B

Retiree Subscription Auditor

3.05

Derivation Chain

Step 1 Delegating digital administrative and financial procedures
→
Step 2 Difficulty tracking monthly Subscriptions and auto-payments
→
Step 3 Optimizing fixed costs against reduced post-retirement income

Problem

People in their 50s–60s see their income drop after retirement, yet they don't know exactly how much they spend on fixed monthly charges — telecom bills, Insurance premiums, OTT services, app Subscriptions, and auto-debits. Credit card statements only show 'auto-payment,' so figuring out what's being charged requires checking 3–4 card apps and 2–3 banking apps. Canceling requires calling each service's customer center individually. On average, $60–$115 (~8–15만원) per month leaks to unnecessary Subscriptions.

Solution

Users upload their card/bank statement CSV or screenshots, and the system automatically classifies auto-payments and Subscriptions to display: ① total monthly fixed expenses, ② category-level breakdown (telecom / Insurance / OTT / apps / other), ③ cancellation priority ranking (least-used items at top). Each item includes a cancellation method link (customer center number or in-app path).

Target: People aged 55–65, 1–2 years post-retirement, who haven't audited their fixed expense structure despite reduced income, using 3+ credit cards
Revenue Model: Basic analysis (one-time) Free, monthly auto-update + alerts (new auto-payment detected) at $2.20 (~2,900원) Monthly Subscription, cancellation service referral Per Transaction commission
Ecosystem Role: Consumer
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (71%)

Tech Complexity
32.0/40
Data Availability
19.4/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (55/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
10.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Frontend [low] Backend [medium]
Dashboard