B

Retirement Pension DB-to-DC Switch Analyzer

3.75

Derivation Chain

Step 1 Retirement Pension and personal pension optimization
→
Step 2 Calculating projected payout differences for DB-to-DC conversion
→
Step 3 Post-conversion DC investment strategy simulations

Problem

When mid-50s employees at large companies are advised to switch from DB (Defined Benefit) to DC (Defined Contribution) Retirement Pension plans, the difference in net payout can reach tens of millions of KRW (~tens of thousands of USD) depending on remaining years of service, projected severance, and DC investment returns — yet no comparison tool exists. Bank consultations push their own products, and HR departments only explain the system without providing personalized advantage/disadvantage analysis, leaving employees to decide by gut feeling or postpone the switch.

Solution

Users input their current salary, years of service, and expected retirement date to automatically compare projected payouts between staying on DB vs. switching to DC. For DC conversion, the tool provides simulations across conservative, balanced, and aggressive portfolio strategies with after-tax net payout figures. A decision-making checklist can be downloaded as a PDF.

Target: Office workers aged 50–58 at large or mid-sized companies, 3–7 years from retirement, currently enrolled in DB-type Retirement Pension
Revenue Model: Basic comparison Free. Detailed simulation Report (investment strategy combinations + tax calculations) PDF download at 5,000 KRW (~$3.75) Per Transaction. Future IRP (Individual Retirement Pension) account referral commissions.
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
4.0/5
V Validation
4.0/5
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 (68%)

Tech Complexity
29.3/40
Data Availability
18.3/25
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 (58/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
10.5/15
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 [medium] Backend [medium] Data Pipeline [low]
Dashboard