INSURANCE

Life Insurance Underwriting Automation & APS Processing

Clear the APS backlog without raising mortality exposure.

Life insurance carriers face massive backlogs in policy issuance due to the manual review of lengthy attending physician statements, lab results, and medical history disclosures. By securely uploading these dense medical documents, underwriting operations teams can streamline mortality risk assessment. The platform deploys machine learning PII governance alongside advanced document extraction to redact unnecessary private data while categorizing critical conditions like hypertension or diabetes.

Topic mapping isolates complex medical histories buried deep in physician notes, so a condition mentioned once on page 40 is not the thing that slips through. Underwriters receive deterministic risk summaries with direct citations to the original medical records, enabling faster policy approval turnarounds without increasing mortality exposure.

Base Column
physician_note ▾
Value Column
condition_flag ▾
Base Column physician_note ×Value Column ×
38%ofphysician_note=cardiac_history
flag for review
mortality impact
100%
flagged count
494
statement count
1,300
see examples
23%ofphysician_note=metabolic_marker
flag for review
mortality impact
69%
flagged count
299
statement count
1,300
see examples

Document Digitization

Use Steeped AI's document digitization to convert multi-page PDF medical records and handwritten doctor notes into clean, machine-readable text. An APS that arrives as a fax becomes a structured field in the same dataset as the lab panel.

APS RECORDSTRUCTURED FIELDSCondition: cardiacOnset: 2019Rx: statinRedacted: yes

Data Preparation & ML PII Detection

Use Steeped AI's data preparation to automatically scan and redact non-essential personal identifiers, keeping the file compliant with strict medical privacy law. Every flagged record surfaces for review before removal, so underwriting stays in control of what leaves the dataset.

Flagged 6,200 rows with non-essential PII for your review
Addresses and contact details in notes were redacted
Policy numbers were masked but kept joinable
Removed column "raw_fax_ocr" because 77% were blank
Retention tags were applied to every source file
generating clean dataset
SAFE DATA

AI Topic Mapping

Use Steeped AI's AI topic mapping to categorize medical conditions, family health histories, and prescription usage buried within unstructured physician notes. What a reviewer might miss on the fortieth page becomes a countable field on every file.

cardiac historymetabolic markerfamily history

Relational Metrics

Steeped AI's automated metric breakouts cross-tabulate condition clusters, applicant age bands, and lab markers against issued mortality class without a manual pivot table. Real deterministic math runs across every meaningful column pair before an underwriter opens the file.

Expand
Condition × Mortality Class
Cardiac + Age 55+
38%
Metabolic Marker
29%
Tobacco History
23%
Family Onset
15%
Base Column
condition_flag ▾
Value Column
mortality_class ▾
38%ofCardiac + 55+=Rated Class
predictive power
89%
rated count
114
case count
300
see examples

Talk to Your Data

Steeped AI's talk to your data lets an underwriting lead ask which condition combinations are slowing approvals and get ranked, cited findings back instantly. Answers are pulled from the real case record, never generated.

Reporting & Citations

Steeped AI's reporting and citations produces the risk summary itself, with every conclusion carrying a numbered citation back to the exact line of the physician statement behind it. A reinsurer or auditor can trace each rating decision to its source.

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The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.