Category: Regulation

ERISA: Reflecting On 50 Years And Looking To The Future

Mary Pat Campbell

Link: https://www.forbes.com/sites/dandoonan/2024/09/02/erisa-reflecting-on-50-years-and-looking-to-the-future/


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Excerpt:

This year is a major milestone for the retirement industry as the Employee Retirement Income Security Act of 1974 (ERISA) reaches its 50th anniversary today.

ERISA is the federal law that for the first time set important standards for most voluntarily established retirement and health plans in the private sector to protect individual participants and beneficiaries. Key provisions of ERISA require plans to provide participants with essential plan information; set minimum standards for participation, vesting, benefit accrual, and funding; establish fiduciary responsibilities for those who manage plan assets; and guarantee payment of benefits through the Pension Benefit Guaranty Corporation (PBGC) for terminated defined benefit pension plans.

There are numerous ERISA successes to celebrate, but there also are challenges associated with the law that can be addressed to help create better retirement outcomes in the future.

Major Successes of ERISA

ERISA has helped Americans prioritize saving for retirement through employer plans at a key juncture when people are living longer. When ERISA was enacted, defined benefit pension plans were the primary type of retirement plan offered by private sector employers.

Thanks in part to ERISA, U.S. pension plans have paid out roughly $8.7 trillion dollars to America’s seniors just since 2009. According to the Investment Company Institute, another $12 trillion in assets are held by pension plans that invest and manage these funds for the benefit of 25 million retirees and millions of workers. Pensions continue to do much of the heavy lifting to preserve a reasonable standard of living for retirees by supplementing Social Security benefits.

Another success of ERISA is that it provides a wide range of protections to workers to ensure retirement assets go toward workers’ retirement benefits and employers are adequately funding these plans. ERISA also created a federal insurance program directed by the PBGC that protects retirement benefits, even if a plan closes or a company goes out of business. Importantly, the program is funded by premiums paid by pension funds, not taxpayers. Typically, the PBGC steps in to oversee plan assets and ensure payment of benefits after a firm ceases to exist. This has proven to be an incredibly successful program, protecting millions of retirees and their beneficiaries.




Author(s): Dan Doonan

Dan Doonan is executive director of the National Institute on Retirement Security, a non-partisan, non-profit research think tank located in Washington, D.C. Dan has been a Forbes Contributor since 2021, and he has more than 25 years of experience on retirement issues from a variety of vantage points – an analyst, consultant and plan trustee. His work is driven by the belief that everyone has a shared interest in a strong and resilient retirement infrastructure in the U.S. that provides sufficient retirement income in the most cost-efficient manner possible. Dan holds a B.S. in Mathematics from Elizabethtown College and is a member of the National Academy of Social Insurance.
 


Publication Date: 2 Sept 2024


Publication Site: Forbes

Comments on Request for Information on Uses, Opportunities, and Risks of Artificial Intelligence in the Financial Services Sector

Mary Pat Campbell

Link: https://www.regulations.gov/document/TREAS-DO-2024-0011-0001/comment

Description:

Publicly available comments on Dept of Treasury’s request for information on AI use, opportunities & risk in financial services sector.

Example: https://www.regulations.gov/comment/TREAS-DO-2024-0011-0010 — comment from ACLI

The NAIC has developed its definition of AI, and the insurance industry has responded with
information in accordance with that definition. Any definition developed by Treasury should
align with, or at a minimum not conflict with, definitions of AI in existing regulatory
frameworks for financial institutions.

The Treasury definition of AI should reflect the following:
o Definitions should be tailored to the different types of AI and the use cases and
risks they pose. The definition used in this RFI is similar to an outdated definition put
forth by the Organization for Economic Coordination and Development (OECD),
which could be narrowed for specific use cases (e.g., tiering of risks under the EU
framework).
o There are also distinctions between generative AI used to make decisions, without
ultimately including human input or intervention, and AI used with human decisionmaking being absolute or the usage being solely for internal efficiencies and
therefore not impactful for customers.
o AI covers a broad range of predictive modeling techniques that would otherwise not
be considered Artificial Intelligence. A refinement to the definition that classifies AI
as machine learning systems that utilize artificial neural networks to make
predictions may be more appropriate.
o The definition of AI should exclude simpler computation tasks that companies have
been using for a long time.

Author(s): Various

Publication Date: accessed 9 Aug 2024

Publication Site: Regulations.gov

Request for Information on Uses, Opportunities, and Risks of Artificial Intelligence in the Financial Services Sector

Mary Pat Campbell

Link: https://www.federalregister.gov/documents/2024/06/12/2024-12336/request-for-information-on-uses-opportunities-and-risks-of-artificial-intelligence-in-the-financial

Excerpt:

SUMMARY:

The U.S. Department of the Treasury (Treasury) is seeking comment through this request for information (RFI) on the uses, opportunities and risks presented by developments and applications of artificial intelligence (AI) within the financial sector. Treasury is interested in gathering information from a broad set of stakeholders in the financial services ecosystem, including those providing, facilitating, and receiving financial products and services, as well as consumer and small business advocates, academics, nonprofits, and others.

DATES:

Written comments and information are requested on or before August 12, 2024.

….

Oversight of AI—Explainability and Bias

The rapid development of emerging AI technologies has created challenges for financial institutions in the oversight of AI. Financial institutions may have an incomplete understanding of where the data used to train certain AI models and tools was acquired and what the data contains, as well as how the algorithms or structures are developed for those AI models and tools. For instance, machine-learning algorithms that internalize data based on relationships that are not easily mapped and understood by financial institution users create questions and concerns regarding explainability, which could lead to difficulty in assessing the conceptual soundness of such AI models and tools.[22]

Financial regulators have issued guidance on model risk management principles, encouraging financial institutions to effectively identify and mitigate risks associated with model development, model use, model validation (including validation of vendor and third-party models), ongoing monitoring, outcome analysis, and model governance and controls.[23] These principles are technology-agnostic but may not be applicable to certain AI models and tools. Due to their inherent complexity, however, AI models and tools may exacerbate certain risks that may warrant further scrutiny and risk mitigation measures. This is particularly true in relation to the use of emerging AI technologies.

Furthermore, the rapid development of emerging AI technologies may create a human capital shortage in financial institutions, where sufficient knowledge about a potential risk or bias of those AI technologies may be lacking such that staff may not be able to effectively manage the development, validation, and application of those AI technologies. Some financial institutions may rely on third-party providers to develop and validate AI models and tools, which may also create challenges in ensuring alignment with relevant risk management guidance.

Challenges in explaining AI-assisted or AI-generated decisions also create questions about transparency generally, and raise concerns about the potential obfuscation of model bias that can negatively affect impacted entities. In the Non-Bank Report, Treasury noted the potential for AI models to perpetuate discrimination by utilizing and learning from data that reflect and reinforce historical biases.[24] These challenges of managing explainability and bias may impede the adoption and use of AI by financial institutions.

Author(s): Department of the Treasury.

Publication Date: 6/12/2024

Publication Site: Federal Register