Cryptocurrency Investment Fraud Analysis

A cryptocurrency fraud scheme promised 10-15% monthly returns. Over 50,000 investors lost approximately Rs. 1,200 crores. Students will trace blockchain transactions, analyze fund flows, and identify key beneficiaries.

# Aplly.xyz Case Study Submission

## Title
Cryptocurrency Investment Fraud Analysis

## Type
Cybercrime & Digital Forensics

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
A cryptocurrency fraud scheme promised 10-15% monthly returns. Over 50,000 investors lost approximately Rs. 1,200 crores. Students will trace blockchain transactions, analyze fund flows, and identify key beneficiaries.

## Case Details

Function Focus: Cybercrime & Digital Forensics — manual reasoning, decomposition, and critical judgment (no spreadsheet or AI tool permitted in Phase 1)

Scenario:
You are the analyst at a fictional consultancy ("Praxis Advisors") tasked with answering: Cryptocurrency Investment Fraud Analysis. You have a clean, synthetic dataset described below. You must produce a defensible answer using structured reasoning — no tool-assisted shortcut on the first pass.

Dataset Structure:
- case_id
- platform_name
- scheme_type
- investors_count
- total_lost_usd
- promised_return_pct
- duration_months
- promoter_location
- promoter_arrested
- recovery_pct
- countries_involved
- exchanges_used

Tasks:
1. Sort/segment the records to isolate suspicious entities (IPs, accounts, devices). Do this by hand first.
2. Map the connections between entities into a network; identify hubs, bridges, or high-risk clusters.
3. Correlate timestamp/event columns to reconstruct an attack or fraud timeline and locate the first-mover root source.
4. Score or rank candidate entities by risk using the dataset columns, justifying weights explicitly.
5. After your manual pass, re-run the risk ranking in a tool and reconcile any differences.

Expected Output:
A one-page incident analysis memo: suspect/entity shortlist with risk scores, network/timeline map, root-source identification.

Evaluation Criteria:
Soundness of the network/timeline reconstruction, correct risk-scoring methodology, evidence integrity reasoning.

## Data Sources

| case_id | platform_name | scheme_type | investors_count | total_lost_usd | promised_return_pct | duration_months | promoter_location | promoter_arrested | recovery_pct | countries_involved | exchanges_used |
|---|---|---|---|---|---|---|---|---|---|---|---|
| CF001 | CryptoKing | Ponzi | 25000 | 45000000 | 15 | 18 | Dubai | True | 8 | 4 | Binance, KuCoin |
| CF002 | BitYield | High-yield pool | 8500 | 12500000 | 22 | 8 | Thailand | True | 5 | 3 | Binance, Coinbase |
| CF003 | CloudHash Pro | Cloud mining | 12000 | 32000000 | 35 | 24 | Singapore | True | 12 | 5 | Huobi, OKX |
| CF004 | NFT Royalty Club | NFT pool | 3200 | 8500000 | 50 | 6 | India | False | 0 | 2 | WazirX, CoinDCX |
| CF005 | TradeBot AI | Trading bot | 18000 | 28000000 | 28 | 14 | UK | True | 15 | 6 | Binance, Kraken |
| CF006 | DeFi Matrix | DeFi pool | 6500 | 18500000 | 40 | 10 | Switzerland | False | 3 | 4 | Uniswap, PancakeSwap |
| CF007 | MetaCoin Global | MLM | 42000 | 65000000 | 18 | 36 | Malta | True | 2 | 8 | Multiple DEXs |
| CF008 | GreenHash Mining | Green mining | 3500 | 5200000 | 12 | 15 | Canada | True | 20 | 3 | Binance |
| CF009 | AI Arbitrage X | Arbitrage bot | 9500 | 22500000 | 32 | 9 | India | False | 0 | 2 | WazirX, CoinDCX |
| CF010 | P2P LendCoin | P2P lending | 7800 | 14200000 | 25 | 12 | Estonia | True | 10 | 4 | Binance, Kraken |


Full dataset: https://github.com/arora200/aplly_case_db/datasets/062-cybercrime-crypto-fraud (synthetic, 15 records)

## Solution Frameworks
Network/graph analysis, log correlation, kill-chain mapping, anomaly detection, chain of custody & evidence integrity

## Solver Guidance & Tutorials
_Solver guidance added by the pipeline (tutorial links) — see `solver_guidance` field._

## What You'll Learn
- Map relationships into actionable networks
- Reconstruct attack timelines from logs
- Score risk from raw attributes

## Tags
Cryptocurrency Fraud, Blockchain Analysis, Financial Crime, Network Analysis

## Registration Links
Register as Solver / Register as Evaluator