In actuarial and financial systems, correctness is necessary — but it is rarely sufficient.
I recently completed an End-of-Service (EOS) valuation system used in a regulated accounting environment. While the mathematics behind EOS is well understood, the real challenge lies elsewhere: data inconsistency, year-over-year drift, regulatory edge cases, and minimizing operational risk.
This post is a short case study on what actually mattered while building the system.
The Core Problem
The system needed to support:
- EOS valuations under multiple actuarial approaches
- IAS 19 compliance, including first-time adoption cases
- Different valuation frequencies (monthly, quarterly, rolling periods)
- Automated disclosures and reports
- Strong data integrity checks to catch upstream HR data issues
Most importantly, it needed to work reliably with real client data, not ideal datasets.
Model Design: Supporting Variations Without Duplication
Instead of building a single monolithic model, the system was designed around explicit model variants, including:
- Age-based mortality models
- Age–service-based mortality models
- Current year, previous year, and adjusted models
- Additional analytical views (Financial Assumptions, Salary Assumptions, Demographic Assumptions, Date of Birth, and Date of Joining based models)
Each model shared a common structural core but had clearly separated assumptions and inputs. This avoided the common trap of “one model with many flags,” which quickly becomes unmaintainable.
IAS 19 First-Time Adoption: A Hidden Complexity
First-time IAS 19 adoption is not just a switch — it changes how historical data is interpreted.
The system explicitly supports:
- Transition-specific logic paths
- Separate handling for prior-period comparisons
- Clear segregation between adopted and non-adopted valuation outputs
This made the system easier to audit and reason about, especially during reviews.
Valuation Periods: Time Is a First-Class Input
Instead of treating time as an afterthought, valuation periods were modeled as first-class inputs:
- Monthly
- Bi-monthly
- Quarterly
- Rolling monthly-to-year valuations
This allowed consistent outputs without duplicating logic for each reporting frequency, a surprisingly common source of errors in financial systems.
Data Integrity Checks: Where Most Real Issues Appear
One of the most valuable parts of the system had nothing to do with calculations.
We implemented employee movement and consistency checks, such as:
- Employees present in last year’s data but missing in the current year
- Employees missing from both the current data and the exit data
- Unexpected movements that often signal upstream HR issues
These checks turn silent data problems into explicit, reviewable queries, reducing downstream valuation risk.
In practice, this saved far more time than optimizing calculations.
Automation with a Human-in-the-Loop
Disclosures and reports are auto-generated to minimize manual work, but the system is intentionally designed for a final human review step.
This balance matters:
- Fully manual → slow and error-prone
- Fully automated → brittle in regulated environments
The goal was review, not rework.
Key Takeaways
A few lessons that stood out:
- In regulated domains, data reliability often matters more than model sophistication
- Explicit model separation beats “configuration-heavy” designs
- Most production issues come from what’s missing, not what’s present
- Good systems assume imperfect inputs and make problems visible early
Final Thoughts
This project reinforced something I’ve seen repeatedly while working with financial and actuarial systems:
Accurate results are important — but trustworthy systems are what survive audits, reviews, and real clients.
If you’re working at the intersection of engineering, finance, or regulation, I’d be happy to exchange notes.
Top comments (0)