Year-End Tax Settlement Retroactive Optimizer

3.50

Derivation Chain

Step 1 Digital administrative & Tax Accounting delegation

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Step 2 Problem of annual dependency on tax accountants

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Step 3 Problem of identifying tax-saving opportunities proactively without a tax accountant

Problem

Dual-income workers aged 45-55 discover during year-end tax settlement season what they could have done differently throughout the year to reduce taxes. Strategies like consolidating medical expenses under one spouse, optimizing credit card vs. debit card spending ratios, and making additional pension savings contributions are only effective when executed from January through the year — yet taxpayers only hear 'do this next year' advice from tax accountants in December. This timing gap costs households ₩500K-2M (~$375-$1,500) in missed tax savings annually.

Solution

Upload your previous year's tax settlement PDF (or HomeTax simplified data) to generate a monthly 'tax-saving action list for the remaining year.' First, analyzes prior year data to show deduction limit achievement rates for medical expenses, Education costs, donations, and pension savings. Second, calculates the optimal card usage strategy (credit card 25% threshold → debit card switch timing) for the remaining period. Third, sends monthly action reminders.

Target: Dual-income workers aged 45-55, annual income ₩50-100M (~$37,500-$75,000) range, households with spouse, children, and dependent parents creating complex deduction scenarios

Revenue Model: Basic analysis Free (3 categories), full category analysis + monthly alerts Annual Subscription ₩19,900 (~$15), tax accountant 1:1 consultation referral brokerage fee

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

4.0/5

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.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity

29.3/40

Data Availability

20.6/25

MVP Timeline

20.0/20

API Bonus

0.0/15

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 (56/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

7.0/10

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] Data Pipeline [low]

Dashboard