Examples of errors in SSA Payroll Information Exchange reports?
Executive summary
The Payroll Information Exchange (PIE) is designed to pull employer payroll data into Social Security Administration (SSA) systems to reduce manual reporting and improper payments, but real-world pilot analyses and SSA guidance document multiple concrete error types — from optical-scanning mistakes and date/earnings mismatches to invalid file identifiers and name/SSN mismatches — that can produce incorrect credits or rejected reports [1] [2] [3] [4].
1. Optical‑scanning and document transcription errors that distort paystub values
A focused SSA accuracy study found a large source of gross‑earnings mismatches traced not to the payroll provider but to optical scanning inaccuracies when the agency compared scanned paystub images in its SSAMWR system to the original paystub photos uploaded by beneficiaries, producing thousands of apparent mismatches that largely reflected scanning errors rather than payroll data faults [2].
2. Date and pay‑period mismatches that break record linking
The PIE validation compared four variables — pay period start, pay period end, pay date, and gross earnings — and used matching rules that required alignment of date variables; failures in two‑of‑three date matches or misaligned pay dates caused records from PIE and SSA’s internal systems to fail to link, creating the 7,976 initial mismatch set the study investigated [2] [5].
3. Gross‑earnings discrepancies from differing data sources and formats
Even when records partially matched, gross earnings sometimes differed between The Work Number (Equifax) data and SSA’s processed records; the SSA sample review extrapolated that the vast majority of cases did match across all variables but raised attention to the subset where earnings amounts diverged, pointing to formatting, rounding, or extraction inconsistencies as plausible causes [2].
4. Invalid file and record identifiers that cause rejections at intake
Employers submitting wage files to SSA can trigger processing errors when Record Identifiers or EFW2/EFW2C formatting are invalid; SSA’s error reference explicitly lists invalid Record Identifiers and other file‑format issues as reasons the agency cannot process wage reports until corrected and resubmitted [3].
5. Typographical, name‑change, and SSN mismatches that prevent crediting
SSA employer notices and guidance show common causes of unmatched wage credits are typographical errors, unreported name changes, and inaccurate or incomplete employer records; when a reported name and SSN don’t match SSA records the agency cannot credit earnings and notifies employers to correct the problem [4].
6. Missing or omitted employer reports and the gaps they create
Errors also arise when wages are omitted from employer filings or reports go missing entirely; SSA guidance for correcting earnings records cites "employee omissions from processed employer reports or missing reports" as a cause of incomplete records that require manual correction [6] [7].
7. Data‑coverage and representativeness concerns about third‑party payroll sources
PIE’s contractor arrangements — including Equifax’s The Work Number, which SSA found covers a large share of nonfarm payrolls — carry the caveat that neither Equifax nor SSA had fully analyzed whether payroll providers represent benefit recipients at the same rate, a representativeness gap that can yield blind spots or differential error rates for specific beneficiary populations [2].
8. Administrative tradeoffs, agency remedies, and oversight signals
SSA and external reviewers like the GAO frame PIE as a tool to reduce improper payments and reporting burdens, but the rollout includes administrative costs and new processes (SSA estimates net administrative costs over ten years) and relies on employer and vendor fixes — such as error notices, AccuWage pretests, and employer correction notices — to address the concrete errors listed above [8] [3] [4].
9. What this means for outcomes: benefits, limits, and who bears risk
While SSA claims PIE will reduce manual mistakes and some beneficiary reporting obligations, the documented error types — scanning/transcription issues, date/gross mismatches, file formatting errors, name/SSN mismatches, and coverage gaps in payroll data providers — show that automation shifts the locus of error rather than eliminating it, concentrating risks in data exchange and vendor processing that SSA, employers, and beneficiaries will all need to monitor and correct [1] [2] [8].