A
Health Insurance Premium Spike Prevention Calendar
4.25
Derivation Chain
Step 1
Clarity Act — virtual asset income taxation becomes a reality
→
Step 2
Risk of losing dependent status under health insurance as investment income increases
→
Step 3
Need to predict the point when monthly premiums surge 3-5 times upon loss of dependent status and conversion to regional health insurance
Problem
When early retirees around age 55 or their spouses are registered as dependents under their children's workplace health insurance, if financial income (interest, dividends, virtual asset gains) exceeds KRW 20,000,000 (approx. $15,000) per year, dependent status is revoked and they are forcibly converted to regional health insurance. This causes monthly premiums to surge from under KRW 100,000 (approx. $75) to KRW 400,000-800,000 (approx. $300-$600). Due to the time lag between income generation and dependent status assessment, they are notified suddenly. With the implementation of virtual asset taxation (2026), the number of people in their 50s facing this issue is expected to surge, but there is no tool to track in real time how close their total financial income is to the threshold.
Solution
When users enter their expected annual financial income (interest, dividends, virtual asset capital gains, rental income) by category on the web, the service (1) displays in real time the remaining amount before losing dependent status, (2) provides a monthly income calendar that warns 'if you sell at this point, you exceed the threshold,' and (3) simulates the expected monthly health insurance premium if status is lost. It also compares timing adjustment scenarios such as 'how much you save by postponing the sale to next year.'
NUMR-V Scores
NUMR-V Scoring System
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
N=.15 U=.20 M=.15 R=.30 V=.20
Feasibility (62%)
Data Availability
18.3/25
Feasibility Breakdown
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
Market Validation (67/100)
Validation Breakdown
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |
Technical Requirements
Frontend [medium]
Backend [medium]