Data Migration in Social Security: The Hardest Part of Modernization—and the Part You Cannot Shortcut

By Francis Tots

For social security institutions, “data migration” is often described as a workstream inside a core-system modernization program. In practice, it becomes the single largest determinant of whether modernization succeeds, whether benefits and contributions remain trustworthy, and whether the new platform earns institutional legitimacy. That is because social security modernization is not only a technology change; it is a transfer of legally meaningful history—people, rights, obligations, decisions, and payments—into a new operational reality.  It doesn’t matter which software solution you are implementing; the reality remains the same: the data is key to the success of the project.

IT directors across the world already know the basic storyline: legacy systems are aging, policies evolve faster than code, customer expectations rise, and interoperability with national ID, tax, civil registry, and banking or mobile money becomes non-negotiable. What is less commonly acknowledged—until implementation begins—is that social security data is structurally different from typical government or enterprise datasets, and the migration challenge is therefore different in kind, not just in scale. It is not “big data” in the trendy sense; it is high-stakes, high-linkage, policy-conditioned data where small errors can create real financial harm, legal disputes, and reputational crises.

This paper takes a practical look at why social security data migrations are uniquely hard, what official oversight reports show about real-world data quality gaps (including in the United States), what technical and organizational patterns repeatedly derail programs, what solutions actually work, and how AI can assist—without creating the illusion of a shortcut.

1) Why social security data is different: volume, structure, and “legal truth”

1.1 It is longitudinal, life-course data, not “transactional data”

Most enterprise migrations move a “current state” plus a limited history, because the enterprise can operate on the present and keep old history in archives. Social security cannot do this. Social security migrates lifetimes. A single insured person’s record can begin at registration and continue through decades of employment, multiple employers, and multiple reporting cycles, with corrections and audits applied years later. That same record can include periods of unemployment, disability, maternity, sickness, or other contingencies, each with its own eligibility logic and evidence trail. It can include multiple claims—old-age, survivors, disability—often with recalculations and retroactive adjustments when law changes, when earnings are corrected, or when a decision is appealed and revised.

The point is not only that there is “a lot” of data. The point is that the data is deeply relational and cumulative. Each pension calculation depends on a chain of linked facts: identity, insured periods, validated earnings or contribution bases, scheme membership, credited periods, dependents, and the timing of events. If you migrate the facts but break the relationships—an employer contribution no longer linked to the correct wage period, a dependent no longer linked to the correct beneficiary, or a service credit no longer linked to the right policy version—then you have moved data successfully but destroyed truth operationally.

For many institutions, this life-course complexity is often intensified by program expansion over time. Many administrations have extended coverage from civil servants to private sector employees, then to categories such as self-employed, informal sector, or voluntary contributors. Each expansion adds new data structures, new identifiers, and new exceptions. Migration is therefore not a “database exercise”; it is the reconstitution of an institutional memory.

1.2 It is policy-driven and effective-dated by nature

Social security systems embed law and policy. Rules change, and those changes must be applied prospectively through new accrual or contribution rules, but also retrospectively through recalculations under amended law, and often through transitional provisions that “grandfather” specific cohorts or protect acquired rights. In real administrations, exceptions and adjudicated decisions are not edge cases; they are normal governance. Appeals tribunals, discretionary waivers, court rulings, and board decisions create “authorized deviations” from standard processing that must remain traceable and enforceable.

This produces layers of effective dating, versioning, and “as-of” truth. A contribution may be posted in 2022 for wages earned in 2019, corrected in 2024 after an audit, then reflected in a recalculated pension in 2025 because the person retired in 2023 but the correction arrived late. If your migration collapses effective dates, overwrites historical values with current values, or loses the logic that explains why a record is exceptional, you can migrate “accurately” and still produce wrong outcomes. In social security, correctness is not just field-level accuracy; it is time-based integrity plus policy-context integrity.

International best-practice guidance repeatedly emphasizes governance and disciplined data management because social security performance is ultimately judged by service quality and benefit/payment correctness (International Social Security Association [ISSA], n.d.).

For IT directors, the practical takeaway is that migration design must treat effective dating as a first-class requirement. It must preserve event sequences, policy versions, and decision trails—not merely balances or summary totals.

1.3 Identity is foundational—and frequently imperfect

Across many nations, identity infrastructure is improving quickly, including digital ID programs, biometrics, and unique ID numbers. Yet social security institutions still face incomplete civil registration coverage (especially historically), inconsistent identifiers across agencies, multiple IDs per person, and name or date-of-birth variability across documents. Legacy paper records and manual registers often remain part of the institutional truth, particularly for older cohorts, rural populations, cross-border workers, and historically underserved groups.

Even when a national ID exists, system-to-system integration and identity assurance are non-trivial. “Having an ID number” does not guarantee that legacy records are correctly linked to it, that duplicates are resolved, or that updates flow reliably across the ecosystem. If the civil registry updates death records late, if national ID records have alternate spellings, or if tax/employer identifiers are inconsistently captured, social security ends up maintaining “shadow identities” that must be reconciled during migration.

The World Bank’s ID4D guidance emphasizes that robust identification depends on uniqueness, accuracy, and secure lifecycle management (World Bank, 2018).  This matters directly to migration because identity is the join key for everything else. If identity is unstable, every linkage—benefit eligibility, contribution attribution, dependent relationships, and payment routing—becomes fragile.

1.4 The data is “legally actionable”

If an HR system migrates imperfect data, payroll continues and errors are inconvenient. If a social security system migrates imperfect data, the institution can pay the wrong person, pay the right person the wrong amount, deny a legitimate claim, create audit findings and reputational damage, and trigger costly and politically sensitive remediation. Social security data is fiduciary in nature: contributions are a form of public trust, benefits are legal entitlements, and the institution is accountable to members, employers, oversight bodies, and the state.

That legal and fiduciary weight changes what “good enough” means. In many enterprise migrations, a small percentage of “dirty data” can be tolerated and cleaned later. In social security, a small percentage of errors can translate into thousands of incorrect payments, systemic overpayments that take years to recover, or a wave of appeals that overwhelms operations. It can also undermine public confidence in modernization, which is often politically fragile.

For IT directors, the implication is straightforward: migration success criteria must include legal defensibility, not just technical completion. You are not only migrating records; you are migrating the institution’s ability to stand behind its decisions.

2) What official reports show about real data gaps and quality issues

Data quality problems are not a “developing country problem.” They exist even in some of the most mature administrations, and they show up in ways that directly matter to migration planning.

2.1 The U.S. example: missing death information and identity cross-referencing

A widely cited U.S. oversight finding illustrates a specific kind of data gap. The U.S. Social Security Administration (SSA) Office of Inspector General reported that SSA’s Numident—the master file of Social Security Number assignments—lacked death information for approximately 18.9 million numberholders born in 1920 or earlier at the time of review (Social Security Administration Office of Inspector General [SSA OIG], 2023). The report stresses that missing death data hampers fraud prevention and broader government data matching, even if it does not automatically mean benefits are being paid to those individuals.

This matters to IT directors for two reasons. First, it proves that even large institutions with decades of digital history can still carry massive pockets of missing or incomplete lifecycle data. Second, it demonstrates why migration must treat lifecycle events—death, emigration, eligibility termination—as first-class data objects, not optional attributes. If death is merely a free-text note or a loosely validated date field in the target system, then your new system will reproduce the same weaknesses, except now they will be harder to explain because “the system is modern.”

The SSA OIG work also highlights the practical reality that identity is not static even in strong identifier regimes. SSA maintains cross-references to handle identity complexities over time, underscoring that duplicates and identity corrections are operational facts, not embarrassing anomalies (SSA OIG, 2023). For many social security institutions—often operating across older paper eras, national ID transitions, and multi-agency identifier mismatches—this should reduce the stigma around identity remediation and reframe it as essential system engineering.

2.2 Data quality is a prerequisite for analytics, integrity, and AI

The U.S. Government Accountability Office has repeatedly emphasized a principle that applies directly to social security: data quality and skilled workforce capacity are essential to unlock benefits from AI in fraud and improper payment contexts (Government Accountability Office [GAO], 2025).  The core message is simple but decisive: institutions cannot “AI their way out” of weak data foundations. If data is incomplete, inconsistent, or poorly governed, AI models become unreliable, produce false positives or false negatives, and erode trust.

For social security institutions around the world, this is not theoretical. Many administrations want to use analytics or AI for compliance targeting, contribution anomaly detection, duplicate pension detection, or identity fraud prevention. Those ambitions are valid, but they depend on the boring fundamentals: consistent identifiers, reliable event histories, governed reference data, and auditable transformations. AI is an amplifier; it amplifies the quality of what you feed it.

2.3 Global operational guidance points to the same root issues

Global guidance converges on the same root issues: data must be governed, standardized, validated, and actively managed as an institutional asset. ISSA guidance on service quality explicitly calls for formal data quality management processes—data profiling, cleansing, standardization, and integration—supported by tools and validation mechanisms (ISSA, n.d.). ISSA governance guidance similarly stresses the need for data governance frameworks to formalize authority and control over data assets (ISSA, n.d.).  In other words, data management is an ongoing challenge and should be a continuous effort in any social security administration.  It’s not a one-time affair to be done during the implementation of a new system.  In fact, doing it only when a new system is being implemented, is the perfect recipe for disaster as it will dramatically increase the chance of failure or at least cause major delays to the new software implementation.

From the delivery-systems perspective, the World Bank highlights interoperability and dynamic data sharing among social registries, identification systems, and administrative databases as essential for accuracy and responsiveness (World Bank, 2025).  For many developing nations—where social security increasingly interacts with national ID, tax, civil registry, and payment rails—this is not a “nice to have.” It is the ecosystem in which your new core must live, and it will expose your data weaknesses quickly if migration does not fix them.

3) The technology challenges that make social security migration uniquely risky

3.1 “Large data” is not just row count—it is linkage density

A social security database typically contains master data for people, employers, dependents, and beneficiaries, but it also contains event data such as employment periods, contribution filings, and wage lines. It contains entitlement data such as insured periods, credited periods, and service credits. It contains adjudication data such as decisions, evidence, and appeals. It contains financial data such as assessments, receivables, penalties, installments, and payments, and it contains payment outputs such as payroll runs, reversals, recoveries, and reinstatements.

A migration is not successful merely because these tables are moved. It is successful only if referential integrity is preserved so that relationships remain valid; if time-based integrity is preserved so that effective dating and event sequences remain correct; and if computational integrity is preserved so that benefit formulas apply to the correct historical facts and produce defensible results. In social security, integrity is multi-dimensional, and a project that measures success only by “records loaded” is measuring the wrong thing.

3.2 Batch windows, payroll cycles, and cutover constraints

Many social security institutions operate within narrow operational windows. Monthly pension payroll must run. Periodic employer filing cycles and compliance actions must continue. Nightly batch validations and interfaces to banks or mobile money must not break. Annual statements and actuarial extracts often have statutory or governance deadlines.

Migration must be engineered around these hard deadlines, not around developer convenience. During cutover, “we’ll rerun the load” is often not an option because rerunning may collide with payroll deadlines, banking settlement windows, or legal obligations to pay beneficiaries on time. This is why social security migrations require disciplined rehearsals, performance engineering, and operational cutover planning that treats payroll as sacred. It is also why wave strategies and parallel runs matter: they reduce the probability that a single mistake becomes a national crisis.

3.3 Performance and scalability constraints across the migration factory

In practice, migration bottlenecks appear in predictable places. Extraction can be slow because legacy platforms may be mainframes, proprietary databases, or brittle schemas with poorly indexed structures. Transformation can be slow because effective dating logic, code translation rules, and deduplication all require expensive joins and complex business logic at scale. Loading into a normalized target model can be slow because inserts trigger index rebuilds, constraints, and referential checks; and if you load “raw” without staging, you lose control over repeatability.

Reconciliation queries can become a bottleneck because they often require heavy joins across huge partitions to prove equivalence. Audit logging and lineage capture can also become surprisingly heavy because the metadata and evidence trails can approach the size of the data itself when done properly.

The program must therefore architect for throughput, not just correctness. This is the difference between a migration “script” and a migration “factory”: a factory is designed to run repeatedly, predictably, and fast enough to meet operational constraints while producing evidence you can defend.

3.4 Identity resolution at national scale is a specialized engineering problem

Deduplication and identity proofing often involve 1:N or N:N comparisons, which can be computationally expensive and operationally sensitive (World Bank, 2018).  When identity attributes are imperfect—names, dates, addresses—deterministic matching fails, and probabilistic approaches become necessary. That immediately introduces governance questions: what confidence threshold is acceptable for automatic merges, which cases require manual adjudication, how do you prevent “silent merges,” and how do you preserve lineage so you can explain the decision years later?

For many social security institutions, identity resolution is often the single biggest hidden risk in migration because it sits at the intersection of technology, operations, law, and public trust. A bad deduplication decision is not just a technical error; it can merge two people’s contribution histories, split one person into two beneficiaries, or trigger incorrect payments that are politically explosive.

3.5 Security and privacy multiply complexity

Social security data is among the most sensitive data a government holds. Migration must enforce encryption in transit and at rest, strict access controls and privileged access management, audit trails for every extract and transformation, controlled test data creation through masking or synthetic data, and segregation of duties between implementation partners and institutional roles.

For many institutions, modernization also coincides with heightened national attention to data sovereignty and cross-border hosting. That affects cloud strategy, vendor models, where environments are hosted, how data is transported, and which teams can access which datasets. If security is treated as a compliance checklist rather than as an engineering constraint, it will surface late and cause delays—or worse, force unsafe shortcuts that create long-term institutional risk.

4) How migration derails projects: the recurring failure modes

Across jurisdictions, the same patterns appear.

4.1 Data profiling starts too late

When profiling starts late, teams discover “unknown unknowns” after design is locked. Duplicate employers may appear because the legacy system allowed multiple employer records for one legal entity across years or regions. Insured persons may be unlinked to contributions because older processes posted contributions to “suspense” accounts or temporary identifiers. Wage periods may be missing or inconsistent because filings were captured as summaries rather than line items. Dates may contradict each other because of placeholder defaults or manual entry errors. Legacy code values may have no definition because they were created by developers to handle ad hoc exceptions and never documented. Shadow processes and manual corrections may have been performed outside the system and never formally captured.

ISSA’s emphasis on early profiling and formal data quality management exists precisely because these problems are predictable, and because discovering them late turns them into budget and schedule shocks (ISSA, n.d.).

4.2 No empowered data ownership

The hardest migration questions are not technical. Someone must decide which source is authoritative for death, especially when civil registry and institutional records disagree. Someone must decide which employer identifier wins when a tax ID conflicts with an internal registration number. Someone must define the policy-compliant treatment of missing contribution months: do you treat them as non-contributory, do you allow late posting, do you require evidence, or do you define a remediation campaign? Someone must define what happens when two records represent the same person: how do you merge history without overwriting legal evidence?

Without an empowered governance structure, these decisions get delayed, then made ad hoc, then revisited repeatedly. The result is churn: transformation rules change late, retesting expands, and trust erodes. ISSA explicitly frames data governance as the formal exercise of authority and control over data management, which is precisely what migrations require when truth conflicts arise (ISSA, n.d.).

4.3 Underestimating cleansing as “just ETL mapping”

ETL mapping is not cleansing. ETL mapping describes how a source field moves to a target field. Cleansing includes institutional remediation, evidence review, and rule-making. It involves deciding what to do when the source is wrong, not merely where to store it. When teams treat cleansing as a technical exercise, time and cost explode late in the program because the migration repeatedly fails reconciliation, and because operations refuse to accept outputs they cannot defend.

In social security, cleansing is also a service-quality investment. If you migrate dirty data, you create a new system that produces clean-looking screens backed by dirty truth. That is the worst outcome: the institution loses the “excuse” of a legacy system while keeping the same problems.

4.4 Weak reconciliation strategy

Institutions sometimes validate migration with record counts, spot checks, or the statement “it loaded successfully.” But social security migrations require validation at multiple levels. Totals and aggregates must reconcile, such as total contributions by year, by scheme, and by employer category, because these reveal missing cohorts and systemic posting errors. Cohort checks must reconcile, such as pensioners by age band, benefit type, and scheme, because these reveal classification and eligibility distortions. Benefit calculation equivalence testing must be performed because even small rule or history differences can change amounts materially. Payment output equivalence testing must be done because payroll is where errors become public. Exception handling equivalence must be proven because suspensions, recoveries, offsets, and reinstatements are precisely where legacy “institutional knowledge” tends to hide.

Without this, cutover becomes a leap of faith. And in social security, faith is not a control.

5) What works: the architecture and governance patterns that succeed

5.1 Build a “migration factory,” not a one-time script

Treat migration as an engineered product with repeatable pipelines, version-controlled transformation logic, automated data quality checks and dashboards, a defect management workflow, and incremental improvement with each migration rehearsal. This is not an academic ideal; it is operational necessity. You should expect multiple full rehearsals. In social security, the first run is how you learn what the data really is, and the second and third runs are how you prove you can control it.

A migration factory also supports governance, because you can track which defects were found, which rules were applied, which exceptions were adjudicated, and how outcomes improved across rehearsals. That evidence is what convinces executives, auditors, and stakeholders that modernization is reducing risk rather than creating it.

5.2 Segment and sequence by waves

Wave strategies reduce risk by aligning migration sequencing with operational criticality. Migrating pensioners first is common because payments are high-risk and the institution must be absolutely confident in payroll outcomes. Migrating active contributors next allows focus on employer filing and contributions logic, which often has different complexity and volume patterns. Migrating special schemes or exceptional cohorts separately—military, judges, legacy categories—prevents their unique rule sets and historical structures from contaminating the main migration wave. Migrating the long tail of historical data in a controlled way, or maintaining a read-only archive layer, can reduce cutover risk while still meeting legal and reporting obligations.

Wave design should follow operational risk, not organizational politics. If the program chooses waves based on “who shouts loudest,” it increases risk. If it chooses waves based on “where mistakes are most damaging,” it reduces risk.

5.3 Use strong master data governance and survivorship rules

A practical approach begins by defining “golden records” for person and employer master data. This does not mean pretending that one dataset is perfect; it means defining, attribute by attribute, which source is authoritative and why. Survivorship rules specify which source wins for which attribute—name, date of birth, address, national ID, employer legal name, tax ID—and under what conditions exceptions are allowed.

Just as importantly, a sound approach keeps lineage. The institution should store original source attributes, not just overwritten values, so that it can explain how it arrived at the current representation. This is especially important in contexts where data may be corrected through campaigns, and where legal defensibility may require showing the evidence trail.

ISSA’s governance guidance explicitly links data governance frameworks to formal authority and consistent operations, and ISSA ICT guidance emphasizes master data governance as foundational for effective ICT outcomes (ISSA, n.d.).

5.4 Engineer for performance: partitioning, CDC, and parallelization

High-throughput migration patterns are not optional when the data is large and the windows are tight. Partitioning by year, scheme, or region can isolate heavy joins and make both loading and reconciliation manageable. Change data capture (CDC) during transition can prevent the institution from freezing the legacy system for weeks, which is often operationally impossible. Parallelizing loads by partition allows faster throughput while enabling strict reconciliation per partition, so that failures are contained rather than catastrophic.

Staging raw extracts into immutable storage—often described as a “bronze layer”—enables repeatability and auditability because the program can always re-run transformations from the same raw baseline. Optimizing target loading with bulk operations and carefully controlled constraint enforcement can deliver major performance gains, but only if governed properly; disabling constraints without an auditable plan is a common way migrations create hidden corruption.

5.5 Treat interoperability as part of migration readiness

Your new platform will be tested immediately by integration. National ID verification and deduplication will expose identity gaps. Civil registry death notifications will expose lifecycle-event weaknesses. Tax authority wage and employer data exchange will expose inconsistencies in employer registers and contribution bases. Payment rails—banks, mobile money, treasury—will expose errors in payment routing and beneficiary identity. E-services and self-service channels will expose record inconsistencies because citizens see their history directly.

The World Bank frames interoperability and dynamic data sharing across delivery systems as essential for accuracy and responsiveness, which is particularly relevant for institutions building modern digital ecosystems (World Bank, 2025).  Migration readiness is therefore not only an internal database readiness; it is readiness to live in an interoperable state without collapsing under the pressure of cross-system comparisons.

6) Why there are no shortcuts in data cleansing—and what cleansing actually includes

A frequent leadership temptation is: “Can’t we just migrate and clean later?” In social security, that strategy almost always fails because the new system immediately produces legally actionable outputs—entitlements and payments. If you load bad data into a modern core, you may reproduce overpayments and arrears, institutionalize duplicate identities, generate massive complaint volumes, destroy trust in the modernization effort, and create a permanent “data debt” that becomes politically and operationally unmanageable.

ISSA explicitly recommends institutional data quality processes—profiling, cleansing, standardization, integration—because service quality depends on it (ISSA, n.d.).

6.1 The full task list of social security data cleansing (in practice)

Data profiling and measurement is the starting point because you cannot govern what you cannot measure. Profiling includes completeness checks for null rates and missing documents, validity checks for out-of-range dates and impossible ages, uniqueness checks for duplicate identities and duplicate employer registrations, consistency checks for conflicting attributes across sources, and timeliness checks for late wage reporting or delayed life-event updates. A mature program turns these into a quantified “data health dashboard” and a prioritized defect backlog, so leadership can see progress and tradeoffs.

Standardization is the work of making data comparable across sources and across time. This includes consistent handling of names—casing, whitespace, tokenization, transliteration, compound surnames—because identity matching depends on it. It includes address normalization and administrative area coding because geography matters for services and sometimes for scheme rules. It includes date standardization, including the treatment of partial dates and placeholder defaults. It includes identifier standardization such as checksum rules, padding, and normalization of legacy formats. It includes reference data standardization for scheme types, benefit types, employer sectors, and job categories, because inconsistent reference values are a common cause of reporting and eligibility errors.

Validation is where technical data quality meets business and policy logic. It includes cross-field logic checks such as ensuring marriage dates follow birth dates or that retirement dates follow employment start dates. It includes scheme eligibility constraints such as minimum insured periods, vesting rules, and membership rules. It includes contribution period continuity checks because gaps and overlaps change entitlements. It includes benefit status transition validation—especially around suspensions and reinstatements—because these often hide legacy workarounds. It includes payment constraints such as preventing payments beyond termination unless an authorized override exists. The output of validation should be a rules library with test cases and a clear exception classification, not a pile of “data errors” with no operational meaning.

Deduplication and identity resolution is frequently the most sensitive part of cleansing because it changes how the institution “sees” a person or employer. Deterministic matching can work where unique ID, biometrics, or verified attributes exist, but probabilistic matching becomes necessary where attributes are imperfect. That requires confidence scoring, careful thresholds, and manual adjudication workflows for ambiguous cases. It also requires merge logic that preserves lineage and does not erase evidence. Identity robustness and deduplication are emphasized in ID system guidance precisely because uniqueness and lifecycle accuracy are foundational (World Bank, 2018).

Correction and enrichment is the work of closing gaps. It includes correcting known systemic legacy errors such as misused codes or bad defaults. It can include enrichment from authoritative sources such as civil registry, national ID, or tax systems where legally permitted and where data-sharing agreements exist. It includes documenting irrecoverable gaps and defining policy-compliant treatment, because sometimes the correct answer is not “fill in the blank,” but “define a legal and operational rule for missingness.” It often includes remediation campaigns such as pensioner life certification, employer re-registration, or targeted member record cleanup, which are operational programs, not IT tasks.

Historical integrity and effective dating work ensures that the migration does not overwrite history. It includes preserving event sequences, preventing current values from replacing historical truth, reconstructing periods where the legacy system stored only summaries, and mapping legacy “as-of” semantics into the new model. This work is essential because benefit outcomes depend on history, not just today’s balance.

Reconciliation and auditability is non-negotiable in social security. Record-level reconciliation is required for high-risk cohorts such as pensioners and high-value cases. Totals reconciliation is required by year, scheme, and region. Benefit calculation equivalence testing is required because it proves computational integrity. Payment output equivalence testing is required because it proves payroll safety. Lineage capture from source to transform to target is required because it provides defensible evidence. The deliverable is an auditable reconciliation pack suitable for internal audit, external oversight, and executive signoff.

6.2 The institutional payoff of cleansing

Cleansing is expensive, but it produces durable institutional benefits. It reduces complaints and appeals because citizens encounter fewer contradictions. It enables cleaner interoperability with national ID, tax, and civil registry because the institution can match confidently. It strengthens fraud and error detection because anomalies become visible against a consistent baseline. It improves actuarial and management reporting because measures are based on stable definitions and reliable histories. It increases trust in digital self-service because users see coherent records rather than confusing inconsistencies. In other words, you are not just cleaning for the migration—you are repairing the institution’s data foundation.

7) AI in migration: where it genuinely helps, and where it cannot replace governance

AI is not a substitute for cleansing, but it can be a powerful accelerator once you have clear rules, authoritative sources, a human decision workflow, and auditable controls. GAO’s framing is useful here: AI benefits depend on data quality and workforce capability (GAO, 2025).

High-value AI use cases for social security migrations often begin with assisted entity resolution. AI can propose likely duplicates, rank match candidates, cluster near-duplicates for review, and learn from adjudicated outcomes to improve triage. This can reduce manual work dramatically, but only if the institution maintains an audit trail and prevents silent merges that cannot be defended later.

AI can also support document understanding for legacy archives. Many institutions have decades of paper evidence. AI can extract structured fields from scanned documents, classify document types, and flag inconsistencies between documents and system data. This is especially helpful where staffing is limited and manual data entry is a bottleneck. Yet verification remains essential because evidence-based decisions must be legally defensible, and scanned documents often contain ambiguities or missing pages.

AI-driven anomaly detection can help prioritize remediation. Models can identify contribution patterns that are implausible for a cohort, sudden payment changes that warrant review, or records with high likelihood of identity collision. This is valuable because it helps leadership focus human attention where it matters most, rather than spreading limited capacity across the entire dataset uniformly.

AI can also assist with code mapping when legacy code systems are poorly documented. It can propose mappings based on textual descriptions where they exist, historical usage patterns, and similarity across datasets. However, final mappings must be owned by the institution and tested rigorously, because a wrong mapping can create systemic misclassification that affects entitlement outcomes and reporting.

What AI cannot do is decide legal truth in ambiguous cases, create authority where governance is absent, guess missing history reliably without risk, or remove the need for reconciliation and audit evidence. In social security, defensibility matters as much as accuracy, and governance is not automatable.

8) A practical blueprint for IT directors: how to structure the migration program

A disciplined, defensible migration is built in layers.

The governance layer must come first. Institutions need named data owners and stewards by domain—person, employer, contributions, benefits, payments—with decision rights and escalation paths formalized in a data governance framework (ISSA, n.d.).  This layer defines authoritative sources, survivorship rules, and acceptance criteria so that technical teams can implement stable rules rather than constantly renegotiating truth.

The engineering layer builds the migration factory. It includes immutable extract storage for repeatability, transformation pipelines under version control, automated validation checks, performance architecture for partitioning and parallelization, and complete logging and lineage. The goal is a system that can run again and again, improving each cycle, while generating evidence that leadership can trust.

The operational layer runs remediation like a program, not a side task. It includes remediation campaigns for pensioners, employers, and dependents, exception-handling teams with clear workflows, and communications and customer service readiness for cutover impacts. This is where many migrations fail, because remediation requires institutional coordination and sustained leadership attention, not just technical execution.

The assurance layer proves equivalence before cutover. It includes rehearsal cycles with increasing scope, reconciliation packs and sign-offs, parallel runs for critical payroll cycles, and post-cutover monitoring with rapid rollback contingencies. This layer is what converts migration from hope into controlled risk.

Conclusion: modernization is not “new software”—it is restored data truth

A modern core system can be implemented on time and still fail if the institution migrates poor data without cleansing, governance, and reconciliation. Social security modernization is, at its heart, a credibility project. The new platform must produce outcomes the institution can defend.

The uncomfortable truth—and the most empowering one for leadership—is this: there are no shortcuts in data cleansing. The work is real, measurable, and solvable, but only if it is treated as core institutional transformation rather than an “IT activity near the end.” For many social security IT directors, the opportunity is also enormous: if modernization is used to repair identity quality, employer registers, contribution histories, and payment integrity, then interoperability with national ID and digital payments becomes a genuine leap forward rather than another fragile interface.

References

Government Accountability Office. (2025, April 9). Fraud and improper payments: Data quality and a skilled workforce are essential for unlocking the benefits of artificial intelligence (GAO-25-108412).

International Social Security Association. (n.d.). Guideline 13: Data quality.

International Social Security Association. (n.d.). Guideline 72: Data governance framework.

Social Security Administration Office of Inspector General. (2023, July 31). Numberholders age 100 or older who did not have death information on the Numident (A-06-21-51022).

World Bank. (2025, September 19). Digital delivery systems for social protection (Interoperability and dynamic data sharing).

World Bank. (2018). Guidelines for ID4D diagnostics.