Announcement: SOA congratulates the new ASAs and CERAs for October 2026.

Log in / My SOA

The Right AI Tool for the Job: How Actuaries Choose Between Models, Platforms, and Architectures RFP

Background and Purpose

Today, actuaries have many AI options to choose from: general-purpose large language models, actuarial-specific copilots, traditional statistical and machine learning platforms (GLMs, GBMs, neural nets), low-code modeling environments, vendor platforms, open-source tools, and fully custom in-house builds. At the same time, actuaries are under real pressure to adopt AI with pace while making choices that are practical, defensible, and fit for purpose. The combination of these factors makes research on how to choose the right AI for the job very valuable.

Existing guidance tends to address either technology or high-level governance. But there is less research on the actual decision processes actuaries go through, including the trade-offs among interpretability, regulatory defensibility, matching price to risk, cost, speed, integration with existing workflows, and whether to use a vendor solution, build internally, or pursue a hybrid approach. These decisions also vary between practice areas and organizational contexts. The intent of this research is to produce output that is useful to practicing actuaries engaging AI in actuarial processes, including a practical decision matrix or framework that can help actuaries evaluate which models, platforms, architectures, and sourcing approaches are appropriate for different use cases.

Research Objective

The Society of Actuaries Research Institute is seeking researchers to produce a practical, evidence-based framework to help actuaries determine the appropriate AI model, platform, architecture, and sourcing approach for different actuarial use cases. The research should address how actuaries can compare general-purpose AI tools, AI tools supplied by actuarial-specific vendor platforms, open-source tools, low-code environments, internally developed solutions, and hybrid approaches. The objective is not simply to catalogue available technologies, but to provide a practical decision matrix that helps actuaries evaluate which approach is fit for purpose given the use case, risk level, governance requirements, implementation constraints, and organizational context.

Map the decision space. The research should map the range of AI-related choices facing actuaries, including model selection, platform selection, deployment architecture, data integration, workflow design, and build-versus-buy decisions. The scope should include where relevant, valuation, financial reporting, actuarial modeling, pricing, underwriting, assumption setting, experience studies, reserving, enterprise risk management, ALM, management reporting, documentation, peer review, and governance activities.

Identify he criteria actuaries use, or should use, to choose. The research should identify practical selection criteria such as accuracy, explainability, regulatory defensibility, model risk, auditability, data privacy and security, implementation effort, cost, speed to deployment, scalability, maintainability, integration with existing actuarial systems, vendor dependency, internal talent requirements, and the level of human oversight needed. The framework should distinguish between lower-risk productivity use cases and higher-impact actuarial processes where stronger controls, validation, documentation, and governance may be required.

Examine organizational and governance context. The research should consider how organizational context affects AI tool selection, including company size, practice area, technical maturity, availability of actuarial and data science talent, IT and compliance requirements, procurement processes, risk appetite, data infrastructure, cloud strategy, and existing vendor relationships. Researchers should also consider how choices may differ across life, health, retirement, and property and casualty applications.

Produce a practical decision framework. The research should produce a practitioner-oriented decision framework, checklist, or matrix that can be used to evaluate alternative AI approaches for specific actuarial use cases. For example, the framework might compare when a vendor platform is appropriate versus when an in-house or open-source solution is preferable; when a rules-based, statistical, machine learning, or generative AI approach is sufficient; and when a hybrid architecture is more appropriate than a single-tool solution. The framework should be supported by practical examples or scenarios that demonstrate how actuaries would apply it in areas such as valuation, modeling, pricing, underwriting, reserving, experience studies, reporting, and governance.

Deliverables should include a publishable written report; a practical decision framework, matrix, or checklist; illustrative actuarial use-case examples; and a summary suitable for presentation to a professional audience, such as an SOA webinar or meeting. Proposals are encouraged to include a targeted literature or market scan, practitioner interviews or surveys, case studies, and examples showing how the framework can be applied to common actuarial decisions involving models, platforms, architectures, and vendor versus in-house solutions.

Examples of actuarial use-case areas that may be addressed include:

  • Life and annuity valuation, financial reporting, and assumption governance.
  • Actuarial modeling, projection systems, model conversion, and model governance.
  • Pricing, product development, and risk classification.
  • Underwriting, triage, evidence review, and decision support.
  • Experience studies, assumption setting, and trend analysis.
  • Reserving, claims analytics, and IBNR estimation.
  • ALM, scenario analysis, capital modeling, and enterprise risk management.
  • Management reporting, documentation, peer review, and actuarial governance workflows.

Note that the list above is not meant to be exhaustive but merely examples of relevant topics that may be researched.

Proposal Requirements

To facilitate the evaluation of proposals, the following information should be submitted:

  1. Resumes of the researcher(s), including any graduate student(s) expected to participate, indicating how their background, education and experience bear on their qualifications to undertake the research. If more than one researcher is involved, a single individual should be designated as the lead researcher and primary contact. The person submitting the proposal must be authorized to speak on behalf of all the researchers as well as for the firm or institution on whose behalf the proposal is submitted.
  2. An outline of the approach to be used (e.g., literature search, model, etc.), emphasizing issues that require special consideration. Details should be given regarding the techniques to be used, collateral material to be consulted, and possible limitations of the analysis.
  3. A description of the expected deliverables and any supporting data, tools or other resources to be used.
  4. Cost estimates for the research, including computer time, salaries, report preparation, material costs, etc. Such estimates can be in the form of hourly rates, but in such cases, time estimates should also be included. Any guarantees as to total cost should be given and will be considered in the evaluation of the proposal. While cost will be a factor in the evaluation of the proposal, it will not necessarily be the decisive factor.

As a guide for developing project budgets, please review the Historical Project Cost Guide (see Appendix).

Please note that as a policy, the SOA Research Institute generally does not provide funding to cover academic institution overhead expenses.

  1. A schedule for completion of the research, identifying key dates or time frames for research completion and report submissions. The SOA is interested in completing this project in a timely manner. Suggestions in the proposal for ensuring timely delivery, such as fee adjustments, are encouraged.
  2. Other related factors that give evidence of a proposer's capabilities to perform in a superior fashion should be detailed.
  3. The proposal length, not including researcher resumes, should be no more than 5 pages in length.
  4. The proposal should not be written by AI but in the researcher’s own words.
  5. Any use of AI for any purpose in generating the proposal should be fully disclosed.

Selection Process

The SOA will appoint a Project Oversight Group (POG) to oversee the project. The POG is responsible for recommending to the Section Research Committee the proposal to be funded, if any. Input from other knowledgeable individuals also may be sought, but the Section Research Committee will make the final recommendation, subject to Society of Actuaries Research Institute (SOA) leadership approval. An SOA staff research actuary will provide staff actuarial support.

Questions

Any questions regarding this RFP should be directed to research-ait@soa.org with the subject line: How Actuaries Choose Between Models, Platforms, and Architectures.

Submission of Proposal

Please email your proposal to research-ait@soa.org with the subject: How Actuaries Choose Between Models, Platforms, and Architectures; proposals must be received no later than November 13, 2026. It is anticipated that all proposers will be informed of the status of their proposal by the end of February 2027.

Conditions

The selection of a proposal is conditioned upon and not considered final until a Letter of Agreement is executed by both the Society of Actuaries Research Institute and the researcher.

The Society of Actuaries Research Institute reserves the right to not award a contract for this research. Reasons for not awarding a contract could include, but are not limited to, a lack of acceptable proposals or a finding that insufficient funds are available. The Society of Actuaries Research Institute also reserves the right to redirect the project as is deemed advisable.

The Society of Actuaries Research Institute plans to hold the copyright to the research and to publish the results with appropriate credit given to the researcher(s).

The Society of Actuaries Research Institute may choose to seek public exposure or media attention for the research. By submitting a proposal, you agree to cooperate with the [Society of Actuaries/sponsoring entity] in publicizing or promoting the research and responding to media requests.

The Society of Actuaries may also choose to market and promote research to members, candidates and other interested parties. You agree to perform promotional communication requested by the Society of Actuaries, which may include, but is not limited to, leading a webcast on the research, presenting the research at an SOA meeting, and/or writing an article on the research for an SOA newsletter.

Conflict of Interest

You agree to disclose any of your material business, financial and organizational interests and affiliations which are or may be construed to be reasonably related to the interest, activities and programs of the Society of Actuaries or the Society of Actuaries Research Institute.

Appendix

The cost ranges below are intended as a guide for budgeting project costs for proposals in response to SOA Research Institute Request for Proposals (RFP). Please note these figures span the 33rd to 66th percentiles for all projects as well as projects that involve a specific approach (lit review, survey, etc.). They are based on historical costs over several recent years. Expected costs for some RFPs may fall outside these ranges depending on the nature of the work and resources required for completion.

All Contracted Projects

This category includes all contracted projects that the Institute has undertaken within the last several years. The 33rd – 66th percentile project costs range is $25,000 – $50,000.

Literature Reviews

This category includes projects that involved only a literature review or the cost for the portion of a larger project that included a literature review. The 33rd – 66th percentile project costs range is $15,000 – $20,000.

Surveys

This category includes all projects that had a survey as their primary component. The 33rd-66th percentile project costs range is $28,000 – $55,000.