Predictive analytics can tell you where to look. Auto.IES helps you understand what may actually be wrong.

Traditional risk-scoring and predictive analytics solutions can help identify cases that look unusual or are statistically more likely to contain an error. That is useful—but it is only the beginning of the review. A risk score still leaves a worker with important questions:

• What is actually wrong?

• Why does it matter?

• What evidence supports the finding?

• What policy applies?

• What should I review before I certify this case?

Auto.IES™ is designed to automate the answer to those questions.

Auto.IES is PCG’s AI-enabled Eligibility Assurance service. It reviews structured case information, documents, verification results, case activity, and applicable policy to identify potential eligibility and procedural issues before a determination is finalized. For each finding, Auto.IES can provide the worker with supporting evidence, confidence information, relevant policy references, and the context needed to make an informed decision. The worker and agency remain in control of the final eligibility determination.

Traditional Risk Detection Solutions

Risk detection solutions are designed primarily to identify patterns, anomalies, and cases that warrant additional attention.

They can:

• Flag high-risk cases
• Identify statistical outliers
• Prioritize cases for review
• Detect patterns across historical data
• Tell workers where they may need to look

But identifying risk can also create another queue for already-busy eligibility staff. Workers may still need to reopen the case, review documents, research policy, validate the finding, determine whether an actual error exists, and decide what action is appropriate. Risk detection simply identifies the question.

Auto.IES Eligibility Assurance

Auto.IES is designed to move beyond the risk score and evaluate the eligibility decision itself.

Auto.IES can:

• Identify specific potential eligibility and procedural issues
• Analyze structured and unstructured case information
• Evaluate supporting documentation and evidence
• Apply configured policy rules, calculations, analytics, and AI
• Explain why a potential issue matters
• Provide supporting evidence and policy references
• Assess confidence and potential risk
• Support corrective action before certification
• Capture an audit-ready record of the review
• Preserve human and agency decision authority

Auto.IES answers the question in advance.

Better Accuracy Should Not Mean More Work

Rather than simply generating another list of cases to investigate, Auto.IES is designed to perform much of the initial review automatically and return focused, evidence-backed findings to the worker. The result:

Solve Today’s Error. Improve Tomorrow’s Program.

Risk detection is often focused on the immediate question:

Which cases should we investigate?

Auto.IES is designed to help agencies answer a larger strategic question:

Why are eligibility errors happening, and how can we prevent them from happening again?

By aggregating findings and worker outcomes across cases, Auto.IES can help identify recurring:

• Error categories
• Policy interpretation issues
• Documentation gaps
• Data-quality problems
• Workflow friction
• Training opportunities
• Verification weaknesses
• System and interface issues

This creates a continuous improvement cycle:

Detect → Explain → Correct → Measure → Improve

The goal is not simply to catch more errors. It is to build a stronger eligibility operation over time.

Don’t Just Predict Risk. Prevent Wrong Eligibility Decisions.

See Auto.IES in action → Request a demonstration