Financial AI Layoff Severance Defense Coach

3.50

Derivation Chain

Step 1 Spread of AI-driven layoffs in the U.S. financial sector

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Step 2 Worker rights protection service against AI layoffs

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Step 3 Coaching for optimal severance and unemployment benefit strategies during AI layoffs

Problem

As AI adoption triggers department-level layoffs in finance, insurance, and securities firms, affected employees accept company offers without properly understanding severance calculation methods (DC vs. DB conversion timing), unemployment benefit eligibility, and retirement allowance negotiation. According to the Korea Labor Institute, workers frequently receive 3-8 million KRW (approx. $2,250-$6,000) less due to severance-related errors, and labor attorney consultations cost 200,000-500,000 KRW (approx. $150-$375) per session, making them less accessible.

Solution

By entering basic information such as years of service, salary, and retirement pension type, the service simulates severance amounts for different scenarios and AI coaches the optimal strategy. (1) Simulate severance differences when converting between DB and DC types; (2) compare scenarios of retirement allowance vs. recommended resignation; (3) automatically determine eligibility for unemployment benefits, re-employment allowances, and vocational training costs, and provide application guides.

Target: Employees in their 40s and 50s at large financial, insurance, and IT companies who are aware of layoff risks, and labor union counseling departments.

Revenue Model: Basic simulation free, detailed coaching report 19,000 KRW (approx. $14.25) per report, premium monthly subscription 39,000 KRW (approx. $29.25) (unlimited scenarios + legal update notifications).

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

3.0/5

R Realizability

4.0/5

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.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (76%)

Tech Complexity

34.7/40

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 (55/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

9.0/15

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

Backend [medium] Frontend [low] AI/ML [low]

Dashboard