Enterprise Fraud Risk Program Solicitation No. TRS000645

Teacher Retirement System of Texas

Request for Information (RFI)

NIGP Class/Item Codes: 918-71 IT Consulting

Anticipated Schedule of Events

December 15, 2025 Issuance of RFI

January 15, 2026, 3:00 PM Central Time Deadline for Submission of Information

Table of Contents I. Summary

II. Request For Information

A. Introduction

B. TRS Background

C. Statement of Purpose

D. Scope of Work

E. TRS’s Current Technical Environment Overview

F. TRS Data and AI Platform Standards

G. Non-functional Requirements of the Solution

III. Disclaimers

IV. RFI Content and Format

V. Demonstrations

I. Summary

1. Purpose/Overview Request for Information (“RFI”) to gather information about, recommendations for, and see demonstrations of potential solutions for enterprise systems to identify and prevent fraudulent activity.

2. Type of Solicitation Request for Information (“RFI”)

3. Issuing Office

Teacher Retirement System of Texas Procurement & Contracts 4655 Mueller Blvd Austin, TX 78723 ATTN: Procurement & Contracts

4. Deadline for Submission Information Teacher Retirement System of Texas (“TRS”) no later than January 15, 2026, 3:00 p.m., Central time

5. TRS Procurement and Contracts Representative and point of contact for this Solicitation

Max Hernandez max.hernandez@trs.texas.gov

6. RFI Response and Questions Submitted

Responses to this RFI must be emailed to the TRS representative listed in Item 5, above, and follow the instructions in Section IV. RFI Content and Format mailto:max.hernandez@trs.texas.gov

II. Request For Information

A. Introduction The Teacher Retirement System of Texas (“TRS”), a public pension fund and entity of the State of Texas, issues this Request for Information (“RFI”) to gather information about, recommendations for, and see demonstrations of solutions for enterprise systems to identify and prevent fraudulent activity.

This RFI is to gather information on available comprehensive fraud detection and prevention solutions that will allow TRS to identify and mitigate risks associated with employee fraud, vendor fraud, and third-party fraud. All proposed solutions should leverage industry-leading tools, machine learning, and data analytics to detect anomalies, flag suspicious activities, and support investigative workflows.

This is not a Request for Proposal (“RFP”). No contract will be issued or awarded based on responses to this RFI. Responses to this RFI are strictly voluntary, and there is no penalty for vendors who choose not to participate. For example, not responding to this RFI does not preclude participation in any future RFP, if any is issued. The information obtained in this RFI may help TRS formulate an RFP in the future, to which all qualified companies will be welcome to respond. If an RFP is issued, TRS will post the RFP on the Comptroller’s Electronic State Business Daily (ESBD).

B. TRS Background TRS is a service-oriented public pension system providing retirement, healthcare, and related benefit programs for Texas public education employees, retirees, and their beneficiaries (“Participants”).

The Teacher Retirement System of Texas (“TRS” or “Agency” or “Customer”) manages more than $200 billion in funds and serves more than 2 million public education employees and retirees. The mission of the TRS is to improve the retirement security of their members by prudently investing and managing the Trust assets and delivering benefits that make a positive difference in their lives. One way to ensure TRS’s ability to do this successfully is to set up systems to identify and prevent fraudulent activity within TRS and with those TRS must interact.

The TRS laws, rules, and additional information about TRS and the Board are accessible through the TRS website: https://www.trs.texas.gov.

C. Statement of Purpose TRS intends to learn about the capabilities, services, and experience of vendors in offering enterprise fraud management solutions that offer a powerful comprehensive approach to integrated fraud prevention, monitoring, detection, mitigation, investigation, analysis, policy, and prosecution in several areas that affect its entire enterprise including:

• Member services: Fraudulent disbursement, benefit statements/claims statement fraud, asset misappropriation, employee acting outside of their authority, identity theft, healthcare insurance fraud, falsified documents, fake accounts, etc.

https://www.txsmartbuy.gov/esbd https://www.txsmartbuy.gov/esbd

• Investment Operations: Misrepresentations regarding owed fees and expenses;

misrepresentations during due diligence and ongoing diligence; fraudulent accounts for transfer of capital calls; fake account statements.

• Administrative Operations (Procurement, Financial, HR) o Human Resource Management: Fictitious employees, timesheet fraud, employee misconduct, employee approving own actions, sharing password and login information, resource fraud/theft, etc.

o Procurement: Missing contract documents, data, signatures, different ship to address, purchasing goods from fictitious vendors, scopes of work or evaluation criteria designed to exclude vendors/select preferred vendor, deliverables not included in contract document, purchases don’t match requisition, approving invoices that do not match contract prices, winning bid high compared to cost estimates/industry averages/published pricing, late bid or low bidder (insider information).

o Financial: Fraudulent disbursement, financial statement fraud, asset misappropriation, inventory and other asset fraud, employee misconduct, duplicate invoicing, shell companies, falsified expenses, etc.

• Collusion: Is an aspect of fraud that should be identified with all the items above.

D. Scope of Work RFI Expectations and Format

Responses should demonstrate how proposed solutions can design, optimize, and integrate enterprise fraud management solutions that enable real-time, high-velocity, risk-aware operations with measurable outcomes like reducing false positives, faster case resolution, and improved audit readiness. Respondents should provide the following:

1. Request for Information – Enterprise Fraud Management Solutions

Our organization is seeking information from qualified respondents regarding fraud management including but not limited to fraud prevention, detection, mitigation, investigation, analytics, policy, and prosecution. Solutions that can address a broad spectrum of fraud events listed in Section C, above.

2. Enterprise Fraud Management Solution Scope – High-Level Overview

TRS seeks information about available fraud management solutions capable of addressing a diverse range of use cases across multiple business domains comprising both internal and external risk events (see Section C, above).

Solutions should be secure, scalable, maintainable, demonstrate flexibility, and possess the ability to integrate with the existing technical environment enabling configuration to accommodate TRS’s needs. Vendors with experience deploying comprehensive, cross-functional fraud management capabilities within existing technology environments are strongly encouraged to respond to this RFI.

TRS is interested in hearing from vendors who offer either of the following types of solutions:

Hernandez, Max BU: This doesn’t add new information. I recommend deleting it.

Barnes, Jacquelyn @Hernandez, Max - we would like this section added back, please.

• Commercial off-the-shelf (COTS) solutions with pre-built functionality that can be integrated into TRS’s existing technical environment with minimal customization.

• Modular and extensible solutions that leverage existing data platform investments such as running on Azure, enabling the development and scaling of a comprehensive fraud management capability tailored to our operational needs.

(see additional details below).

Vendors should describe their approach to integrating with current enterprise applications, data sources, and workflows, ensuring seamless configuration and interoperability within an established technical environment. Submissions should demonstrate the ability to leverage TRS’s technology base to deliver comprehensive fraud prevention, detection, investigation, and analytics capabilities, while minimizing disruption, and maximizing operational efficiency.

Submissions should be in a clear, structured, modular layout that enables TRS to assess the quality of vendor capabilities, compliance, and solutions. Submissions must include:

• Solution Architecture

• Functional and Non-functional Capabilities (refer to Section G. Non-functional

Requirements of the solution, below, for additional details.

• Integration and Interoperability

• Data Governance and Security

• Compliance and Regulatory Alignment

• Reporting

• Analytics and AI

• Implementation

• Support and Maintenance approaches section of the solution.

Submissions should also include case study references, as well as any relevant implementation experience across diverse fraud domains. Please include a high-level description of the solution’s uses of a subscription, per-user, per-case, or another pricing model.

3. Vision for Integrated Fraud Management Solution

Provide a strategic vision for how fraud detection intersects with enterprise operations to form a seamless, analytics-driven ecosystem to optimize fraud monitoring, resolution workflows, and risk scoring. The solution should support continuous improvement in fraud prevention, detection accuracy, and operational efficiency across employees, vendors, and third-party domains.

4. Automation and Workforce Optimization

Demonstrate how your solution will reduce manual bottlenecks in fraud detection and investigation, increase accuracy, and free personnel to focus on high-value activities such as policy enforcement, compliance oversight, and stakeholder engagement.

Include examples of how automation improves prioritization of high-risk events and reduces false positives/negatives. Identify FTE workload requirement/level of effort for implementation and operationalize and maintain toolset. Provide function role KSAs to support implementation/operationalization

5. Evidence of Success in Complex Environments Present clear evidence of successful enterprise fraud management solution deployments in peer organizations, particularly those operating in complex regulatory, research, and funding environments. Highlight outcomes such as improved auditability, reduced fraud incidents, and enhanced constituent trust.

6. Implementation Strategy and Best Practices

Describe your proven implementation strategy for deploying enterprise fraud detection systems. Include key phases such as data integration, system configuration, and training.

Detail best practices for technical integration with existing systems (e.g., CRM, Contact channels, ERP, HRIS, procurement platforms), and approaches to effective change management. Explain how you ensure user adoption and minimize disruption to operations. Indicate whether implementation services are outsourced or delivered in-house.

E. TRS’s Current Technical Environment Overview Most pension and health solutions, for both internal and external use, are developed in-house and managed by TRS, except for CRM and virtual contact center, which utilize vendor platforms.

Solutions for procurement, finance, and human resources are primarily provided by vendors and in some cases integrated with other state agency solutions.

TRS operates a modern data and AI platform built on Microsoft Azure serving as a central component for scalable data engineering, advanced analytics, and machine learning. The organization utilizes a broad suite of Azure services to support its cloud-based computing and development needs.

Most TRS employees’ digital experience is powered by Microsoft platforms.

While TRS is actively expanding its cloud capabilities, many of its core systems and critical data assets continue to reside in well-established on-premises active-active data centers. This hybrid environment supports a range of business applications.

F. TRS Data and AI Platform Standards TRS follows modern data architecture best practices to ensure scalability, governance, and interoperability. At the core of the design is the medallion architecture, promoting progressive data refinement—from raw ingestion to curated, business-ready data. Where appropriate, solutions should support this layered approach for both batch and streaming data flows, leveraging open standards such as Apache Delta Lake for ACID transactions, schema enforcement, and time travel.

Our platform emphasizes modularity, separation of compute and storage, and elasticity to handle diverse workloads. We prioritize interoperability through open formats (Delta, Parquet), and strong governance via centralized catalogs and lineage tracking. In addition, we require support for

a. RESTful APIs and OpenAPI Specification for programmatic access

b. standard connectors consistent with an Azure ecosystem

c. Apache Kafka or equivalent for real-time ingestion and event-driven processing.

Vendors should highlight how they align with these architectural principles, including integration with open-source frameworks like Apache Spark and MLflow. This ensures our ecosystem remains vendor-neutral, future-ready, and optimized for both analytical and operational use cases.

Vendor should also highlight how their platform and solutions leverage emerging AI standards Model Context Protocol (MCP) for standardized AI-to-tool integration and Agent-to-Agent (A2A) protocol for secure vendor agent collaboration.

G. Non-functional Requirements of the Solution Integration and Interoperability:

a. Should support modern integration patterns (e.g., REST, GraphQL or gRPC) and batch data exports to connect with internal systems and other vendor products and platforms.

b. Should be modular architecture that allows plug-and-play integration with external fraud signal providers and identity verification services.

Auditability and Transparency:

a. Should provide explainable decision tables and machine learning scoring to reduce false positives while maintaining high fraud detection accuracy.

b. Should support feedback loops that capture investigator dispositions (e.g., confirmed fraud, false positive) and refine detection logic over time.

c. Should provide behavioral analytics and anomaly detection using unsupervised learning to identify subtle fraud patterns.

d. Immutable logs of fraud detection decisions and overrides.

Data Normalization and Aggregation:

a. ETL pipelines capable of ingesting structured and unstructured data from diverse sources.

b. Schema harmonization and metadata tagging to ensure consistent data formats across ingestion points.

c. Batch and stream processing support for legacy and modern data flows.

Scalability and Architecture:

a. Modern architecture with support for horizontal scaling.

b. Solutions should support Real-time processing and be scalable to detect/block high-volume transactions monitoring including deepfake content.

c. Low-latency alerting.

Security and Privacy Compliance:

a. Compliance with applicable federal and state standards for data protection and privacy including encryption and audit trails.

b. Integration with existing authentication services.

c. Role-based access controls and audit trails for investigative workflows and compliance reporting.

d. Data ownership and storage locations adherence.

e. Retention policies aligned with pension and health mandates.

Maintainability and extensibility:

a. Modular rule configuration without code changes to adapt to changing fraud definitions and thresholds.

b. Version control of fraud detection models and rules.

c. Integrations and plug-ins for third-party analytical and AI solutions.

d. Disaster recovery & business continuity plan.

III. Disclaimers This RFI is issued solely for information and planning purposes and does not constitute a Request for Proposal or an obligation on the part of TRS.

Responses to RFIs are public information. TRS must adhere to the requirements of Chapter 552 of the Texas Government Code (the "Texas Public Information Act") regarding the disclosure of public information. TRS does not have authority to agree that any information submitted will not be subject to disclosure under the Texas Public Information Act. Respondents are advised to consult with their legal counsel concerning disclosure issues resulting from this process and to take precautions to safeguard trade secrets and proprietary or otherwise confidential information. TRS assumes no obligation or responsibility relating to the disclosure or nondisclosure of information submitted by Respondents. If it is necessary for Respondent to include trade secrets or proprietary or otherwise confidential information in its submittal, Respondent must clearly mark in bold red letters the term “CONFIDENTIAL” using at least 14-point font, on that specific part or page of the submittal which Respondent believes to be confidential. All submittals and parts of submittals that are not marked confidential will be considered public information. If TRS receives a public information request seeking information marked by Respondent as confidential, Respondent will receive notice of the request as required by the Texas Public Information Act. If TRS receives a public information request for submittals and parts of submittals that are not marked confidential, the information will be disclosed to the public as required by the Texas Public Information Act. Do not mark your entire Response as “CONFIDENTIAL.”

Responses to this RFI will not be returned.

Responders are solely responsible for all expenses associated with responding to this RFI.

IV. RFI Content and Format Responses should address the information requested by TRS above. Respondents are encouraged to propose any other solutions that may benefit TRS in addressing its stated needs. Respondents should present their information as concisely as possible and not provide information that is outside the scope of this RFI.

Respondents must email their Response to max.hernandez@trs.texas.gov. The email Subject line must include:

• Respondent name

• TRS000645 - Enterprise Fraud Risk Program mailto:max.hernandez@trs.texas.gov

V. Demonstrations TRS may request demonstrations from Respondents whose solutions show an ability to provide the services described in this RFI in person or via video conference.