Ghost Subscription Detector

3.40

Derivation Chain

Step 1 Digital asset and account cleanup

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Step 2 Unrecognized recurring payment detection

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Step 3 Integrated cancellation procedure guide across card companies

Problem

A 55-year-old head of household has 12-15 automatic payments registered across 3-4 credit cards accumulated over 10 years, but only actively uses 5-6 of those services. The rest are forgotten Free Trial subscriptions, services a child registered years ago, or residual charges from already-deactivated Platform accounts. 30,000-80,000 won (~$22-60) leaks out monthly as 'ghost payments,' but card company apps only display merchant names, making it difficult to identify which service each charge belongs to. Cancellation procedures vary by service.

Solution

Users upload their credit card transaction history via CSV/Excel, and the service automatically detects recurring payment patterns to provide: (1) a monthly auto-payment overview map, (2) merchant-name-to-service-name mapping with an active usage verification checklist, and (3) service-specific cancellation guides (web/app/phone) with step-by-step instructions and direct links. Includes a cancellation completion tracking checklist.

Target: Heads of household aged 50-60, holding 2+ credit cards, family members each subscribing to various services independently, people who feel their monthly card bill is 'higher than expected'

Revenue Model: Basic analysis (up to 5 items/month) free. Full multi-card integrated analysis + cancellation guide at 3,900 won (~$2.90) Per Transaction. Monthly auto-monitoring (new recurring payment detection alerts) at 2,900 won (~$2.15) Monthly Subscription.

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

3.0/5

M Market

4.0/5

R Realizability

5.0/5

V Validation

3.0/5

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 (73%)

Tech Complexity

29.3/40

Data Availability

23.3/25

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 (51/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

7.5/15

Solo Buildability

5.0/10

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