Employment History Verification API: How to Verify Candidate Work Experience Using UAN, PAN & Mobile

Introduction

Resume fraud costs businesses an estimated $600 billion annually. Over 64% of candidates have misrepresented their work experience, skills, or job titles on resumes and with AI-generated documents becoming harder to detect, that number is climbing. If your HR team still relies on phone-based reference checks and manual document review, you are exposed.

The solution is not more human effort. It is API-driven employment history verification — real-time, automated checks against government databases like EPFO, Income Tax, and UIDAI that return verified employment records in milliseconds, not weeks.

This guide covers how employment verification APIs work, which government databases they connect to, and how to build a complete employment verification workflow using DocuExprt's agentic AI platform from candidate data intake through automated cross-verification and report generation.

Why Employment History Verification Matters for Enterprise HR

The Cost of Getting It Wrong

A single bad hire at the mid-management level costs organizations between $50,000 and $240,000 when you factor in recruitment costs, onboarding, lost productivity, severance, and rehiring. For senior roles, that figure can exceed $500,000.

But the real risk is not just financial. In regulated industries like banking, insurance, healthcare, government, an unverified hire can trigger compliance violations, regulatory penalties, and reputational damage that far exceeds the hiring cost.

Resume Fraud Has Reached Crisis Levels

The numbers tell the story:

The gap between detection capability and fraud sophistication is widening. Nearly two-thirds (62%) of hiring professionals believe candidates are now better at faking identities with AI help than HR teams are at detecting deception.

Manual Verification Is Broken

Traditional employment verification methods have fundamental limitations:

• Phone-based reference checks take 3-7 business days per candidate and depend on former employers answering
• Letter-based verification can take 2-4 weeks and is easily forged
• Manual EPFO checks require candidates to produce physical documents that can be fabricated
• No real-time cross-verification between claimed experience and actual records

For organizations processing 50+ hires per month, manual verification becomes a bottleneck that either slows hiring or gets skipped entirely creating the compliance gaps that regulators exploit.

Regulatory Compliance Is Non-Negotiable

In India's BFSI sector, RBI and SEBI mandate robust employee screening for anyone in positions of financial responsibility or with access to sensitive customer data. Key requirements include:

• Fit and Proper criteria for directors and key functionaries at banks and NBFCs
• KYC staff screening — employees handling KYC must themselves be verified
• Third-party vendor verification for anyone accessing customer data
• Audit trail maintenance — regulatory audits sample random employee records, and missing verification documentation triggers serious compliance findings
• DPDP Act compliance — explicit consent, purpose limitation, and data minimization for all candidate data processing

How Employment Verification APIs Work

Employment verification APIs connect your HR systems directly to government databases, returning verified employment records in real time. Here is the core architecture:

The Three Government Database Layers

API vs. Manual Verification Comparison

DocuExprt's Employment Verification APIs

DocuExprt provides five employment verification API endpoints that cover the full spectrum of employment history checks:

1. UAN-To-Employment-History API

The flagship employment verification endpoint. Input a UAN number and receive the complete EPFO employment history.

Input: UAN Number
Returns:

• Employee name (as registered with EPFO)
• All employer names and establishment IDs
• Date of joining and date of exit for each employer
• Member IDs across employers
• PF filing status and contribution history
• Exit reason codes

Primary use case: Validate a candidate's claimed work experience against official EPFO records. If a candidate claims 5 years at Company X but EPFO records show only 2 years, the discrepancy is flagged automatically.

2. PAN-To-Employment-Status API

Input a PAN number and verify current employment status through income tax records.

Input: PAN Number
Returns:

• Current employment status
• TDS deduction history (Form 26AS data)
• Income declarations cross-referenced with employment claims

Primary use case: Detect moonlighting and dual employment. If a candidate claims to be unemployed but Form 26AS shows active TDS deductions from another employer, you know immediately. Since the pandemic, moonlighting investigations have grown 4X since 2019 — this API catches what HR interviews cannot.

3. Aadhaar-To-UAN Lookup

When a candidate doesn't know their UAN number (common for blue-collar and early-career workers), this API bridges the gap.

Input: Aadhaar-linked details
Returns: Associated UAN number(s)

Primary use case: Enable employment history verification for candidates who only have their Aadhaar number available. Chain this with the UAN-To-Employment-History API for complete verification.

4. UAN-To-UAN Verification

Validates that a UAN number is genuine and active within the EPFO system.

Input: UAN Number
Returns: UAN validity status, associated details

Primary use case: Quick validation before running the full employment history lookup. Filters out fake or inactive UAN numbers.

5. Name Match API

Cross-references names across multiple documents and database records to catch identity discrepancies.

Input: Name strings from different sources
Returns: Match confidence score, variation analysis

Primary use case: Verify that the name on the resume, PAN card, Aadhaar card, and EPFO records all belong to the same person. Catches identity fraud where candidates use another person's employment history.

Building an Employment Verification Workflow in DocuExprt

DocuExprt's visual workflow builder lets you create a complete employment verification pipeline using five node types without coding. Here is a production-ready workflow architecture:

Workflow Architecture: Employee Background Verification

Step-by-Step Workflow Setup

Step 1: Configure Input Node

Set up the Input node to accept candidate data from multiple sources:

• File upload: Resume/CV in PDF, image, or scanned document format
• Form fields: PAN number, UAN number (optional), Aadhaar number (optional)
• Bulk upload: CSV file with multiple candidate records for batch processing
• API trigger: Incoming webhook from your ATS (Applicant Tracking System)

Step 2: Configure Processing Nodes

Add four processing nodes in parallel:

1. AI Document Extraction — DocuExprt's AI extracts candidate name, claimed employers, job titles, tenure dates, and education from the uploaded resume. Works with 20+ languages including Hindi, Telugu, Tamil, and Gujarati.
2. UAN-To-Employment-History API — Fetches the complete EPFO employment history using the candidate's UAN number.
3. PAN-To-Employment-Status API — Checks current employment status and TDS history via the PAN number.
4. Name Match — Cross-references the candidate name from the resume against names in EPFO and PAN records.

Step 3: Configure Conditional Node

The Conditional node is where DocuExprt's agentic AI makes autonomous decisions:

• IF resume claims match EPFO records (employer names, dates within ±30 days) → Route to "Verified" output
• IF minor discrepancies exist (date differences of 30-90 days, minor name variations) → Route to "Review Required" output
• IF major discrepancies exist (missing employers, fabricated tenure, identity mismatch) → Route to "Failed" output with flagged issues
• IF PAN-To-Employment-Status shows active employment elsewhere → Flag "Moonlighting Detected"

Step 4: Configure Output Node

Generate structured verification reports:

• PDF verification report with pass/fail/flag status for each check
• Discrepancy summary highlighting specific mismatches between resume and records
• Compliance audit trail with timestamps, data sources, and API response logs
• Integration output — push results back to your HRMS or ATS via webhook

Step 5: Configure Evaluation Node

Set confidence scoring thresholds:

• High confidence (>95%): Auto-approve, no human review needed
• Medium confidence (70-95%): Route to HR team for manual review with pre-populated discrepancy notes
• Low confidence (<70%): Auto-reject with detailed reason codes

Industry Use Cases

BFSI Employee Onboarding Workflow

The challenge: RBI and SEBI mandate background verification for all employees with financial responsibility. Banks and NBFCs processing 100+ new hires per quarter need verification that is both thorough and fast.

The workflow:

1. New hire submits resume + PAN + UAN via the HRMS integration
2. DocuExprt extracts claimed experience and runs parallel API checks
3. Employment history is cross-verified against EPFO records
4. Active employment status is checked to ensure the candidate has resigned from their previous role
5. Verification report is generated with compliance-ready audit trail

The result: Verification turnaround drops from 7-14 days to under 2 minutes. Compliance documentation is auto-generated for regulatory audits. HR teams process 100+ verifications per day instead of per month.

IT/ITES Mass Hiring Workflow

The challenge: Large IT companies process hundreds of candidates per week during campus recruitment drives and lateral hiring cycles. Manual verification at this scale either becomes the hiring bottleneck or gets skipped.

The workflow:

1. Batch upload candidate CSV from ATS (500+ candidates)
2. DocuExprt processes all candidates in parallel using bulk API calls
3. Candidates are auto-sorted into Verified / Review / Failed categories
4. HR team only manually reviews the 10-15% flagged candidates
5. Results are pushed back to ATS with verification status

The result: 500 candidate verifications that would take a 5-person team 2 weeks complete in under 4 hours. Moonlighting detection catches dual-employment cases that interviews never surface.

Government & Defence Contractors Workflow

The challenge: Security clearance requires complete employment history cross-verification for 10+ year periods. Every gap must be accounted for. Audit trails must be tamper-proof.

The workflow:

1. Extended employment history pulled for the full EPFO tenure
2. Every employer in the candidate's history is verified against EPFO establishment records
3. Gaps between employments are flagged for follow-up
4. All verification data is stored with immutable audit logs
5. Role-based access control ensures only authorized personnel view sensitive records

The result: Complete 10-year employment history verified and documented in minutes. Audit-ready reports satisfy defense procurement compliance requirements.

Background Verification Companies Workflow

The challenge: BGV companies are intermediaries that need to process verifications at scale for their enterprise clients. They need API-first architecture, white-label capabilities, and token-based pricing.

The workflow:

1. Client submits verification requests via API
2. DocuExprt processes UAN, PAN, and Aadhaar lookups
3. Results are returned via webhook or API polling
4. Token-based pricing means BGV companies pay per verification, not per seat

The result: BGV companies reduce per-verification costs by 60-80% while delivering results in seconds instead of days. API-first architecture means zero manual intervention for standard checks.

Integration Guide

REST API Endpoints

DocuExprt's employment verification APIs follow standard REST conventions with JSON request/response formats:

Authentication: API key-based authentication with workspace-level isolation

Available Endpoints

Sample API Request (UAN-To-Employment-History)

{
  "uan_number": "1001XXXXXXXX",
  "consent": "Y",
  "reason": "employment_verification"
}

Sample API Response

{
  "status": "success",
  "data": {
    "employee_name": "Rajesh Kumar",
    "uan": "1001XXXXXXXX",
    "employment_history": [
      {
        "employer": "ABC Technologies Pvt Ltd",
        "establishment_id": "DLCPM0012345",
        "member_id": "PY/DLH/0012345/001/0001234",
        "date_of_joining": "2019-06-15",
        "date_of_exit": "2022-03-31",
        "exit_reason": "resignation"
      },
      {
        "employer": "XYZ Financial Services Ltd",
        "establishment_id": "MHBAN0067890",
        "member_id": "PY/MH/0067890/001/0005678",
        "date_of_joining": "2022-04-18",
        "date_of_exit": null,
        "exit_reason": null
      }
    ],
    "total_employers": 2,
    "pf_filing_status": "active"
  },
  "request_id": "req_abc123xyz",
  "timestamp": "2026-03-05T10:30:00Z"
}

Integration Options

• Direct API calls — Use from any language/framework with HTTP support
• Webhook callbacks — Register webhooks for async processing of bulk requests
• No-code workflow builder — Build complete verification pipelines without writing code
• Cloud storage integration — Input from and output to Amazon S3, Azure Blob, GCP, or local folders
• HRMS/ATS connectors — Push/pull data from your existing HR systems

Pricing Model

DocuExprt uses token-based pay-per-document pricing. Each API call consumes tokens based on the complexity of the verification:

• No monthly minimums or seat-based licensing
• Volume discounts for enterprise accounts
• Free trial tokens available for testing