Life Insurance Data Operations Consulting That Starts at the Policy Record
We rebuild the policy data under experience studies and valuation, so reserves rest on records that agree.
Life insurance data operations consulting rebuilds the policy data behind experience studies, assumption reviews and valuation runs, so a life or annuity carrier’s reserves rest on one trusted record per policy instead of extracts that disagree. KORE1 also places the data engineers and analysts who keep that record clean, and 92% of KORE1 placements remain with the client a year on.
Last updated: September 17, 2026
The carriers that call us mostly run life and annuity platforms somewhere between $5 billion and $50 billion. Some are PE-owned. Nearly all of them have bought a block or two, and those policies still live in whatever admin system came with the deal.
Studies start from extracts. An analyst pulls five files, spends weeks making them agree, and the lapse and mortality results land right on top of the assumption review. That’s a data problem sitting under an actuarial deliverable, which is why KORE1 runs this work beside our insurance IT staffing practice and the people it places. New software won’t fix it.
One Life Policy, as Five Files Remember It
A composite specimen. Not a real policy. It shows the disagreements KORE1 consultant Khurram Tehseen reads for before trusting any study result, on one 20-year level term policy in the month its level premium period ended.
Specimen policy · issued April 1, 2006 · as each file held it in April 2026
| File | Date of birth | Issue date | Face amount | Status | Plan |
|---|---|---|---|---|---|
| Application, signed 2006 | 07/19/1961 | Not yet issued | $500,000 | Applied | 20-year level term |
| Policy admin system | 07/19/1961 | 04/01/2006 | $500,000 | In force, in grace | T20L |
| Valuation extract | 07/19/1961 | 04/01/2006 | $500,000 | In force | 217 |
| Reinsurance bordereau | 07/19/1966 | 04/01/2006 | $250,000 | In force | TERM20 |
| Billing file | 07/19/1961 | 03/02/2006 | $500,000 | Lapsed | T20L |
| Canonical record | 07/19/1961Trusted | 04/01/2006Trusted | $500,000 direct, $250,000 cededTwo fields | In grace periodUncertain | 217, 20-year level termMapped |
- Disagrees with the canonical record
- Right number, different meaning
- The record every study reads
- Uncertainty tag, held until the facts settle
Read the Status column first. A lapse study built on the billing file counts this policy as lapsed in April, while one built on the admin system counts it in force. That split lands right where lapse rates jump, and in the Society of Actuaries’ 2021 post-level term lapse study shock lapses at the end of the level period ran from 27% to 96% depending on the premium increase, with annual-mode policies lapsing well above monthly ones. Forty thousand policies like this one will move a reserve.
The rest fails quietly. The reinsurer keyed a birth year wrong and reports only its ceded share, the billing file stores the application date as the issue date, and three systems use three names for one plan. None of it is an actuarial question, but all of it lands in the study.
Rerun Last Year’s Lapse Study Before Changing an Assumption
At one global life insurer, experience studies ran on extracts and took a long time to deliver. Khurram built that carrier’s first predictive lapse study on a new data pipeline. Reserve accuracy improved by $754 million, $254 million of it from lapse alone, and study delivery time fell 25%.
Proof comes from the past. We rerun a study the carrier already finished, on the same policies and the same period, through the rebuilt pipeline. Then it gets scored against the result that went into the last assumption review.
- Exposure that ties outPolicy counts and face amounts reconcile to the valuation file before a single rate gets calculated.
- Every status change datedLapse, grace, reinstatement and conversion each carry a date the study can count on.
- One automation liveA working piece of the pipeline in production before the four-week diagnostic ends.
The model comes later. Sometimes much later, and sometimes with a machine learning engineer or a Python developer attached. Khurram brings the rule he uses on private credit closes straight across. Scorecard now, ML when your data can support it.
Valuation and Filings Read the Same Policy Record
The study is one reader. Valuation reads the same policy data, and so do regulators and reinsurers, each on a separate clock.
- Each reporting dateThe discount rate behind the liability for future policy benefits gets updated, under FASB’s ASU 2018-12 for long-duration contracts.
- At least once a yearCash flow assumptions are reviewed, and updated if something has changed.
- By Sept. 30Carriers with $50 million or more of direct individual life premium submit mortality experience under VM-51 of the NAIC Valuation Manual.
- 30 days, then Feb. 28The experience reporting agent flags possible errors within 30 days of a submission, and corrections are due by Feb. 28 of the next year.
Four clocks. One record underneath.
The manual expects bad data. For policies issued before 1990, VM-51 lets a company certify hardship when fields aren’t readily available in its systems or in legacy computer systems still used for older business. Block buyers know them.
Each deadline rereads the record. Fix the record once and every deadline gets easier, which is why we build it before anyone adds a data warehouse or hires more SQL developers to write extracts.
What Breaks First in Life Insurance Data Operations
Carriers rarely call about data. They call about these.
The lapse study lands after the review
Results arrive so close to the assumption review that nobody has time to question them before they’re used.
Three admin systems from three deals
Every acquired block kept its own system, so one study means three extracts and three sets of status codes.
The bordereau never ties to the admin file
Ceded amounts, birth dates and plan codes disagree, and settling them eats days out of every quarter.
One analyst knows the extract logic
The joins, the exclusions and the plan mapping live in one person’s head and one folder of SQL.
Annuity benefit payments reached $110 billion in 2024, the most ever per the ACLI’s 2025 Life Insurers Fact Book, and each check was drawn against a policy record. LIMRA counted more than 740,000 plan participants moved through pension risk transfer deals in 2025, each one arriving as a census somebody has to clean. Balance sheets keep shifting too. The Fed’s March 2025 study of life insurers and risky corporate credit found that insurers with affiliated asset managers controlled $4.1 trillion of general account assets at the end of 2023, about 72 percent of the industry, through partnerships the authors call complex and arguably opaque.
When the gap is people rather than architecture, KORE1’s insurance staffing desk covers the business side, our financial services IT recruiters handle admin system and integration roles, and compliance analysts can own the filing calendar while the data under it gets rebuilt. Our read on insurtech hiring in 2026 tracks which insurance roles are growing and which are shrinking, and the rest of the technology stack runs through KORE1 IT staffing services.
Keeping the Policy Record Honest After Handover
A rebuilt pipeline that only its builder understands is the old problem in a new place. Owners get named first.
Two seats carry it. The engineering seat holds the pipelines, the validation rules and the plan-code map. The study seat works the exception list, deciding whether a flagged status or an odd birth date changes anything the actuaries will see, and it can sit with an analyst who already knows your blocks. For the engineering side we recruit through staff augmentation for data engineering teams or a direct data engineer or ETL developer hire, while designing the canonical record itself is data architect work and a data governance analyst often ends up owning the rules.
Engineers who have shipped inside a regulated company settle in faster, for reasons our piece on engineering leadership in regulated industries lays out. Some carriers want one senior owner. Many begin with a fractional head of data on contract terms, well before anyone opens a nine-month search. When a permanent seat is the right answer, that becomes a chief data officer search of its own.
Khurram tests AI readiness bluntly. Could next year’s study still run if the analyst who wrote the extracts took a month off? If the honest answer is no, the carrier isn’t ready, whatever software it owns.
How We Rebuild Life and Annuity Data, Step by Step
Five steps. The first uses a study you’ve already filed, so every later step has something real to be scored against.
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1
Replay a finished study
We rerun a completed lapse or mortality study on the rebuilt pipeline and score it against the filed result.
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2
Write the policy record
One canonical record per policy, with rules for trusted fields and a visible uncertainty tag on the rest.
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3
Map every plan and status code
Each admin system’s codes resolve to one plan list and one set of status events a study can count.
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4
Seat the two owners
An engineer takes the pipeline, a study analyst takes the exceptions, and both start before we step back.
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5
Run the next review on it
The following assumption review uses the rebuilt data and gets compared with the replay from step one.
Common Questions
What does life insurance data operations consulting cover?
It covers the data work between a policy admin system and an actuarial result, including extracts, policy-level reconciliation, status and plan-code mapping, and the pipelines that feed experience studies, valuation and regulatory filings. Judgment stays with your actuaries.
Can’t our admin system vendor or TPA fix the data?
Inside its own system, yes. The disagreements that hurt a study sit between systems, in the reinsurance bordereau, the billing file and the valuation extract, and no single vendor owns that gap.
Should a new actuarial or data hire come before this work?
Usually not. A new hire inherits the same five files and spends the first year reconciling them, so we scope the record first and let the diagnostic show whether the long-term owner is a data engineer, an analyst or a fractional data leader.
How soon does something work?
Four weeks for one piece. The Data Foundation Diagnostic maps where a study’s time goes and ships one working automation before it ends, while a full rebuild across several acquired blocks runs longer.
Can AI read medical records for underwriting reliably?
For defined fields with a rule check behind every value, yes. An engine Khurram built to read medical records cut underwriting cycle time by 33% at 99.9% decision accuracy, and a person still approved each mortality rating it drafted.
Does this help with a block acquisition or a PRT quote?
It does. Khurram calls every block acquisition a data project wearing an M&A costume, and a PRT quote is the same problem with a census instead of a policy file. A block’s data cleanse sits on the deal’s critical path, and a pension risk transfer quote waits on a census that has to be scrubbed before anyone can price it.
Can KORE1 hire the people who maintain it?
Yes. KORE1 recruits both seats, the engineer who maintains a rebuilt pipeline and the experience study analyst who works its exceptions, on contract or direct hire terms, and has recruited technology and finance talent since 2005.
Start With the Study You Filed Last Year
Tell us which study took longest and which file you trust least. We’ll tell you plainly whether replaying it would pay off.
Talk Through Your Next Study →