ILR Compliance and Data Quality for Training Providers in England

Every provider delivering publicly funded further education and skills provision in England must submit learner data through the Individualised Learner Record. The ILR captures information about learners, their learning aims, and outcomes, which DfE and devolved authorities use to calculate funding payments, monitor delivery, and produce national statistics.

ILR compliance operates on the principle that the data submitted must accurately reflect actual delivery. Discrepancies between ILR data and learner file evidence create audit risk. Errors in funding model codes, outcome recording, or eligibility fields can result in funding clawback even where delivery was compliant.

ILR Structure and Entities

The ILR uses a data model with entities representing different aspects of the learner and their learning. Understanding this structure helps providers maintain accurate records.

Learner Entity

The learner entity contains basic information about each individual: name, date of birth, sex, ethnicity, postcode, and unique learner number (ULN). Each provider should return one record per learner, regardless of how many learning aims they are undertaking.

The learner's postcode is particularly important as it determines funding responsibility. For learners in devolved areas, the postcode determines which Mayoral Combined Authority funds their provision. Providers must record the learner's home postcode, not the provider's location.

Learning Delivery Entity

Each learning aim undertaken by a learner is recorded as a separate learning delivery record. This includes the learning aim reference, start date, planned end date, funding model, and delivery information.

Multiple learning aims for the same learner are recorded as multiple learning delivery records linked to the single learner record. This allows accurate tracking of learner programs that combine several qualifications or learning aims.

Outcome and Achievement

Learning delivery records must include outcome information once the learner completes, withdraws, or reaches their planned end date. The completion status and outcome grade fields record whether the learner achieved, partially achieved, or did not achieve the learning aim.

Outcome data is essential for funding and performance measurement. For grant-funded providers, completion payments replaced achievement payments from August 2024, but accurate outcome recording remains required.

Funding and Monitoring

The learning delivery funding and monitoring (LDFAM) entity captures funding-related information including source of funding, eligibility markers, and delivery monitoring codes. Correct LDFAM recording ensures accurate funding calculation.

Submission Requirements

Providers must submit ILR data monthly through the Submit Learner Data service. Understanding submission deadlines and requirements helps maintain compliance.

Submission Calendar

The ILR operates on a monthly return cycle from R01 (covering the start of the academic year in August) through R14 (the final year-end return in October). Each return has a submission deadline, typically around the fourth working day of the following month.

Key returns include R04 (November return, used for in-year monitoring), R06 (January return, used for mid-year review), R10 (May return, used for performance assessment), and R14 (October return, used for final funding claim and reconciliation).

Data Validation

The Submit Learner Data service runs validation rules against each submission. Errors are flagged and must be corrected before the file can be accepted. Warnings indicate potential issues that should be reviewed but do not prevent submission.

Validation rules check data format, consistency within records, and some eligibility requirements. However, validation does not check whether data matches learner file evidence—this is an audit function.

File Format

ILR data is submitted in XML format following the published specification. Most providers use management information systems (MIS) that generate compliant XML files. The ILR specification document details all field requirements, valid values, and collection rules.

Common Data Quality Issues

Analysis of ILR data quality reports and audit findings reveals recurring issues that providers should address.

Funding Model Errors

Recording the wrong funding model is a significant compliance issue. Each funding stream has specific rules, and using the incorrect funding model can result in incorrect funding claims.

Common errors include using Adult Skills (funding model 35) when Tailored Learning (funding model 11) is appropriate, recording apprenticeship funding models incorrectly, and failing to update funding models when learners' circumstances change.

Providers should verify the correct funding model for each learning aim against the current funding rules and the Learning Aim Reference Application (LARS) database.

Source of Funding Errors

The Source of Funding (SOF) field must accurately reflect which body funds the learner. For DfE-funded provision (non-devolved areas), SOF 105 applies. For devolved areas, the appropriate MCA SOF code must be used.

SOF errors typically arise from not checking the learner's postcode against the devolution postcode dataset. A learner's home postcode determines their SOF, not the provider's location. Incorrect SOF recording can result in funding being claimed from the wrong body.

Outcome Recording Issues

Missing or incorrect outcome data creates both funding and performance measurement issues. Common problems include failing to record withdrawals with accurate withdrawal dates, recording completion without achievement where achievement should be claimed, and not updating planned end dates when circumstances change.

Outcome data should be recorded promptly when learners complete, withdraw, or reach planned end dates. Delayed outcome recording creates reconciliation issues at year-end.

Prior Attainment Errors

For statutory entitlement provision, prior attainment fields must accurately record the learner's existing qualifications. Claiming full funding for statutory entitlement provision when the learner already holds the relevant attainment level is an audit finding.

Providers should verify prior attainment declarations and, where possible, check against Personal Learning Record data. The prior attainment field should reflect verified attainment, not learner self-report alone.

Postcode and Residency Issues

Learner postcodes must be valid and reflect the learner's actual residence. Invalid postcodes, work addresses instead of home addresses, and outdated postcodes create funding and eligibility issues.

Providers should verify postcodes at enrollment and update records if learners move during their program. The devolution postcode dataset identifies which postcodes are funded by which body.

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Data Quality Tools

DfE provides several tools to help providers monitor and improve ILR data quality.

Provider Data Self-Assessment Toolkit (PDSAT)

PDSAT reports flag potential data errors and anomalies in ILR submissions. Reports identify records that may have errors, unusual patterns that warrant investigation, and potential eligibility issues.

Providers should run PDSAT reports monthly after each ILR submission and investigate flagged records. Addressing PDSAT findings before audit reduces compliance risk.

Funding Information System (FIS)

The Funding Information System allows providers to check how their ILR data translates into funding claims. FIS reports show funding calculations, identify records that generate no funding, and highlight potential issues.

Regular FIS review helps providers identify records where funding is not being claimed as expected, which may indicate data errors.

Post-16 Monitoring Reports Dashboard

The monitoring reports dashboard provides aggregate analysis of provider data quality and performance. Reports cover data completeness, outcome rates, and comparison with sector benchmarks.

ILR and Audit

ILR data forms the basis for funding audit. Auditors compare ILR records against learner file evidence to verify that claimed funding is supported by compliant delivery.

Audit Approach

Auditors typically select a sample of learner records for detailed review. For each sampled record, they check learner eligibility evidence against ILR fields, learning delivery evidence against recorded learning aims and dates, and outcome evidence against recorded achievements and completions.

Common Audit Findings

• Eligibility errors include ILR records claiming funding for learners whose file evidence does not support eligibility. This may involve residency, immigration status, prior attainment, or age.
• Delivery errors include learning aim start or end dates that do not match file evidence, planned hours that do not reflect actual delivery, and completion claims where evidence shows withdrawal.
• Evidence gaps include ILR records with no corresponding learner file evidence, missing enrollment documentation, and incomplete attendance records.

Audit Readiness

Maintaining audit readiness requires that ILR data accurately reflects learner file evidence from the point of data entry. Post-hoc corrections to make data match evidence are less defensible than accurate recording from the start.

Internal sampling and review processes help identify data quality issues before audit. Providers should regularly compare ILR records against source evidence for a sample of learners.

ILR Submission Best Practices

Effective ILR management requires systematic approaches to data quality.

Data Entry Standards

Establish clear standards for data entry, including who is authorized to enter or modify ILR data, verification requirements for key fields (postcode, prior attainment, eligibility), and documentation requirements for changes.

Staff responsible for ILR data entry should understand the funding implications of key fields and the evidence requirements to support data entries.

Regular Review Cycles

Implement regular review cycles including weekly data entry review for new enrollments and changes, monthly PDSAT report review and investigation, termly sample audits comparing ILR data to learner files, and annual reconciliation before R14 submission.

Error Correction Procedures

When errors are identified, establish clear procedures for correction. This includes investigating the source of the error, correcting the ILR record, updating learner file documentation as needed, and identifying systemic issues that may affect other records.

Document all corrections with reasons and authorization. This creates an audit trail showing that errors were identified and corrected through proper processes.

MIS Configuration

Ensure management information systems are configured correctly for current funding rules. This includes updated funding model mappings, current qualification funding validity, current devolution postcode data, and current validation rules.

MIS updates should be tested before the start of each academic year and when in-year changes occur.

How Yotru Supports ILR Compliance

While Yotru is not an ILR submission system, training providers use Yotru to support aspects of learner record management.

• Learner documentation created through Yotru can form part of the evidence base supporting ILR records. Standardized CV development across cohorts creates consistent documentation that supports audit evidence requirements.
• Outcome tracking for employability-related outcomes benefits from systematic approaches. Providers using Yotru's platform for training providers can evidence learner progression in ways that complement ILR destination data.

For providers seeking to improve learner job outcomes, integrated employability support generates documentation that supports both ILR compliance and quality improvement.