Amendment 1_FSA RFI Responses_Round One_081525.pdf

FSA Technology Modernization Initiative RFI Response - Round 1 # Question FSA Response

1 Is this activity considered a new requirement or a follow-on to an existing contract?

The acquisition strategy has not been determined but the input from this RFI will assist with formulating the strategy. Our goal is to provide opportunities for all socio-economic categories where applicable.

2 If there is an incumbent, would you be able to share the vendor’s name and the contract number so that I may identify it in FPDS?

There are multiple incumbents; but the primary vendors are but not limited to Accenture Federal Services, LLC, General Dynamic Information Technology, Peraton, JazzSolutions, and Focus-Nava.

3 Noted that it was a set-aside, which type of set-aside? The acquisition strategy has not been determined, the input from this RFI will assist with formulating the strategy. Therefore, this requirement has not been set-aside. Our goal is to provide opportunities for all socio economic categories where applicable.

4 Does this initiative include Accenture’s Digital and Customer Care contract?

Yes, along with other FSA systems and projects as referenced in the RFI.

5 I wanted to learn more about this opportunity but saw that the RFI and other documents were deleted on sam.gov, could you share them with me or can you give us an update on when the information will be updated

Federal Student Aid (FSA) confirmed receipt of vendor inquiry and stated that it will post all questions and answers to the RFI mid-August. Also, referred vendor to the posting again to click on the working attachment links. The two attachments which were deleted were replaced with updated attachments with the RFI post having a total of three working attachment links.

1. FSA Modernization RFI_072125.docx

2. Attachment 1_Context Diagrams.zip

3. Attachment 2_FSA Modernization RFI QA_072125.docx

6 The documents related to 1 and 3 are deleted. Can you repost?

1. FSA Modernization RFI

2. Attachment 1: Context Diagrams

3. Attachment 2: FSA Modernization RFI Q&As

Federal Student Aid (FSA) confirmed receipt of vendor inquiry and stated that it will post all questions and answers to the RFI mid-August. Also, referred vendor to the posting again to click on the working attachment links. The two attachments which were deleted were replaced with updated attachments with the RFI post having a total of three working attachment links.

1. FSA Modernization RFI_072125.docx

2. Attachment 1_Context Diagrams.zip

3. Attachment 2_FSA Modernization RFI QA_072125.docx

7 It seems the attachments for the RFI and the Q&A in the SAM notice have been deleted. Is it possible to get these?

Federal Student Aid (FSA) confirmed receipt of vendor inquiry and stated that it will post all questions and answers to the RFI mid-August. Also, referred vendor to the posting again to click on the working attachment links. The two attachments which were deleted were replaced with updated attachments with the RFI post having a total of three working attachment links.

1. FSA Modernization RFI_072125.docx

2. Attachment 1_Context Diagrams.zip

3. Attachment 2_FSA Modernization RFI QA_072125.docx

8 We noticed that the attachments have been deleted on

Sam.gov. Could you please send these attachments to us as we intend to respond to this RFI.

Federal Student Aid (FSA) confirmed receipt of vendor inquiry and stated that it will post all questions and answers to the RFI mid-August. Also, referred vendor to the posting again to click on the working attachment links. The two attachments which were deleted were replaced with updated attachments with the RFI post having a total of three working attachment links.

1. FSA Modernization RFI_072125.docx

2. Attachment 1_Context Diagrams.zip

3. Attachment 2_FSA Modernization RFI QA_072125.docx

9 Currently the RFI documents are showing deleted from SAM. Will those documents be updated to support responses?

Federal Student Aid (FSA) confirmed receipt of vendor inquiry and stated that it will post all questions and answers to the RFI mid-August. Also, referred vendor to the posting again to click on the working attachment links. The two attachments which were deleted were replaced with updated attachments with the RFI post having a total of three working attachment links.

1. FSA Modernization RFI_072125.docx

2. Attachment 1_Context Diagrams.zip

3. Attachment 2_FSA Modernization RFI QA_072125.docx

10 Has there been any update regarding the industry day? I just want to confirm that details will be provided with an update to the RFI, or if another method will be used.

Information on Industry Day, the link for the registration, and responses to the questions is included in Amendment #1 of the RFI.

11 I noticed that the original and the amended RFIs are both in deleted status and are not downloadable. The only documents that are downloadable are the context diagrams. Is there a way to obtain the RFI please?

Federal Student Aid (FSA) confirmed receipt of vendor inquiry and stated that it will post all questions and answers to the RFI mid-August. Also, referred vendor to the posting again to click on the working attachment links. The two attachments which were deleted were replaced with updated attachments with the RFI post having a total of three working attachment links.

1. FSA Modernization RFI_072125.docx

2. Attachment 1_Context Diagrams.zip

3. Attachment 2_FSA Modernization RFI QA_072125.docx

12 RFI Page 18, Page Limit reference, will the Government allow a Table of Contents, Table of Figures, and Glossary of Abbreviations and Acronyms that do not count against the page limit?

All content—including front matter—counts toward the page limit.

13 RFI Page 18, Page Limit reference, in graphics, will the Government allow font smaller than 11 point?

You can use fonts smaller than 11 points in graphics, but only if:

The text is clearly legible at 100% zoom or print scale.

The graphic is not the primary method of conveying critical information. You ensure accessibility standards are still met (e.g., contrast, clarity, screen reader compatibility).

14 RFI Page 18, Page Limit reference, for graphics, will the

Government allow 11x17 pages that count as two pages against the page limit?

(1) The Table of Contents, Table of Figures, and Glossary of Abbreviations are included in the total page limit.

(2) The required font size is listed in the RFI and a smaller font size may not be reviewed.

(3) The required page size is 8.5 X 11. 11X17 size pages may not be reviewed.

15 Summary level question reference, the context for this RFI is the One Big Beautiful Bill and is focused on technology.

Please describe the business drivers that are impacting or prompting the scope of this RFI.

The One Big Beautiful Bill represents a once-in-a-generation opportunity to transform how Federal Student Aid (FSA) delivers services to millions of students, parents, borrowers, schools, and partners. These legislative changes combined with shifts in customer expectations, the pace of technology evolution, and the imperative to safeguard high-value federal data are driving FSA to reimagine the entire student aid technology ecosystem. Today, the student aid lifecycle spans multiple, interconnected processes (i.e., from application, eligibility determination, disbursement, servicing, repayment, and oversight) supported by a mix of legacy platforms, modernized components, and partner-operated systems. While this environment has enabled decades of delivery, it also introduces friction points that constrain agility and customer experience:

• Fragmented data and siloed systems that limit real-time visibility into a student’s journey, create reconciliation burdens, and slow policy or operational changes.

• Multiple vendor platforms and disparate integration patterns that increase complexity, cost, and risk when introducing new functionality or responding to legislative mandates.

• Inconsistent user experiences across borrower, partner, and internal interfaces that make navigating aid programs more difficult for customers and harder to manage for institutions.

• Stability and scalability challenges during peak seasons (e.g., FAFSA launch, repayment restarts) where performance and uptime are mission-critical.

• Heightened cybersecurity demands for protecting Personally Identifiable Information (PII) and Federal Tax Information (FTI) in an environment targeted by increasingly sophisticated threats.

16 Summary level question reference, please describe the schedule goals (priorities from the Administration, procurement timelines, etc.) that FSA may be considering for future implementation.

FSA will be leveraging the responses received from this RFI to help shape our procurement strategy and timeline. Your input is crucial in ensuring that we develop a comprehensive and effective approach to modernizing our technology and service delivery. We encourage vendors to provide comprehensive roadmaps or timelines that outline the steps required to implement their recommended solutions. Your detailed input will be instrumental in helping us develop a robust and efficient procurement strategy.

17 Summary level question reference, please describe any specific technology modernization strategies and requirements and how these items relate to the TIVOD modernization efforts.

Key modernization needs include migrating from batch-based integrations to near real-time data flows, implementing a single source of truth for Title IV transactions, consolidating or simplifying vendor-managed platforms, improving system scalability during peak periods, and embedding robust cybersecurity and privacy controls. This RFI is an open invitation for industry partners to bring forward innovative architectures, integration patterns, and delivery approaches (e.g., greenfield, incremental, or hybrid) that can address these requirements while maintaining business continuity. By co-creating with FSA, respondents can help design a future state that is agile, resilient, secure, and capable of delivering an integrated, high-quality experience for all stakeholders.

18 "Industry Day: FSA plans to host a virtual Industry Day the week of Aug. 18, 2025; additional details will be provided at least five business days before this event.”

If no such details have been released yet, can you confirm where they will be shared?

Information on Industry Day, the link for the registration, and responses to the questions is included in Amendment #1 of the RFI.

19 I'd like to register our company for the industry day for the RFI: Federal Student Aid (FSA) Technology Modernization Initiative Would you please provide me with the details and how to register for it?

Information on Industry Day, the link for the registration, and responses to the questions is included in Amendment #1 of the RFI.

20 Please confirm that in addition to a cover page, a reasonable number of non-content supporting pages such as a table of contents, abbreviation and acronym descriptions, graphic/image index etc. do not count against the 40 page limit.

All content—including front matter—counts toward the page limit.

21 What is your expected timeline for publishing questions and answers?

Information on Industry Day, the link for the registration, and responses to the questions is included in Amendment #1 of the RFI.

22 Does the government intend to award both large business and small business tracks under this IDIQ? As a small business with capabilities across multiple categories, we would welcome the opportunity to contribute meaningfully. However, opportunities limited to the large business track may be challenging due to past performance requirements. Offering both tracks would promote broader participation and ensure that qualified small businesses can support FSA’s mission effectively.

The acquisition strategy has not been determined the input from this RFI will assist with formulating the strategy. Our goal is to provide opportunities for all socio-economic categories where applicable.

23 What type of organizational conflict of interest issues does

FSA anticipate could arise from current FSA contracts? If any potential OCI concerns are identified, would FSA consider mitigation plans acceptable?

The acquisition strategy has not been determined for this procurement, but our goal is to provide opportunities for all socio-economic categories.

24 In light of the newly released FAR order, Class Deviation RFO-2025-36, issued by the General Services Administration in support of Executive Order 14275 (Restoring Common Sense to Federal Procurement), does FSA consider this mission-critical work best suited for agency-specific contracting, or does it foresee releasing it through boarder government-wide acquisition vehicles, such as GSA MAS or GSA HACS SIN?

At the time of this RFI, the majority of the scope of the modernization effort is deemed mission critical and will not transfer to GSA.

25 Section 2.0 – Background, RFI page 3, Is FSA open to leveraging commercial loan servicing or financial systems as part of the modernization effort, or does the agency expect all platforms to be purpose-built for the federal environment?

FSA is open to considering commercial loan servicing or financial systems as part of the modernization effort, provided they can be adapted to meet the agency’s unique policy, compliance, security, and performance requirements. Leveraging proven commercial capabilities may accelerate delivery, reduce costs, and benefit from industry best practices, while purpose-built solutions may offer greater alignment to the federal student aid mission and regulatory environment. The optimal path may involve a hybrid approach of integrating commercial components where feasible and custom-building only where necessary to address mission-critical needs. Through this RFI, FSA seeks industry insight on the trade-offs, integration considerations, and innovation opportunities that could allow commercial solutions to coexist with or replace certain federal platforms, ensuring modernization efforts are both mission-driven and cost-effective.

26 Streamline and integrate data infrastructure / Multiple Vendor Platforms, RFI pages 3 and 6, What specific data governance standards are envisioned across vendor-partners?

FSA envisions a uniform, enterprise-wide data governance framework that applies consistently across all vendor-partners, ensuring data quality, security, and interoperability throughout the student aid ecosystem. This would likely include standardized data definitions and taxonomies, common exchange formats, master data management practices for core entities (e.g., students, loans, institutions), and clear rules for data stewardship, lineage, and retention. Vendors would be expected to align to shared integration patterns such as API-first architectures and real-time data exchange protocols, and to comply with established validation, reconciliation, and audit requirements. The goal is to create a “single source of truth” environment where all partners contribute to and consume consistent, accurate data in a secure and compliant manner. Through this RFI, FSA seeks industry recommendations on proven governance models, enforcement mechanisms, and toolsets that can operate effectively in a multi-vendor, hybrid-cloud environment.

27 Enhance customer—including students, parents, borrowers, and partner institutions—experience and self-service, Real-Time and Proactive Analytics, RFI pages 4 and 14, How does Federal Student Aid anticipate leveraging artificial intelligence to personalize communications for different user segments?

FSA is exploring the use of artificial intelligence (AI) to help transform how we engage with and support our customers—students, parents, borrowers, and partner institutions. As outlined in our Request for Information (RFI), one of our strategic priorities is to create a seamless, personalized, and proactive digital experience centered on the needs of our users.

To that end, we envision leveraging AI technologies to deliver tailored communications and support experiences based on individual user profiles, behavior, and context. This could include and not limited to:

• Recommend relevant next steps in a student’s financial aid journey, such as reminders to complete FAFSA corrections, submit required documentation, or enroll in a repayment plan.

• Personalize content and language based on the user’s circumstances (e.g., first-time FAFSA applicant vs. borrower in repayment) and preferred communication channels (e.g., email, SMS, chatbot).

• Improve accessibility and user satisfaction by enabling conversational AI and natural language processing to power intelligent virtual assistants that can guide users through complex processes in real time.

• Detect and respond proactively to potential issues or risks—such as identifying borrowers who may be at risk of delinquency and offering early outreach or relevant resources.

FSA is particularly interested in AI capabilities that can enhance self-service, reduce information gaps, and improve outcomes for all while maintaining rigorous standards for data privacy, and responsible AI in automated decision-making. We welcome input from industry partners on best practices, proven tools, and innovative approaches to responsibly implement AI-driven personalization within a secure and compliant federal environment.

28 Enhance customer—including students, parents, borrowers, and partner institutions—experience and self-service, Real-Time and Proactive Analytics, RFI pages 4 and 14, What are the highest-priority predictive modeling use cases for enabling proactive analysis and engagement?

The highest priority FSA use cases are prediction of delinquency and prediction of default, in order to target effective proactive messaging and interventions for borrowers.

Secondary use cases include flow abandonment interventions, increased personalization support for CSR interactions, and increased personalization of self-service advice tools such as Loan Simulator.

29 Improve System reliability and stability, RFI page 4, Has the agency identified use cases for the use of AI to streamline internal processes and automate repetitive tasks associated with student loan processing, servicing, and data management? If so, can these be shared?

FSA recognizes the transformative potential of AI to improve efficiency, reduce error rates, and enhance the stability of our systems by automating manual or repetitive functions across the student aid lifecycle. While the RFI is part of our early market research, we have begun identifying several preliminary use cases where AI could meaningfully contribute to our modernization goals.

These potential use cases include and not limited to:

• Automated document classification and data extraction: AI could be used to read and extract key information from uploaded documents (e.g., income verification, identity documents) to reduce manual review burden and speed up processing times.

• Smart triage and ticketing: For customer service operations, AI can help categorize and route inquiries—whether received via phone, email, or chat—based on content and urgency, improving resolution times and consistency.

• Data quality monitoring and anomaly detection: AI tools could help identify inconsistencies or outliers in financial aid records (e.g., duplicate borrower IDs, unusual disbursement patterns) to support data integrity and compliance.

• Loan servicing workflow automation: Opportunities exist to automate routine servicing actions such as payment posting, status updates, and eligibility determinations (e.g., for income-driven repayment plans), which today require coordination across multiple systems and manual input.

• Predictive risk scoring: AI could be applied to anticipate borrower outcomes—such as risk of default or missed deadlines—and trigger early outreach or tailored interventions to support those most in need.

While these examples represent early thinking, FSA is seeking industry input to further refine, prioritize, and operationalize AI-enabled use cases. We welcome recommendations on tools, frameworks, and governance approaches that can help us responsibly implement AI to support reliable, efficient, and responsible AI delivery of federal student aid services.

30 Data Silos and Fragmentation, RFI page 5, What specific data domains are currently not covered by the EDMAPS platform?

EDMAPS does not yet cover all data included in other FSA IT systems used in administering Title IV. For existing data pipelines from upstream systems into EDMAPS, those pipelines may need to be expanded or otherwise adjusted to cover the full scope of the upstream system. Additional information will be provided at the time of solicitation.

31 Partner Integration Challenges, RFI page 6, How can AI assist smaller institutions with legacy SIS integration challenges?

FSA understands that smaller institutions—particularly those with limited technical staff or outdated Student Information Systems (SIS)—face significant challenges in integrating with modern federal platforms. As part of our broader modernization efforts, we are exploring how artificial intelligence (AI) can help reduce this burden and improve the partner experience across institutions of all sizes.

We believe AI has strong potential to support smaller institutions in the following ways and not limited to:

• Data mapping and transformation assistance: AI tools can analyze legacy SIS data structures and help automatically map fields to FSA’s standard data formats. This can significantly reduce the time and technical effort needed to align with integration requirements.

• Intelligent data validation and error detection: AI models can assist institutions by flagging formatting issues or missing data during file submissions and suggesting corrective actions in real time—minimizing back-and-forth troubleshooting.

• Low-code/no-code automation tools: AI-infused platforms may allow institutions to configure integrations through visual interfaces, minimizing the need for deep programming knowledge.

• Legacy system abstraction: AI-based middleware tools could help translate between older SIS data formats and modern APIs—essentially acting as a smart "bridge" to facilitate secure and reliable data exchanges without requiring full system overhauls. FSA welcomes vendor ideas on how AI and related technologies can improve integration experiences for smaller institutions. We are also interested in solutions that are scalable, cost-effective, and support technical capabilities across our diverse institutional partners.

32 Section 4.2.3 – Data, RFI page 7, Does FSA have a preferred approach for future-state data architecture (e.g., data lake, data mesh, or data fabric), or is the agency seeking vendor recommendations on the best model to unify data across systems?

FSA is seeking vendor recommendations on the most effective model to unify data across systems while meeting performance, compliance, and scalability requirements. The chosen approach should enable secure, real-time data sharing, support advanced analytics, and ensure data quality and governance across a complex, multi-vendor ecosystem. FSA is particularly interested in architectures that can integrate legacy systems with modern platforms, accommodate both structured and unstructured data, and evolve over time without disrupting core operations. Through this RFI, FSA invites industry partners to suggest and justify their recommended models, including examples from large-scale, high-security environments where similar unification goals were successfully achieved.

33 Section 4.2.5 – Hosting, RFI page 8, FSA references hybrid, multi-cloud, and colocation-based models as potential hosting strategies. Are there specific workloads or functional domains FSA anticipates will need to remain on-premises or in co-lo environments due to performance, compliance, or integration constraints?

FSA anticipates that certain, still under analysis, workloads and functional domains may need to remain in on-premises or colocation (co-lo) environments across the Enterprise in the near term due to performance sensitivity, compliance requirements, contractual requirements, or integration dependencies with legacy systems however the goal is to fully adopt a cloud based model.

FSA’s strategy is to resolve these dependencies through phased modernization and integration, ultimately enabling a unified, secure, multi-tenant cloud environment.

34 Section 4.2.4 – Security, RFI page 8, Does FSA see the development and implementation of a Zero Trust Strategy/Architecture as critical to their Modernization Initiatives (even if in a phased and road mapped approach)?

Yes, the development and implementation of a Zero Trust Architecture (ZTA) is not only critical but foundational to FSA’s modernization initiatives. In alignment with Executive Order 14028 and OMB Memorandum M-22-09, FSA—working in close coordination with the Department of Education’s Office of the Chief Information Officer (OCIO)—has committed to a strategic rollout of Zero Trust capabilities.

35 Section 4.2.4 – Security, RFI page 8, How is FSA prioritizing their Zero Trust Strategy/Architecture considering that most of the Modernization Initiatives will be heavily dependent on a strongly documented, tagged, classified, suitably tooled, and highly automated basis to allow for making rapid decisions at scale?

FSA views its modernization strategy as a key enabler of its Zero Trust Architecture (ZTA) efforts. In alignment with Executive Order 14028 and OMB Memorandum M-22-09, FSA is implementing Zero Trust in a phased, strategic manner that supports automation, data tagging, classification, and real-time decision-making. These capabilities are essential to securing the agency’s systems and data while enabling the agility and scalability required for modernization.

36 Section 4.2.4 – Security, RFI page 8, Has FSA considered an approach where you incrementally "strangle" legacy systems by building secure, ZT-native microservices around them, then connecting these new services to the ZT control planes?

FSA has considered and is actively exploring architectural patterns that support incremental modernization of legacy systems, including the use of secure, Zero Trust-native microservices. This approach is consistent with the agency’s broader modernization strategy, which emphasizes agility, modularity, and security-by-design. As outlined in the July 2025 RFI, FSA is modernizing its enterprise cloud hosting, applications, and data integration platforms to support unified, customer-centric services that are secure and scalable. The “strangler pattern” — where legacy systems are gradually surrounded and replaced by modern services — aligns with this strategy, particularly in environments where full system replacement is not immediately feasible.

37 Security, Architectural Approach, Data Privacy, RFI pages 8 and 10-16, How comprehensive is FSA's current inventory and classification of sensitive data?environments?

As part of its modernization roadmap, FSA is working to standardize data classification and tagging practices across legacy and cloud-hosted systems. This includes integrating data governance into system development lifecycles and leveraging automation to support real-time data protection and access control. The goal is to ensure that sensitive data, especially personal identifiable information (PII) and financial records—is consistently identified, labeled, and protected in accordance with federal and Department of Education policies.

38 Security, Data Infrastructure, Data Privacy, RFI pages 8 and 10-16, Are there established standards or pain points related to enforcing consistent data tagging and access controls across core, ancillary, and partner environments?

Yes, FSA has encountered notable pain-points in enforcing consistent data tagging and access controls across its diverse environments. These challenges stem from the complexity of managing legacy systems alongside modern cloud-based platforms, as well as the need to coordinate with multiple external partners and contractors.

To address these issues, FSA is actively working to standardize data governance practices as part of its broader modernization strategy.

39 Security, Secure Integration/Migration Strategies, Data Privacy, pages RFI 8 and 10-16, For phased migration from legacy to modernized systems, are there specific coexistence constraints—or critical systems that have low tolerance for downtime—that vendors should prioritize for Zero Trust “wrapping” or micro-segmentation?

Yes, FSA operates a diverse portfolio of systems, including several designated as High Value Assets (HVAs), which are critical to mission delivery and have low tolerance for downtime.

These systems often support core financial aid processing, identity management, and compliance reporting functions. As such, FSA recognizes the need for secure coexistence strategies during phased modernization.

40 Security, Secure Integration/Migration Strategies, Data

Privacy, RFI pages 8 and 10-16, What are the greatest organizational or cultural challenges you anticipate in transitioning to a Zero Trust-operating environment? Are there existing approaches (training, communication, executive sponsorship) that have proven effective, or gaps where additional vendor support is needed?

The transition to a Zero Trust Architecture (ZTA) introduces significant shifts in how people, processes, and tools interact. One of the greatest organizational challenges is adapting to the principle of least privilege and strict Role-Based Access Control (RBAC), which can disrupt long-standing workflows and access expectations. Implementing micro-segmentation, pervasive encryption, and continuous authentication also require cultural change, particularly in environments where implicit trust and broad access were previously the norm. These changes can create friction among users and administrators alike, especially when legacy systems are involved or when operational continuity is at stake.

To address these challenges, FSA has leveraged executive sponsorship, targeted training, and cross-functional communication strategies to build awareness and buy-in.

41 Section 4.2.6 – Artificial Intelligence, RFI page 9, What will FSA provide for foundational AI / ML models to be supported, or should industry propose, develop, and/or host models as part of the solution?

FSA is currently in the exploratory phase of defining how AI and machine learning (ML) will support our modernization goals. At this time, FSA is not prescribing a specific set of foundational models or platforms, and we are highly interested in hearing from industry about the best approaches—whether those involve using existing commercial models, developing custom models tailored to FSA's needs, or a combination of both.

Vendors are encouraged to:

• Suggest appropriate foundational models (e.g., large language models, predictive ML models) that can meet the use cases described in the RFI, such as enhancing personalization, streamlining internal workflows, or improving data insights.

• Suggest hosting strategies, including whether models should reside within FSA’s secure government-authorized environments or within vendor-managed, FedRAMP-authorized platforms.

• Explain the trade-offs between using open-source, commercial, or proprietary models, particularly in areas such as scalability, maintainability, transparency, and cost.

• Outline training and tuning requirements, including whether models can be fine-tuned on FSA data (while maintaining compliance with federal data privacy and security regulations), or if pre-trained models are sufficient.

• Describe model governance practices, including how the suggested approach ensures ethical AI use, bias mitigation, transparency, version control, and continuous learning.

FSA is open to innovative architectures that align with our goals for security, efficiency, and responsible AI. As the RFI is part of our market research and planning, we look forward to reviewing thoughtful responses that explore the most suitable technical path forward for AI/ML support in the federal student aid ecosystem.

42 Section 4.2.6 – Artificial Intelligence, RFI page 9, What specific AI and analytics use cases are highest priority for FSA currently (e.g., fraud detection, borrower behavior prediction, service automation)?

FSA views artificial intelligence (AI) and advanced analytics as essential enablers of our broader modernization goals—including improving operational efficiency, enhancing the customer experience, and strengthening oversight. While the RFI is part of our early market research, we have begun to identify several high-priority AI and analytics use cases that align with FSA’s mission and strategic direction.

Some of the areas currently under active consideration and not limited to include:

• Fraud detection and risk mitigation: FSA manages substantial volumes of sensitive financial data, making fraud prevention a top priority. AI models could help identify suspicious patterns in application or disbursement data (e.g., detecting anomalies in FAFSA submissions or flagging unusual borrower behavior) to proactively prevent improper payments or identity fraud.

• Borrower behavior prediction: FSA is interested in predictive analytics models that can help identify which borrowers may be at risk of delinquency or default. These insights can be used to trigger timely interventions, outreach campaigns, or personalized repayment guidance—ultimately supporting better long-term outcomes for borrowers.

• Service automation and virtual assistance: AI-driven chatbots, intelligent document processing, and workflow automation tools are being explored to improve efficiency across high-volume functions such as customer service, document intake, eligibility determinations, and back-office processes.

• Operational analytics and system monitoring: AI can assist in monitoring system health, flagging anomalies (e.g., spikes in error rates or system latency), and enabling real-time reporting to support business continuity and service quality.

• Personalized engagement and communications: Using AI to tailor communications, recommend next steps, and adapt digital experiences based on a user’s journey (e.g., first-time FAFSA filer vs. borrower in repayment) remains a long-term goal to improve satisfaction and reduce friction.

FSA is eager to learn from industry about innovative use cases, tools, and governance practices that can help responsibly scale these capabilities within a secure and compliant federal ecosystem. While these priorities are not exhaustive or finalized, they represent areas of strong interest where AI and analytics can deliver measurable impact.

43 Overall Modernization Strategy and Architecture, RFI page

10, AI Code Generation can analyze legacy code - whether written in C++, COBOL, or other outdated languages - and propose refractored code to align with modern development practices and frameworks. Has FSA explored AI Code Generation? If yes, how has it been used? If no, is FSA interested in AI Code Generation?

FSA is monitoring the emerging capabilities of AI-assisted code generation, including tools that can analyze legacy codebases and recommend modernized equivalents using current programming languages, architectures, and design patterns.

At this time, FSA has not yet formally adopted AI code generation tools as part of its development or modernization processes. However, we recognize the potential value these technologies could bring—particularly in accelerating the refactoring of legacy systems, improving code consistency, and reducing the manual burden of re-engineering efforts across older platforms such as COBOL, C++, or other custom-built environments.

As part of our ongoing technology modernization initiative, FSA is very interested in understanding how AI-assisted code generation could support efforts such as:

• Assessing and translating legacy application logic into cloud-native microservices

• Modernizing monolithic systems into modular, API-driven components.

• Identifying technical debt and opportunities to standardize or simplify codebases

• Supporting documentation, code reviews, and reverse-engineering where source materials are limited We encourage vendors to share real-world examples, tool recommendations, and practical considerations for safely and effectively deploying AI code generation—especially in complex, high-stakes environments like FSA’s, where security, compliance, and accuracy are paramount. As we evaluate modernization strategies, FSA is open to piloting AI-enabled development approaches where they can demonstrably improve quality, speed, and maintainability—while ensuring robust testing, human oversight, and alignment with federal development standards.

44 Data Architecture and Integration Plan, RFI page 11, What are the primary obstacles to eliminating overnight batch file transfers currently?

Currently, core enterprise systems rely on tightly coordinated overnight batch processes that use static file generation and scheduled delivery. This legacy architecture presents several challenges to transition toward real-time or near-real-time data exchange:

1. Dependence on Existing Batch-Oriented Workflows – Many interconnected systems are built around nightly cycles, with downstream processes and reporting dependent on the availability of complete, end-of-day datasets.

2. Lack of Modern API Infrastructure – The absence of standardized, enterprise-wide API capabilities means each system would require significant redevelopment to generate, deliver, and consume data in a real-time framework.

3. Integration Complexity Across Multiple Platforms – The current environment includes heterogeneous technologies and vendors, requiring coordinated upgrades to ensure compatibility and consistent data handling.

4. Resource and Bandwidth Constraints – While some initiatives toward real-time integration have begun, progress has been limited by network throughput, processing capacity, and competing organizational priorities.

5. Change Management and Operational Risk – Moving away from batch processing would require not only technical changes but also updates to business processes, testing protocols, and operational monitoring to ensure accuracy and reliability in a continuous data flow model.

In summary, the combination of architectural limitations, infrastructure gaps, and competing priorities continues to make overnight batch processing the most reliable mechanism for coordinated data transfer at this time.

45 Master Data Management (MDM) and Data Quality, RFI page 11, What are the key data entities beyond persons, loans, and schools for MDM?

In addition to persons, loans, and schools, key entities for MDM may include but are not limited to:

• Customer interactions and support cases

• Instructional Programs

• Servicers and third-party contractors

• Institutions and their ownership structures

• The RFI emphasizes the need for a unified data model that reconciles duplicates and inconsistencies across domains.

Additional data entities and maps will be provided at the time of solicitation

46 Enhancing Customer Experience and Self-Service Capabilities, page 13, What federal security requirements (e.g., FISMA, FedRAMP, NIST 800-53, Section 508) must the unified portal meet to ensure compliance with data protection, access control, and accessibility?

The unified borrower portal as accessed by end-users through studentaid.gov will have existing 508 and security requirements without additional burden on servicer requirements. Modern security requirements include those outlined by Executive Order 14028, Improving the Nation's Cybersecurity, and executive Order 14058, Transforming Federal Customer Experience and Service Delivery to Rebuild Trust in Government, and may be met either through the retirement of servicer sites or the integration of SSO and thus MFA, and the retirement of independent servicer login methods.

47 Enhancing Customer Experience and Self-Service

Capabilities, RFI page 13, How does FSA plan to structure the ongoing ownership, resource management, and governance necessary for the unified portal after go-live, including continuous support, iterative feature enhancement, security/compliance maintenance (such as agile or software factory practices), and active portfolio management, to ensure the portal remains responsive to evolving user needs, technical debt, stakeholder input, and regulatory or business changes throughout its lifecycle?

FSA acknowledges the importance of establishing a solid ownership structure, effective resource management, and a robust governance framework. We invite you to provide comprehensive and detailed responses addressing these areas in relation to your recommended solution. Your insights will be crucial in helping us formulate an efficient and effective strategy for the ongoing management of the unified portal.

48 Enhancing Customer Experience and Self-Service Capabilities, RFI page 13, Please describe your capabilities in providing AI-driven services—such as proactive communication, help desk augmentation, and form assistance via conversational digital assistants.

FSA’s supports a vision to create a more intuitive and supportive digital experience for our users. As outlined in the RFI, enhancing self-service and customer engagement through the use of AI is a key strategic priority for FSA.

Some of the areas currently under active consideration and not limited to include:

• Proactive communication: AI tools that can anticipate user needs and deliver timely, relevant messages (e.g., reminders about approaching deadlines, alerts for missing documentation, or guidance based on borrower behavior or lifecycle stage) to reduce confusion and support task completion.

• Contact Center augmentation: Solutions that can empower human support agents with intelligent summaries, context-aware suggestions, and unified user histories— improving resolution speed, consistency, and customer satisfaction. We are also interested in tools that triage tickets, categorize issues, or route inquiries more effectively using natural language processing (NLP).

• Conversational digital assistants: AI-powered chatbots that can assist students, parents, borrowers, and partners in real time as they complete forms, navigate aid options, or seek information. These tools should be intuitive, compliant with accessibility standards, and capable of seamlessly escalating to live support when needed.

While FSA has deployed a virtual assistant and piloting an agentic Interactive Voice Response (IVR), we are actively exploring next-generation AI assistants that go beyond FAQs to provide contextual, personalized, and guided support—whether a student is completing the FAFSA, exploring repayment options, managing aid eligibility, or supporting our partners with day to day operations.

We welcome ideas from vendors that demonstrate proven experience delivering these types of AI services at scale in complex, high-stakes environments. We are interested in how these solutions can be responsibly implemented within a secure federal ecosystem, while preserving user trust, and privacy.

49 Enhancing Customer Experience and Self-Service

Capabilities, RFI page 13, What are the essential requirements for implementing contextual chatbot or live chat solutions in this environment?

FSA welcomes vendor ideas, suggestions, and recommendations for chatbot and live chat solutions that can enhance self-service and user support. We are especially interested in approaches that build upon, complement, or evolve our existing capabilities, including the Aidan chatbot, which currently uses Microsoft Azure AI providing responses to user inquiries on StudentAid.gov.

Any contextual chatbot or live chat solution in our environment should meet several essential requirements and not limited to:

Security and privacy compliance: Full adherence to FISMA, the Privacy Act, and FedRAMP standards. Sensitive data must be protected at all times.

• Context awareness: Ability to maintain and respond to user context within a session, enabling more personalized and relevant interactions.

System integration: Capability to securely integrate with FSA platforms for real-time data access when authorized.

• Seamless escalation: Smooth handoff from chatbot to live agents when needed, with full session history retained to avoid repeat explanations.

• Accessibility: Compliance with Section 508 and WCAG standards Scalability and performance: Ability to support millions of users and remain reliable during peak usage periods (e.g., FAFSA season).

• Governance and transparency: Robust monitoring, audit logging, responsible AI guidelines, and expandable AI models to ensure the best possible responses at the most efficient cost.

We encourage vendors to share innovative solutions while delivering intuitive, user-centered experiences. FSA is committed to continuously improving how we serve students, parents, borrowers, and partners through secure and modern AI solutions.

50 Enhancing Customer Experience and Self-Service

Capabilities, RFI page 13, To what extent do you prioritize integrating advanced AI tools (e.g., chatbots, virtual assistants, predictive analytics) within a unified portal to improve customer support and enable more informed decision-making?

FSA places a high priority on integrating advanced AI tools—such as chatbots predictive analytics, etc.—within a unified digital portal to enhance customer service and empower users with timely, personalized, and informed support.

As outlined in the RFI, one of our core modernization goals is to deliver a seamless, one-stop digital experience for students, parents, borrowers, and partners. This includes consolidating functions currently spread across multiple systems into a single, intuitive platform—centered on StudentAid.gov for students, parents, and borrowers. Within that unified environment, we view AI as a key enabler of smarter, more responsive service delivery.

Some of the areas currently under active consideration and not limited to include:

• Embedding Conversational digital assistants —such as chatbots that can help users navigate financial aid processes, complete forms, troubleshoot issues, and access personalized guidance without needing to call or wait in queues.

• Applying predictive analytics to surface relevant alerts, recommendations, or reminders based on user behavior, life stage, or risk indicators (e.g., a student missing a FAFSA deadline or a borrower at risk of delinquency).

• Enabling data-driven decision-making by integrating analytics dashboards and self-service insights for both internal users (e.g., program managers, policy teams) and external partners (e.g., schools and institutions).

FSA believes these capabilities can dramatically improve both customer satisfaction and operational efficiency, while also advancing trust by ensuring users receive the right information at the right time. While FSA has deployed a virtual assistant, we are actively exploring next-generation AI assistants that go beyond FAQs to provide contextual, personalized, and guided support—whether a student is completing the FAFSA, exploring repayment options, managing aid eligibility, or supporting our partners with day-to-day operations. As such, we encourage vendors to recommend innovative ways to integrate AI tools into a unified, secure, and accessible portal—designed around the needs of our diverse user base.

51 Real-Time and Proactive Analytics, RFI page 14, What specific data would be available to build models predicting borrower default risk?

To build predictive models for borrower default risk, any data that resides in or can be integrated into the EDMAPS system can be available. This includes data from across the student aid lifecycle from FSA ID account creation, FAFSA submission, originations and disbursements, service and collections.

52 Improved Oversight and Policy Outcomes, RFI page 15, What are the top data-related challenges in overseeing contractor performance today? 29. Improved Oversight and Policy Outcomes, RFI page 16, Can you specify the "goals" the agency has in mind for the use of novel analytics capabilities (e.g., natural language query of data or artificial intelligence-driven insights) to be applied? 30.

RFI Response Guidelines Page Limit, RFI page 18, Are the cover letter and Table of Contents also excluded from the 40-page limit?

FSA’s oversight of contractor performance is challenged by fragmented and inconsistent data across multiple vendor-operated systems, limited real-time visibility into key metrics, and the need to reconcile information from disparate sources before it can be acted upon.

Data latency, gaps in integration, and the absence of unified dashboards make it harder to detect issues early, measure compliance consistently, and link operational outcomes to contractual obligations. FSA’s goal for novel analytics capabilities is to enable faster, deeper, and more intuitive insights (e.g., allowing authorized staff to use natural language queries to surface contractor performance trends, applying AI to detect anomalies or forecast risks, and empowering leadership with dynamic dashboards that combine operational and compliance data in near real time). These capabilities should support proactive intervention, more effective policy evaluation, and greater transparency with stakeholders. Regarding the RFI’s page limit, both the cover letter and Table of Contents are included in the 40-page limit.

53 Improved Oversight and Policy Outcomes, RFI page 16, Can you specify the "goals" the agency has in mind for the use of novel analytics capabilities (e.g., natural language query of data or artificial intelligence-driven insights) to be applied?

FSA envisions novel analytics capabilities as a means to unlock faster, deeper, and more actionable insights that improve program oversight, policy evaluation, and service delivery.

Goals include enabling authorized users…