Use of Artificial Intelligence Systems By Insurers

MISSISSIPPI INSURANCE DEPARTMENT MIKE CHANEY Commissjoner of Insurance Stale Fire Marshal DAVID BROWNING Depuly Commissioner of Insurance 501 N. WEST STREET, SUITE 1001 WOOLFOLK BUILDING JACKSON, MISSISSIPPI 39201 www.mid.ms.gov MISSISSIPPI DEPARTMENT OF INSURANCE MAILING ADDRESS Posl Offi<:e Box 79 Jackson, Mississippi 3920~79 TELEPHONE: (601) 359-3569 FAX: (601) 359-2474 WATS: 1-800-562-2957 (Incoming-USA) BULLETIN 2026-9 USE OF ARTIFICIAL INTELLIGENCE SYSTEMS BY INSURERS July 22, 2026 This Bulletin is issued by the Mississippi Insurance Department (MID) to remind all Insurers that hold certificates of authority to do business in the state that decisions or actions impacting consumers that are made or suppo1ted by advanced analytical and computational technologies, including Artificial Intelligence (AI) Systems (as defined below), must comply with all applicable insurance laws and regulations. This includes those laws that address unfair trade practices and unfair discrimination. This Bulletin sets forth MID's expectations as to how Insurers will govern the development/acquisition and use of certain AI technologies, including the AI Systems described herein. This Bulletin also advises Insurers of the type of information and documentation that MID may request during an investigation or examination of any Insurer regarding its use ofsuch technologies and AI Systems. SECTION 1: INTRODUCTION, BACKGROUND, AND LEGISLATIVE AUTHORITY Background AI is transforming the insurance industry. AI techniques are deployed across all stages of the insurance life cycle, including product development, marketing, sales and distribution, underwriting and pricing, policy servicing, claim management, and fraud detection. AI may facilitate the development ofinnovative products, improve consumer interface and service, simplify and automate processes, and promote efficiency and accuracy. However, AI, including AI Systems, can present unique risks to consumers, including the potential for inaccuracy, unfair discrimination, data vulnerability, and lack of transparency and explainability. Insurers should take actions to minimize these risks. MID encourages the development and use ofinnovation and AI Systems that contribute to safe and stable insurance markets. However, MID expects that decisions made and actions taken by Insurers using AI Systems will comply with all applicable federal and state laws and regulations. MID recognizes the Principles ofArtificial Intelligence that the NAIC adopted in 2020 as an appropriate source of guidance for Insurers as they develop and use AI systems. Those principles emphasize the importance of the fairness and ethical use of AI; accountability; compliance with state laws and regulations; transparency; and a safe, secure, fair, and robust system. These fundamental principles should guide Insurers in their development and use ofAl Systems and underlie the expectations set forth in this Bulletin. Legislative Authority The regulatory expectations and oversight considerations set forth in Section 3 and Section 4 of this Bulletin rely on the following laws and regulations: • Unfair Trade Practices Act: The Unfair Trade Practices Act, Miss. Code Ann. §§ 83-5-29 through 83-3-51 (Rev. 2022), (UTPA) regulates trade practices in insurance by: 1) defining practices that constitute unfair methods of competition or unfair or deceptive acts and practices; and 2) prohibiting the trade practices so defined or determined. Actions taken by Insurers in the state must not violate the UTPA, regardless ofthe methods the Insurer used to determine or support its actions. As discussed below, Insurers are expected to adopt practices, including governance frameworks and risk management protocols, that are designed to ensure that the use ofAI Systems does not result in: unfair trade practices. • Corporate Governance Annual Disclosure Act: The Corporate Governance Annual Disclosure Act, Miss. Code Ann.§§ 83-5-701 through 83-5-717, (CGAD), requires Insurers to report on governance practices and to provide a summary of the Insurer's corporate governance structure, policies, and practices. The content, form, and filing requirements for CGAD information are set forth in the Corporate Governance Annual Disclosure Regulation, 19 Miss. Admin. Code, Part 1, Chapter 43, (CGAD-R). The requirements of CGAD and CGAD-R apply to elements of the Insurer's corporate governance framework that address the Insurer's use ofAI Systems to support actions and decisions that impact consumers. r I Property and Casualty Rating: Miss. Code Ann. § 83-2-3 (Rev. 2022) requires that property/casualty (P/C) insurance rates not be excessive, inadequate, or unfairly discriminatory. The requirements of Miss. Code Ann. § 83-2-3 (Rev. 2022) apply regardless of the methodology that the Insurer used to develop rates, rating rules, and rating plans subject to those provisions. That means that an Insurer is responsible for assuring that rates, rating rules, and rating plans that are developed using AI teclrniques and Predictive Models that rely on data and Machine Learning do not result in excessive, inadequate, or unfairly discriminatory insurance rates with respect to all forms of casualty insurance- including fidelity, surety, and guaranty bond- and to all forms of property insurance-including fire, marine, and inland marine insurance, and any combination ofany of the foregoing. 2 Market Conduct: A1iss. Code Ann.§ 83-5-205 (Rev. 2022) establishes the framework pursuant to which MID conducts market conduct actions. These are comprised of the full range of activities that MID may initiate to assess and address the market practices of Insurers, beginning with market analysis and extending to targeted examinations. Market conduct actions are separate from, but may result from, individual complaints made by consumers asserting illegal practices by Insurers. An Insurer's conduct in the state, including its use ofAI Systems to make or support actions and decisions that impact consumers, is subject to investigation, including market conduct actions. Section 4 ofthis Bulletin provides guidance on the kinds of information and documents that MID may request in the context ofan AI-focused investigation, including a market conduct action. SECTION 2: DEFINITIONS For the purposes of this Bulletin the following terms are defined: "Adverse Consumer Outcome" refers to a decision by an Insurer that is subject to insurance regulatory standards enforced by MID that adversely impacts the consumer in a manner that violates those standards. "Algorithm" means a clearly specified mathematical process for computation; a set of rules that, iffollowed, will give a prescribed result. "Al System" is a machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, content (such as text, images, videos, or sounds), or other output influencing decisions made in real or virtual enviromnents. AI Systems are designed to operate with varying levels ofautonomy. "Artificial Intelligence (Al)" refers to a branch of computer science that uses data processing systems that perform functions normally associated with human intelligence, such as reasoning, learning, and self-improvement, or the capability ofa device to perform functions that are normally associated with human intelligence such as reasoning, learning, and self-improvement. This definition considers machine learning to be a subset ofartificial intelligence. "Degree of Potential Harm to Consumers" refers to the severity of adverse economic impact that a consumer might experience as a result ofan Adverse Consumer Outcome. "Generative Artificial Intelligence (Generative Al)" refers to a class ofAI Systems that generate content in the form of data, text, images, sounds, or video, that is similar to, but not a direct copy of, pre-existing diita or content. "Machine Leaming (ML)" Refers to a field within artificial intelligence that focuses on the ability ofcomputers to learn from provided data without being explicitly programmed. 3 "Model Drift" refers to the decay of a model's performance over time arising from underlying changes such as the definitions, distributions, and/or statistical properties between the data used to train the model and the data on which it is deployed. "Predictive Model" refers to the mining ofhistoric data using algorithms and/or machine learning to identify patte1i.1s and predict outcomes that can be used to make or support the making of decisions. "Third Party" for purposes of this Bulletin means an organization other than the Insurer that provides services, data, or other resources related to AI. SECTION 3: REGULATORY GUIDANCE AND EXPECTATIONS Decisions subject to regulatory oversight that are made by Insurers using AI Systems must comply with the legal and regulatory standards that apply to those decisions, including unfair trade practice laws. These standards require, at a minimum, that decisions made by Insurers are not inaccurate, arbitrary, capricious, or unfairly discriminatory. Compliance with these standards is required regardless of the tools and methods Insurers use to make such decisions. However, because, in the absence ofproper controls, AI has the potential to increase the risk of inaccurate, arbitrary, capricious, or unfairly discriminatory outcomes for consumers, it is important that Insurers adopt and implement controls specifically related to their use ofAI that are designed to mitigate the risk ofAdverse Consumer Outcomes. Consistent therewith, all Insurers authorized to do business in this state are expected to develop, implement, and maintain a written program (an "AIS Program") for the responsible use ofAI Systems that make, or support decisions related to regulated insurance practices. The AIS Program should be designed to mitigate the risk ofAdverse Consumer Outcomes, including, at a minimum, the statutory provisions set forth in Section 1ofthis Bulletin. MID recognizes that robust governance, risk management controls, and internal audit functions play a core role in mitigating the risk that decisions driven by AI Systems will violate unfair trade practice laws and other applicable existing legal standards. MID also encourages the development and use of verification and testing methods to identify errors and bias in Predictive Models and AI Systems, as well as the potential for unfair discrimination in the decisions and outcomes resulting from the use ofPredictive Models and AI Systems. The controls and processes that an Insurer adopts and implements as part ofits AIS Program should be reflective of, and commensurate with, the Insurer's own assessment of the degree and nature of risk posed to consumers by the AI Systems that it uses, considering: (i) the nature ofthe decisions being made, informed, or supported using the AI System; (ii) the type and Degree of Potential Harm to Consumers resulting from the use of Al Systems; (iii) the extent to which humans are involved in the final decision-making process; (iv) the transparency and explainability ofoutcomes to the impacted consumer; and (v) the extent and scope ofthe insurer's use or reliance on data, Predictive Models, and AI Systems from third parties. Similarly, controls and processes 4 should be commensurate with both the risk ofAdverse Consumer Outcomes and the Degree of Potential Harm to Consumers. As discussed in Section 4, the decisions made as a result ofan Insurer's use ofAI Systems are subject to MID's examination to determine that the reliance on AI Systems are compliant with all applicable existing legal standards governing the conduct of the Insurer. AIS Program Guidelines 1.0 General Guidelines 1.1 The AIS Program should be designed to mitigate the risk that the Insurer's use ofan Al System will result in Adverse Consumer Outcomes. 1.2 The AIS Program should address governance, risk management controls, and internal audit functions. 1.3 The AIS Program should vest responsibility for the development, implementation, monitoring, and oversight ofthe AIS Program and for setting the Insurer's strategy for AI Systems with senior management accountable to the board or an appropriate committee of the board. 1.4 The AIS Program should be tailored to and propo11ionate with the Insurer's use and reliance on AI and AI Systems. Controls and procedures should be focused on the mitigation of Adverse Consumer Outcomes and the scope ofthe controls and procedures applicable to a given AI System use case should reflect and align with the Degree ofPotential Harm to Consumers with respect to that use case. 1.5 The AIS Program may be independent of or part of the Insurer's existing Enterprise Risk Management (ERM) program. The AIS Program may adopt, incorporate, or rely upon, in whole or in part, a framework or standards developed by an official third-party standard organization, such as the National Institute of Standards and Technology (NIST) A11ificial Intelligence Risk Management Framework, Version 1.0. 1.6 The AIS Program should address the use ofAI Systems across the insurance life cycle, including areas such as product development and design, marketing, use, underwriting, rating and pricing, case management, claim administration and payment, and fraud detection. 1.7 The AIS Program should address all phases of an Al System's life cycle, including design, development, validation, implementation (both systems and business), use, on-going monitoring, updating and retirement. 1.8 The AIS Program should address the Al Systems used with respect to regulated insurance practices whether developed by the Insurer or a third-party vendor. 5 1.9 The AIS Program should include processes and procedures providing notice to impacted consumers that AI Systems are in use and provide access to appropriate levels of information based on the phase ofthe insurance life cycle in which the AI Systems are being used. 2.0 Governance The AIS Program should include a governance framework for the oversight ofAl Systems used by the Insurer. Governance should prioritize transparency, fairness, and accountability in the design and implementation of the AI Systems, recognizing that proprietary and trade secret information must be protected. An Insurer may consider adopting new internal governance structures or rely on the Insurer's existing governance structures; however, in developing its governance framework, the Insurer should consider addressing the following items: 2.1 The policies, processes, and procedures, including risk management and internal controls, to be followed at each stage ofan AI System life cycle, from proposed development to retirement. 2.2 The requirements adopted by the Insurer to document compliance with the AIS Program policies, processes, procedures, and standards. Documentation requirements should be developed with Section 4 in mind. 2.3 The Insurer's internal AI System governance accountability structure, such as: a) The formation of centralized, federated, or otherwise constituted committees comprised of representatives from appropriate disciplines and units within the Insurer, such as business units, product specialists, actuarial, data science and analytics, underwriting, claims, compliance, and legal. b) Scope of responsibility and authority, chains of command, and decisional hierarchies. c) . The independence ofdecision-makers and lines ofdefense at successive stages ofthe AI System life cycle. d) Monitoring, auditing, escalation, and reporting protocols and requirements. e) Development and implementation of ongoing training and supervision of personnel. 2.4 Specifically with respect to Predictive Models: the Insurer's processes and procedures for designing, developing, verifying, deploying, using, updating, and monitoring Predictive Models, including a description of methods used to detect and address errors, performance issues, outliers, or unfair discrimination in the insurance practices resulting from the use ofthe Predictive Model. 6 3.0 Risk Management and Internal Controls The AIS Program should document the Insurer's risk identification, mitigation, and management framework and internal controls for AI Systems generally and at each stage ofthe AI System life cycle. Risk management and internal controls should address the following items: 3.1 The oversight and approval process for the development, adoption, or acquisition ofAI Systems, as well as the identification ofconstraints and controls on automation and design to align and balance function with risk. 3.2 Data practices and accountability procedures, including data currency, lineage, quality, integrity, bias analysis and minimization, and suitability. 3.3 Management and oversight of Predictive Models (including algorithms used therein), including: a) Inventories and descriptions ofthe Predictive Models. b) Detailed documentation of the development and use of the Predictive Models. c) Assessments such as interpretability, repeatability, robustness, regular tuning, reproducibility, traceability, model drift, and the auditability of these measurements where appropriate. d) Validating, testing, and retesting as necessary to assess the generalization of AI System outputs upon implementation, including the suitability of the data used to develop, train, validate and audit the model. Validation can take the form ofcomparing model performance on unseen data available at the time ofmodel development to the performance observed on data post-implementation, measuring performance against expert review, or other methods. 3.4 The protection of non-public information, particularly consumer information, including unauthorized access to the Predictive Models themselves. 3.5 Data and record retention. 3.6 Specifically with respect to Predictive Models: a narrative description of the model's intended goals and objectives and how the model is developed and validated to ensure that the AI Systems that rely on such models correctly and efficiently predict or implement those goals and objectives. 7 4.0 Third-Party AI Systems and Data Each AIS Program should address the Insurer's process for acquiring, using, or relying on (i) third-party data to develop AI Systems; and (ii) AI Systems developed by a third party, which may include, as appropriate, the establislunent of standards, policies, procedures, and protocols relating to the following considerations: 4.1 Due diligence and the methods employed by the Insurer to assess the third party and its data or AI Systems acquired from the third party to ensure that decisions made or supported from such AI Systems that could lead to Adverse Consumer Outcomes will meet the legal standards imposed on the Insurer itself. 4.2 Where appropriate and available, the inclusion ofterms in contracts with third parties that: a) Provide audit rights and/or entitle the Insurer to receive audit reports by qualified auditing entities. b) Require the third party to cooperate with the Insurer with regard to regulatory inquiries and investigations related to the Insurer's use of the third­ party's product or services. 4.3 The performance of contractual rights regarding audits and/or other activities to confirm the third-party's compliance with contractual and, where applicable, regulatory requirements. SECTION 4: REGULATORY OVERSIGHT AND EXAMINATION CONSIDERATIONS MID's regulatory oversight of Insurers includes oversight of an Insurer's conduct in the state, including its use of AI Systems to make or support decisions that impact consumers. Regardless of the existence or scope of a written AIS Program, in the context of an investigation or market conduct action, an Insurer can expect to be asked about its development, deployment, and use ofAI Systems, or any specific Predictive Model, AI System or application and its outcomes (including Adverse Consumer Outcomes) from the use of those AI Systems, as well as any other information or documentation deemed relevant by MID. Insurers should expect those inquiries to include (but not be limited to) the Insurer's governance framework, risk management, and internal controls (including the considerations identified in Section 3 ). In addition to conducting a review ofany ofthe items listed in this Bulletin, a regulator may also ask questions regarding any specific model, AI System, or its application, including requests for the following types of information and/or documentation: 8 1. Information and Documentation Relating to AI System Governance, Risk Management, and Use Protocols 1.1. Information and documentation related to or evidencing the Insurer's AIS Program, including: a) The written AIS Program. b) Information and documentation relating to or evidencing the adoption ofthe AIS Program. c) The scope of the Insurer's AIS Program, including any AI Systems and technologies not included in or addressed by the AIS Program. d) How the AIS Program is tailored to and proportionate with the Insurer's use and reliance on AI Systems, the risk ofAdverse Consumer Outcomes, and the Degree of Potential Harm to Consumers. e) The policies, procedures, guidance, trammg materials, and other information relating to the adoption, implementation, maintenance, monitoring, and oversight ofthe Insurer's AIS Program, including: i. Processes and procedures for the development, adoption, or acquisition ofAI Systems, such as: (1) Identification ofconstraints and controls on automation and design. (2) Data governance and controls, any practices related to data lineage, quality, integrity, bias analysis and minimization, suitability, and Data Currency. ii. Processes and procedures related to the management and oversight of Predictive Models, including measurements, standards, or thresholds adopted or used by the Insurer in the development, validation, and oversight of models and AI Systems. iii. Protection of non-public information, particularly consumer information, including unauthorized access to Predictive Models themselves. 1.2 Information and documentation relating to the Insurer's pre-acquisition/pre-use diligence, monitoring, oversight, and auditing ofdata or AI Systems developed by a third party. 9 1.3 Information and documentation relating to or evidencing the Insurer's implementation and compliance with its AIS Program, including documents relating to the Insurer's monitoring and audit activities respecting compliance, such as: a) Documentation relating to or evidencing the formation and ongoing operation of the Insurer's coordinating bodies for the development, use, and oversight ofAI Systems. b) Documentation related to data practices and accountability procedures, including data lineage, quality, integrity, bias analysis and minimization, suitability, and Data Currency. c) Management and oversight of Predictive Models and AI Systems, including: i. The Insurer's inventories and descriptions ofPredictive Models, and Al Systems used by the Insurer to make or support decisions that can result in Adverse Consumer Outcomes. ii. As to any specific Predictive Model or AI System that is the subject of investigation or examination: (1) Documentation of compliance with all applicable AI Program policies, protocols, and procedures in the development, use, and oversight ofPredictive Models and AI Systems deployed by the Insurer. (2) Information about data used in the development and oversight ofthe specific model or AI System, including the data source, provenance, data lineage, quality, integrity, bias analysis and minimization, suitability, and Data Currency. (3) Information related to the techniques, measurements, thresholds, and similar controls used by the Insurer. d) Documentation related to validation, testing, and auditing, including evaluation of Model Drift to assess the reliability of outputs that influence the decisions made based on Predictive Models. Note that the nature ofvalidation, testing, and auditing should be reflective ofthe underlying components of the AI System, whether based on Predictive Models or Generative AI. 10 2. Third-Party AI Systems and Data In addition, if the investigation or examination concerns data, Predictive Models, or AI Systems collected or developed in whole or in part by third parties, the Insurer should also expect MID to request the following additional types of information and documentation. 2.1 Due diligence conducted on third parties and their data, models, or AI Systems. 2.2 Contracts with third-party AI System, model, or data vendors, including terms relating to representations, warranties, data security and privacy, data sourcing, intellectual property rights, confidentiality and disclosures, and/or cooperation with regulators. 2.3 Audits and/or confirmation processes performed regarding third-party compliance with contractual and, where applicable, regulatory obligations. 2.4 Documentation pertaining to validation, testing, and auditing, including evaluation of Model Drift. MID recognizes that Insurers may demonstrate their compliance with the laws that regulate their conduct in the state in their use ofAI Systems through alternative means, including tluough practices that differ from those described in this Bulletin. The goal ofthe Bulletin is not to prescribe specific practices or to prescribe specific documentation requirements. Rather, the goal is to ensure that Insurers in the state are aware of MID's expectations as to how AI Systems will be governed and managed and of the kinds of information and documents about an Insurer's AI Systems that MID expects an Insurer to produce when requested. As in all cases, investigations and market conduct actions may be performed using procedures that vary in nature, extent, and timing in accordance with regulatory judgment. Work performed may include inquiry, examination ofcompany documentation, or any of the continuum of market actions described in the NAIC's Market Regulation Handbook. These activities may involve the use of contracted specialists with relevant subject matter expertise. Nothing in this Bulletin limits the authority of MID to conduct any regulatory investigation, examination, or enforcement action relative to any act or omission ofany Insurer that MID is authorized to perform. MIKE CH \ COMMISSIONE:;:SURANCE 11