Welcome to Sentinel

Sentinel protects program integrity by identifying fraudulent submissions while maintaining low false-positive rates for legitimate applicants. Define fraud vectors, run batch analysis, and ensure benefits reach the people who need them.

As generative AI makes fake documents increasingly realistic, benefit programs need sophisticated fraud detection that goes beyond document verification. Sentinel analyzes patterns across 450,000+ applications to identify coordinated fraud attempts, synthetic identities, and suspicious behaviors—without creating barriers for legitimate applicants from marginalized communities.

What Sentinel Does

Sentinel is a fraud analysis platform with four core capabilities:

Batch Upload

Upload submission data from Airtable, Terra, or other sources. Process thousands of applications in a single batch.

Fraud Vectors

Define heuristics and detection rules. Geographic clustering, IP analysis, bank verification, duplicate detection, and more.

Automated Workflows

Turn fraud vectors into automated pipelines that run on every batch. Flag suspicious submissions for human review.

Results Export

Export flagged submissions to Airtable for case management review. Sync fraud findings back to Hub.

Who Uses Sentinel

Primary Users: Fraud Analysis Team

David (CEO): Defines fraud vectors, reviews patterns
Brian (Director of Systems Integration): Builds automated workflows
May (Engineer): Implements detection algorithms

Workflow:

Upload batch of submissions (from Airtable or Terra)
Run fraud vector analysis
Review flagged submissions
Export results to new Airtable base
Case managers review and make final decisions

Fraud Vectors

Sentinel supports multiple types of fraud detection:

Geographic Analysis

Vector	Description	Example
Geo Clustering	Multiple applications from same location	50 applications from single IP address
Address Verification	Address doesn’t match other data	Rental assistance for non-existent address
Distance Analysis	Employer/bank far from residence	Bank in different state than home address
Jurisdiction Mismatch	Applicant outside program area	Seattle program, Portland address

Identity Analysis

Vector	Description	Example
Duplicate Detection	Same person, multiple applications	SSN used across 3 programs
Synthetic Identity	Fabricated identity markers	SSN/DOB combination doesn’t exist
Identity Velocity	New identity, high activity	SSN first seen, 10 applications in 1 week
Name Variations	Suspicious name changes	”John Smith” → “Jon Smyth” → “Jonathan Smith”

Document Analysis

Vector	Description	Example
Template Detection	Documents from same template	20 paystubs with identical formatting
Metadata Analysis	Document creation patterns	PDF created 5 minutes before submission
Inconsistency Flags	Data doesn’t match across documents	Paystub income ≠ tax return income
Known Fraudulent	Previously flagged documents	Document hash matches known fraud

Behavioral Analysis

Vector	Description	Example
Submission Velocity	Rapid-fire applications	10 applications in 5 minutes
Time Patterns	Unusual submission times	All applications at 3am
Device Fingerprint	Same device, multiple identities	One browser, 50 different people
Referral Patterns	Coordinated referral abuse	All applications from same referrer

Financial Analysis

Vector	Description	Example
Bank Verification	Bank account doesn’t verify	Account closed or doesn’t exist
Fraudulent Banks	Known problematic institutions	Banks frequently used in fraud rings
Income Inconsistency	Claimed income vs. verified	Claims $2k/month, deposits show$ 500
Payment Velocity	Multiple payments to same account	5 different applicants, same bank account

Workflow Architecture

Risk Scoring Model

Each submission receives a risk score (0-100) based on weighted fraud vectors:

interface RiskAssessment {
  submissionId: string;
  overallScore: number; // 0-100

  vectorScores: {
    geographic: number;
    identity: number;
    document: number;
    behavioral: number;
    financial: number;
  };

  flags: FraudFlag[];
  confidence: number;
  reviewRequired: boolean;
}

interface FraudFlag {
  vector: string;
  severity: 'low' | 'medium' | 'high' | 'critical';
  description: string;
  evidence: any;
  falsePositiveLikelihood: number;
}

Scoring Thresholds

Score Range	Action	False Positive Target
0-30	Auto-approve (no fraud indicators)	N/A
31-50	Low priority review	<5%
51-70	Standard review required	<10%
71-85	High priority review	<15%
86-100	Critical review + escalation	<20%

Human in the Loop

Sentinel flags submissions for review—it does not auto-deny. Every high-stakes decision requires human review.

Path to Redemption

Someone flagged for fraud should not be permanently barred:

Program-Specific Flags: Fraud flags are scoped to the program where detected
Time-Limited: Flags expire after configurable period (default: 1 year)
Appeal Process: Applicants can submit additional documentation
Human Override: Case managers can clear flags with justification
Cross-Program: Only confirmed fraud (human-verified) affects other programs

Bias Mitigation

Legitimate applicants from marginalized communities may exhibit patterns that models incorrectly flag:

Pattern	Why It Happens	Mitigation
Frequent address changes	Housing instability	Weight address history less for housing programs
Non-standard employment	Gig economy, seasonal work	Accept alternative income documentation
Shared bank accounts	Multi-generational households	Allow multiple applicants per account
No traditional ID	Undocumented, homeless	Accept alternative identity verification

Regular audits analyze false-positive rates by demographic group to identify and correct bias.

Data Model

Core Tables

-- Batch uploads for analysis
CREATE TABLE sentinel_batches (
  id UUID PRIMARY KEY,
  name TEXT NOT NULL,
  source TEXT NOT NULL, -- 'airtable', 'terra', 'csv'
  source_config JSONB,
  status TEXT DEFAULT 'pending', -- pending, processing, complete, failed
  record_count INTEGER,
  flagged_count INTEGER,
  created_by UUID REFERENCES users,
  created_at TIMESTAMPTZ DEFAULT NOW(),
  completed_at TIMESTAMPTZ
);

-- Individual records in a batch
CREATE TABLE sentinel_records (
  id UUID PRIMARY KEY,
  batch_id UUID REFERENCES sentinel_batches,
  external_id TEXT, -- Airtable record ID, submission ID, etc.
  submission_id UUID REFERENCES submissions,
  applicant_id UUID REFERENCES applicants,
  raw_data JSONB NOT NULL,
  normalized_data JSONB,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Risk assessments per record
CREATE TABLE sentinel_assessments (
  id UUID PRIMARY KEY,
  record_id UUID REFERENCES sentinel_records,
  overall_score DECIMAL(5,2),
  vector_scores JSONB,
  flags JSONB,
  confidence DECIMAL(3,2),
  review_required BOOLEAN DEFAULT FALSE,
  reviewed_by UUID REFERENCES users,
  reviewed_at TIMESTAMPTZ,
  review_decision TEXT, -- 'cleared', 'confirmed_fraud', 'needs_info'
  review_notes TEXT,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Fraud vector definitions
CREATE TABLE sentinel_vectors (
  id UUID PRIMARY KEY,
  name TEXT NOT NULL,
  category TEXT NOT NULL, -- geographic, identity, document, behavioral, financial
  description TEXT,
  config JSONB NOT NULL, -- Vector-specific configuration
  weight DECIMAL(3,2) DEFAULT 1.0,
  enabled BOOLEAN DEFAULT TRUE,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Automated workflow definitions
CREATE TABLE sentinel_workflows (
  id UUID PRIMARY KEY,
  name TEXT NOT NULL,
  trigger TEXT NOT NULL, -- 'batch_upload', 'scheduled', 'manual'
  vectors UUID[] NOT NULL, -- Array of vector IDs to run
  output_config JSONB, -- Where to send results
  enabled BOOLEAN DEFAULT TRUE,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Known fraudulent data points
CREATE TABLE sentinel_blocklist (
  id UUID PRIMARY KEY,
  type TEXT NOT NULL, -- 'ssn', 'bank_account', 'address', 'ip', 'device_id', 'document_hash'
  value_hash TEXT NOT NULL, -- Hashed for privacy
  reason TEXT,
  confirmed_by UUID REFERENCES users,
  expires_at TIMESTAMPTZ,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

UI Wireframe

Batch Analysis Dashboard

┌─────────────────────────────────────────────────────────────────┐
│  Sentinel                                 [User] [Settings]     │
├──────────────┬──────────────────────────────────────────────────┤
│              │                                                  │
│  Batches     │  ERA December 2024                              │
│  ──────────  │  ═══════════════════                            │
│              │                                                  │
│  > Dec 2024  │  Records: 2,847    Flagged: 127 (4.5%)          │
│    Nov 2024  │  Status: Complete  Reviewed: 89/127             │
│    Oct 2024  │                                                  │
│              │  ┌────────────────────────────────────────┐     │
│  ──────────  │  │  Risk Distribution                     │     │
│  Vectors     │  │  ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░  0-30: 2,450    │     │
│  Workflows   │  │  ▓▓▓░░░░░░░░░░░░░░░░░  31-50: 180     │     │
│  Blocklist   │  │  ▓▓░░░░░░░░░░░░░░░░░░  51-70: 127     │     │
│              │  │  ▓░░░░░░░░░░░░░░░░░░░  71-85: 67      │     │
│  ──────────  │  │  ░░░░░░░░░░░░░░░░░░░░  86+: 23        │     │
│  + Upload    │  └────────────────────────────────────────┘     │
│              │                                                  │
│              │  Flagged Records (Pending Review)               │
│              │  ─────────────────────────────                  │
│              │  ┌──────────────────────────────────────────┐  │
│              │  │ [!] Score: 87  John D*** - IP cluster     │  │
│              │  │ [!] Score: 82  Maria S*** - Doc template  │  │
│              │  │ [!] Score: 79  Robert J*** - Bank verify  │  │
│              │  └──────────────────────────────────────────┘  │
│              │                                                  │
│              │  [Export to Airtable]  [Generate Report]        │
└──────────────┴──────────────────────────────────────────────────┘

Vector Configuration

┌─────────────────────────────────────────────────────────────────┐
│  Fraud Vectors                            [+ New Vector]        │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Geographic Vectors                                             │
│  ══════════════════                                            │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  [✓] IP Clustering                          Weight: 1.2  │   │
│  │      Flag when >5 submissions from same IP in 24hr      │   │
│  │      Threshold: 5 | Window: 24hr | Severity: High       │   │
│  │                                           [Edit] [Test]  │   │
│  ├─────────────────────────────────────────────────────────┤   │
│  │  [✓] Address Verification                   Weight: 1.0  │   │
│  │      Verify address exists via SmartyStreets            │   │
│  │      Fail action: Flag | Severity: Medium               │   │
│  │                                           [Edit] [Test]  │   │
│  ├─────────────────────────────────────────────────────────┤   │
│  │  [ ] Distance Analysis                      Weight: 0.8  │   │
│  │      Flag when employer >100mi from residence           │   │
│  │      Threshold: 100mi | Severity: Low                   │   │
│  │                                           [Edit] [Test]  │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│  Identity Vectors                                               │
│  ════════════════                                              │
│  ...                                                           │
└─────────────────────────────────────────────────────────────────┘

Integration Points

Airtable Integration

Input: Export CSV from Airtable → Upload to Sentinel Output: Export flagged records → New Airtable base for review

// Export format for Airtable
interface AirtableExport {
  recordId: string;
  submissionId: string;
  applicantName: string; // Redacted
  riskScore: number;
  flags: string[]; // Human-readable flag descriptions
  reviewPriority: 'low' | 'standard' | 'high' | 'critical';
  reviewLink: string; // Link back to Sentinel for details
}

Hub Integration

Fraud assessments sync to Hub for unified applicant view:

// Sync to Hub
await syncFraudAssessment({
  applicantId: assessment.applicantId,
  programId: batch.programId,
  riskScore: assessment.overallScore,
  flags: assessment.flags,
  status: assessment.reviewDecision,
});

Terra Integration

Read submissions directly from Terra for analysis:

// Query Terra submissions
const submissions = await getSubmissions({
  formId: 'era-2024',
  dateRange: { start: '2024-12-01', end: '2024-12-31' },
  status: ['submitted', 'under_review'],
});

// Create batch for analysis
const batch = await createBatch({
  name: 'ERA December 2024',
  source: 'terra',
  submissions,
});

Implementation Phases

Phase 1: Batch Upload + Basic Vectors

CSV upload from Airtable
Data normalization pipeline
IP clustering detection
Duplicate detection (SSN, email)
Basic risk scoring
Export to Airtable

Phase 2: Advanced Vectors

Document analysis (template detection, metadata)
Bank verification integration
Geographic analysis
Behavioral patterns

Phase 3: Automated Workflows

Scheduled batch processing
Terra direct integration
Hub sync
Configurable alert thresholds

Next Steps

Fraud Vectors Deep Dive

Detailed documentation for each vector type

Risk Scoring Model

How scores are calculated and calibrated

Workflow Configuration

Building automated detection pipelines

Bias Mitigation

Ensuring fair treatment of legitimate applicants

​Welcome to Sentinel

​What Sentinel Does

Batch Upload

Fraud Vectors

Automated Workflows

Results Export

​Who Uses Sentinel

​Fraud Vectors

​Geographic Analysis

​Identity Analysis

​Document Analysis

​Behavioral Analysis

​Financial Analysis

​Workflow Architecture

​Risk Scoring Model

​Scoring Thresholds

​Human in the Loop

​Path to Redemption

​Bias Mitigation

​Data Model

​Core Tables

​UI Wireframe

​Batch Analysis Dashboard

​Vector Configuration

​Integration Points

​Airtable Integration

​Hub Integration

​Terra Integration

​Implementation Phases

​Phase 1: Batch Upload + Basic Vectors

​Phase 2: Advanced Vectors

​Phase 3: Automated Workflows

​Next Steps

Fraud Vectors Deep Dive

Risk Scoring Model

Workflow Configuration

Bias Mitigation

Welcome to Sentinel

What Sentinel Does

Who Uses Sentinel

Fraud Vectors

Geographic Analysis

Identity Analysis

Document Analysis

Behavioral Analysis

Financial Analysis

Workflow Architecture

Risk Scoring Model

Scoring Thresholds

Human in the Loop

Path to Redemption

Bias Mitigation

Data Model

Core Tables

UI Wireframe

Batch Analysis Dashboard

Vector Configuration

Integration Points

Airtable Integration

Hub Integration

Terra Integration

Implementation Phases

Phase 1: Batch Upload + Basic Vectors

Phase 2: Advanced Vectors

Phase 3: Automated Workflows

Next Steps