B

AI Fake Applicant Detection Screener

2.85

Derivation Chain

Step 1 North Korean hacking groups using AI tools for employment fraud
→
Step 2 Identifying fraudulent applicants in corporate hiring
→
Step 3 Automated anomaly detection for remote hiring applicant identity and credentials

Problem

IT company HR managers need to identify fraudulent applicants (fake credentials, AI-generated portfolios, stolen identities) in remote hiring, but verifying each resume/portfolio takes an average of 2-4 hours per applicant. For companies hiring 50+ candidates per month, verification staffing costs reach KRW 3-5 million (~$2,250-$3,750)/month. If a fraudulent hire infiltrates the company, internal system breaches can cause damages in the hundreds of millions of won.

Solution

Automatically cross-checks applicant resumes, portfolios, and GitHub profiles to score anomaly indicators (credential inconsistencies, AI-generated content detection, timezone mismatches, duplicate application patterns). Flags high-risk applicants, auto-generates additional verification questions, and provides real-time video interview behavioral analysis (eye tracking, response delay patterns).

Target: HR teams at IT companies with 50-300 employees conducting remote/hybrid hiring, companies using HR SaaS platforms
Revenue Model: Per Transaction billing at KRW 5,000 (~$3.75)/applicant screening. Monthly Subscription KRW 490,000 (~$367)/unlimited (for companies screening 100+ per month)
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
2.0/5
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 (72%)

Tech Complexity
29.3/40
Data Availability
23.1/25
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 (52/100)

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
3.0/10
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

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