Organizational Corruption Early Warning Scanner

2.50

Derivation Chain

Step 1 Research on normalization of corruption within organizations

→

Step 2 Corporate compliance department demand for corruption prevention

→

Step 3 Automated corruption indicator detection + internal control audit automation

→

Step 4 Auto-generated internal control self-assessment Reports

Problem

Compliance departments (2–5 people) at mid-size companies (100–500 employees) need to proactively detect internal corruption signals, but manually reviewing expense claims, purchase orders, and HR transfer data takes 40–60 hours per month. Corruption patterns that become 'normalized' (repeated just-under-limit approvals, vendor concentration in purchase orders, etc.) are nearly impossible for humans to catch, and by the time issues are discovered, cumulative damages have already reached tens of thousands to hundreds of thousands of dollars.

Solution

Integrate expense, procurement, and HR data from ERP/accounting systems to automatically detect corruption indicator patterns (split approvals just under limits, weekend/after-hours approvals, vendor concentration in orders, abnormal approval chains, etc.). Auto-generate monthly internal control audit Reports and send immediate alerts to compliance officers when anomalies are detected.

Target: Compliance and audit department staff at mid-size companies with 100–500 employees

Revenue Model: SaaS Monthly Subscription at ~$149/month per company (up to 300 employees), ~$224/month for 500+ employees, 20% discount for Annual Subscription

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

2.0/5

R Realizability

2.0/5

V Validation

2.0/5

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 (68%)

Tech Complexity

29.3/40

Data Availability

18.8/25

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

Dashboard