Retirement Pension Fee Comparator

3.90

Derivation Chain

Step 1 Retirement Pension optimization

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Step 2 Unable to identify fee differences across providers before DC/IRP asset allocation

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Step 3 Same-product fee differences across financial institutions create tens of thousands of dollars in gaps

Problem

People in their 50s managing DC-type Retirement Pensions or IRPs are unaware that total fees for identical TDFs (Target Date Funds) or bond funds vary from 0.3% to 1.2% across financial institutions. Based on 100 million KRW (~$75,000) in pension savings, that is a 300,000-1,200,000 KRW (~$225-$900) annual difference, or 6,000,000-24,000,000 KRW (~$4,500-$18,000) over 20 years. The Financial Supervisory Service comparison disclosure only provides raw data tables, making it impossible to judge 'which institution is best for my situation.' Securities firm consultations only recommend their own products.

Solution

On a web interface, users enter their current pension provider, accumulated savings, and investment preference. The service then: (1) auto-generates a comparison table of total fees (management + sales + custody) across providers for the same product type, (2) visualizes the cumulative 10/20/30-year difference if transferring from current fees to the lowest-fee provider, (3) provides a transfer procedure checklist and estimated processing timeline.

Target: Ages 50-60, holding 50 million KRW (~$37,500) or more in DC-type Retirement Pension or IRP, pre-retirees who are beginning to focus on costs over returns

Revenue Model: Basic fee comparison Free, custom transfer simulation Report (PDF) $3.70 (4,900 KRW) Per Transaction, post-transfer portfolio rebalancing alerts Monthly Subscription $2.90 (3,900 KRW)

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

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 (70%)

Tech Complexity

29.3/40

Data Availability

20.8/25

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