Benefit integrity is more than fraud detection. It demands an end-to-end operating model that ensures every decision is grounded in complete, authentic, accurate, timely, and auditable evidence.

1. Is the applicant, beneficiary, dependent, provider, or representative who they claim to be?
2. Are the submitted documents authentic, current, complete, and unaltered?
3. Does the evidence support the eligibility factors required by policy?
4. Do the extracted facts match trusted internal and external data sources?
5. Were exceptions, inconsistencies, and risk indicators routed to the right reviewer?
6. Can the agency prove, during an audit or appeal, exactly what evidence was used and how the determination was reached?

CMS program integrity priorities include combating fraud, waste, and abuse, supporting state program integrity efforts, and eliminating or recovering improper payments. Medicaid program integrity also emphasizes stronger data and robust analytic tools. Infocap’s Benefit Integrity model operationalizes those goals by embedding intelligent document automation, AI-driven verification, and governed workflow directly into the eligibility and case management lifecycle.

The shift from OCR-only and key-value-pair mapping toward visual context, prompt-based extraction, model routing, generative classification, confidence scoring, and governed workflow automation is central to the modern Benefit Integrity model.

Infocap delivers Benefit Integrity as an intelligent verification and compliance layer that complements existing eligibility, enrollment, MMIS, claims, CMS, case management, content management, and data warehouse platforms. It is not designed to replace core systems. It is designed to make those systems more accurate, more auditable, and more efficient.

Infocap’s approach brings together four essential layers:

1. Orchestration Layer: Tungsten Automation TotalAgility manages intake, classification, routing, validation, exception handling, human review, work queues, audit trails, and downstream system integration.
2. AI Intelligence Layer: Amazon Textract, Comprehend, Rekognition, Bedrock, and Bedrock Data Automation extract, classify, enrich, analyze, and verify data from structured, semi-structured, and unstructured content.
3. Policy and Rules Layer: Eligibility rules, state-specific policies, redetermination logic, document requirements, and risk thresholds are configured so evidence is evaluated consistently across every case.
4. Human Assurance Layer: Casework and program integrity staff see prioritized exceptions, source-linked evidence, confidence indicators, and case summaries – not pages of raw documentation.

The result is a practical Benefit Integrity operating model: digital workers perform the repetitive evidence-processing work at scale, while human workers focus on judgment, exception resolution, policy interpretation, and beneficiary service.

A complete Benefit Integrity program must verify more than data. It must verify trust.

Bad actors increasingly use altered photos, modified metadata, fake holograms, changed expiration dates, high-quality reproductions, and synthetic documents. Manual review alone cannot consistently detect these issues, especially at government scale.

Infocap’s TRUST offering – Tamper Recognition Using Smart Technology – is described as  an AI/ML-powered fraudulent document detection platform for government and military healthcare environments. It supports real-time document authentication, tamper detection, multi-layer identity and document verification, compliance reporting, audit trails, and integration into claims, enrollment, and eligibility systems.

TRUST combines document analysis, Amazon Textract, Amazon Rekognition, hologram and watermark detection, metadata and tamper pattern analysis, cross-checking, and workflow orchestration.

Within Benefit Integrity, this capability allows agencies to move from “extracting what the document says” to “assessing whether the document and its data can be trusted.” That distinction is critical. A forged paystub can still contain perfectly readable text. A modified ID can still pass basic OCR. A doctored notice can still produce valid-looking data. Benefit Integrity requires authenticity checks, not just extraction.

Omnichannel Document Processing

Infocap supports paper, electronic, scanned, mobile, email, PDF, image, form, table, handwriting, and mixed-content documents. This is essential because benefit programs cannot control how every beneficiary, provider, employer, dependent, or representative submits evidence.

Generative Classification and Document Splitting

Infocap can process large combined submissions and automatically identify document boundaries, separate evidence types, and route each document to the proper extraction and validation workflow. This reduces manual indexing and prepares each document for the most appropriate AI model or blueprint.

Context-Based Extraction Instead of Template Dependency

Traditional document automation often required predefined templates, key-value permutations, and custom scripts. Infocap’s current model uses Textract Queries, BDA blueprints, prompt-based extraction, layout understanding, visual context, and natural language instructions to extract data even when formats change.

Multi-Model Evidence Processing

No single model performs best on every document type. A paystub, handwritten form, ID card, bank statement, correspondence letter, spreadsheet, and scanned notice may each require a different approach. Infocap’s orchestration model allows different AWS services and extraction profiles to be invoked based on the document type, evidence need, risk profile, and confidence target.

AI Voting and Cross-Validation

For high-risk fields, Infocap can compare outputs from multiple extraction methods. For example, a pay amount may be extracted using Textract, validated through BDA context extraction, normalized with Comprehend, and compared against agency rules or external data. Where outputs agree, confidence increases. Where they disagree, the case is routed for review.

Tamper and Fraud Signal Detection

Through TRUST and AWS visual AI services, Infocap can examine whether a document appears authentic, complete, and unaltered. This includes detecting suspicious modifications, inconsistent metadata, altered photos, fake or missing visual security features, mismatched identities, and other indicators that should be reviewed before eligibility is granted or renewed.

Caseworker AI Assistance

Infocap can extend document automation with caseworker-facing AI assistance. Authorized users can ask questions such as:

• What evidence supports this income determination?
• Which document contains the applicant’s current address?
• Are there inconsistencies between the application and supporting documents?
• What changed since the last renewal?
• Which documents are missing?
• Summarize the verification issue for supervisor review.
• Show the source page for the extracted employer name.

This transforms AI from a back-end extraction engine into a guided review assistant. The key governance principle remains the same: AI helps reviewers find, summarize, and compare evidence; it does not replace program authority.

Post-Extraction Calculations and Evidence Synthesis

Benefit eligibility often depends on calculations and comparisons, not isolated fields. Infocap can use extracted values to support:

• Gross and net income calculations
• Monthly income averaging
• Household size validation
• Benefit period checks
• Work-hour calculations
• Date range validation
• Duplicate document detection
• Cross-document reconciliation
• Prior-period comparison
• Exception summaries

Audit-Ready Evidence and Reporting

Infocap’s workflow records the evidence used, the source document, extracted values, confidence scores, rules evaluated, reviewer decisions, and downstream system updates. This strengthens oversight and reduces the burden of responding to audits, appeals, quality control reviews, and program integrity inquiries.

Secure, Scalable, AWS-Native Architecture

Infocap’s IDA platform is described as AWS-built, serverless, scalable, API-first, and AI-driven, using AWS services such as S3, Lambda, Fargate, API Gateway, Bedrock, BDA, Textract, and Comprehend. It also describes encryption with AES-256 at rest and TLS/SSL in transit, with FIPS 140-2 validated cryptographic modules. Infocap also notes experience with strict security requirements for DoD, intelligence, federal, and state environments, including NIST 800-171 and FISMA-related environments.

Infocap's Benefit Integrity model is purpose-configured for the specific evidence types, verification requirements, and compliance demands of major government benefit programs.

Benefit eligibility is high-stakes. Incorrect determinations can delay care, disrupt coverage, create financial harm, or expose agencies to improper payments and audit findings. For that reason, Infocap’s Benefit Integrity architecture follows a governed AI model.

The governance model includes:

• Human accountability. AI assists with extraction, classification, summarization, comparison, and routing. Program authority remains with configured rules, policy, and authorized reviewers.

• Explainability. Extracted values are linked back to source documents, page locations, and confidence scores so reviewers can verify evidence.

• Confidence thresholds. Agencies can define which fields and document types require automatic acceptance, secondary validation, or mandatory human review.

• Prompt and blueprint governance. BDA blueprints, extraction prompts, model versions, and validation rules can be versioned, tested, and controlled.

• Model agility. The architecture can adopt improved AWS models or agency-approved services without redesigning the entire business process.

• Exception transparency. Every exception is routed with a reason, such as missing document, expired ID, low confidence, mismatched name, inconsistent income, duplicate submission, or tamper signal.

• Auditability. Every action is logged for oversight, appeals, quality control, and compliance reporting.

• Privacy and security. PII and PHI are protected through encryption, access controls, redaction, secure storage, and role-based workflow design.

This is the difference between AI automation and responsible Benefit Integrity automation. The goal is not to remove humans from government decisions. The goal is to remove avoidable manual burden so humans can focus on the decisions that require judgment.

 

Infocap’s Benefit Integrity solutions can be deployed as a modular modernization layer around existing CMS, MMIS, eligibility, claims, and case management platforms.

A typical deployment includes:

1. Discovery and document inventory: Identify document types, volumes, submission channels, eligibility factors, verification issues, and audit requirements.
2. Workflow design: Configure TotalAgility workflows for intake, classification, extraction, validation, exceptions, and posting.
3. AI service configuration: Define Textract queries, BDA blueprints, Comprehend enrichment, Rekognition checks, and TRUST rules.
4. Rules and policy mapping: Translate program rules into configurable validation logic.
5. Integration: Connect to existing eligibility systems, data sources, repositories, dashboards, FDSH, MMIS, or CMS platforms.
6. Human review design: Create queues, roles, review screens, escalation paths, and supervisor controls.
7. Pilot and ground truth tuning: Validate extraction accuracy, exception rates, false positives, processing time, and reviewer usability.
8. Scale and continuous improvement: Expand document types, refine rules, improve confidence thresholds, and measure outcomes.

Infocap’s MPVaaS positioning describes a complementary layer around existing MMIS and eligibility platforms, using AWS services for secure integrations, document ingestion and extraction, workflow orchestration, event-driven automation, secure storage, and rules execution.

 

Infocap’s Benefit Integrity approach is designed to help agencies achieve measurable outcomes across operational performance, compliance, and beneficiary experience.

• Faster processing: Automated document capture, extraction, classification, and validation reduce the time spent manually reading and indexing evidence.

• Reduced manual workload: Human review is focused on exceptions, discrepancies, and high-risk cases rather than every page of every submission.

• Improved accuracy and consistency: Standardized workflows and configured rules reduce variation across workers, units, regions, and contractors.

• Lower improper payment risk: Required verification steps are captured, documented, and linked to evidence before determinations are finalized.

• Earlier fraud and tamper detection: Suspicious documents and identity signals can be flagged before enrollment, renewal, access, or payment.

• Better audit readiness: Evidence, rules, reviewer actions, and determinations are preserved in a defensible audit trail.

• Improved beneficiary experience: Legitimate applicants and beneficiaries experience faster determinations and fewer unnecessary document resubmissions.

• Scalable modernization: Agencies can improve eligibility verification without waiting for a full core system replacement.

Infocap’s MPVaaS materials describe real-time and batch eligibility verification, automated income and employment checks, state-specific rules, audit documentation, and a design goal of reducing manual processing time by 80–90%.