testing requirements to assess resilience.
- Solvent exit rules, particularly in the UK, to manage the orderly winding down of insurers.
With Solvency UK (SUK) reform completed, regulators are expected to intensify their focus on model, credit, and liquidity risks for life insurers. These evolving expectations demand proactive preparation and adaptation to ensure compliance and minimize disruptions.
1. Model Risk Management (MRM): Increasing Scrutiny on Models
Regulators are moving closer to applying the MRM principles for banks (SS1/23) to insurers. While the Prudential Regulation Authority (PRA) has yet to extend these principles to the insurance sector, it is widely anticipated that they will apply similar rules in the near future.
The Financial Reporting Council (FRC) has already issued technical guidance on actuarial models, aligning closely with banking principles. Insurers, particularly those with approved internal models, must be ready to adapt to these expectations.
Key Challenges for MRM
1. Broader Model Definition: Insurers will need to include all quantitative methods—beyond financial reporting and regulatory processes—under the definition of “model.”
2. Board Training and Oversight: Boards and senior management must be trained to challenge, validate, and approve models effectively.
3. AI/ML Models: Artificial Intelligence (AI), including Generative AI (GenAI), introduces unique risks that require updated frameworks and robust controls.
Actions for Insurers
- Assess Model Risk Appetite: Review model inventories, ensure completeness, and establish clear risk-tiering frameworks.
- Drive Efficiencies: Identify synergies across modeling projects (e.g., SUK capital models, IFRS17 financial statements, climate risk models) to optimize resources.
- AI/ML Governance: Enhance policies and standards for managing AI/ML models, including ethical considerations and consumer protection.
2. Credit Risk Management: Enhancing Capabilities
Life insurers are increasingly exposed to credit risk, particularly as they invest in illiquid assets to improve yields. With SUK reforms expanding investment flexibility, insurers face new challenges in managing credit risk effectively, particularly in asset classes like property-backed investments, infrastructure, and education loans.
Key Challenges for Credit Risk Management
1. Data Gaps: Insurers may lack sufficient data to assess and monitor credit risks for new asset classes.
2. Internal Credit Ratings: Bespoke assets require robust internal valuation and rating processes, which are subject to increased regulatory scrutiny.
3. Governance and MI (Management Information): Regulators have identified gaps in how credit risk is reported and monitored.
Actions for Insurers
- Revisit Credit Risk Appetite: Align credit risk limits with the firm’s strategy and the latest regulatory guidance, such as SS7/18 and SS3/17.
- Improve Internal Credit Assessments: Establish validation and assessment procedures to ensure accurate and forward-looking evaluations.
- Strengthen MI Governance: Develop consistent metrics for monitoring credit quality and stress-testing for adverse scenarios.
3. Liquidity Risk Management: Reporting and Stress Testing
Liquidity risk has become a focus area for regulators, particularly following market-wide stresses in 2020 and 2022. While insurers face different liquidity challenges compared to banks, their hedging strategies and collateral requirements make them susceptible to liquidity strains.
Key Challenges for Liquidity Risk Management
1. Enhanced Reporting Requirements: Proposed rules will require insurers to provide more granular and frequent liquidity data, with 3,000 new reporting data cells anticipated.
2. System Updates: Insurers will need to upgrade IT systems and reporting controls to meet the new requirements.
3. Stress Testing: Understanding the impact of market changes on collateral calls and liquid asset availability will be critical.
Actions for Insurers
- Perform Gap Analysis: Identify weaknesses in current liquidity risk policies compared to the PRA’s framework.
- Conduct Stress Testing: Use scenario analysis to understand liquidity strains under adverse market conditions.
- Dry-Run Reporting: Test systems and governance processes to ensure readiness for the proposed reporting framework by the end of 2025.
Conclusion: A Proactive Approach to Regulatory Compliance
As the financial ecosystem becomes more interconnected, regulators are applying lessons learned from the banking sector to insurers. Life insurers must prepare for increasing expectations in model risk management, credit risk management, and liquidity risk management by:
- Learning from banks’ experiences to enhance their own practices.
- Developing tailored frameworks that reflect the unique features of the insurance business model.
- Proactively engaging with regulators to address challenges and influence final rules.
By taking these steps, insurers can ensure a smoother transition to the new regulatory environment while maintaining their competitive edge.