A

Ticket Scam Detection Alert Bot

3.65

Derivation Chain

Step 1 BTS Gwanghwamun concert proxy booking fraud
→
Step 2 Major concert ticket trading platforms
→
Step 3 Automated ticket transaction fraud detection service

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Ticket fraud — including proxy bookings and counterfeit ticket sales — is surging around major concerts like BTS, yet individual buyers have no way to distinguish scam listings from legitimate ones on secondhand platforms (Bungaejangter, Karrot, etc.). With average ticket prices of ~$75–225 (100,000–300,000 KRW), victims lose not only money but also the chance to attend the show, causing significant emotional distress. Police monitor after the fact, but real-time prevention is nonexistent.

Solution

Real-time crawling of ticket listings on secondhand trading platforms to detect fraud patterns (blacklisted bank accounts, price anomalies, new accounts, repeated similar phrasing), delivering risk alerts like 'This listing has an 87% scam probability' via KakaoTalk/Telegram bot. Combines community report data to build a proprietary fraud database.

Target: Individual consumers in their 20s–30s within K-POP fandoms who purchase concert tickets through secondhand trading, with at least one ticket transaction per month
Revenue Model: Premium Subscription ~$2.15/month per account (2,900 KRW) for real-time alerts + unlimited fraud DB lookups. Free tier limited to 3 lookups/day. 20% discount for Annual Subscription.
Ecosystem Role: Infrastructure
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
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.
N=.15 U=.20 M=.15 R=.30 V=.20

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 (50/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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

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