Hawala Money Transfer Network Analysis

The Enforcement Directorate busted a hawala network facilitating Rs. 1,000 crores transfers between Delhi, Mumbai, Dubai, and Hong Kong. Students will reconstruct transaction flows and identify key operators.

# Aplly.xyz Case Study Submission

## Title
Hawala Money Transfer Network Analysis

## Type
Fraud Pattern Analysis

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
The Enforcement Directorate busted a hawala network facilitating Rs. 1,000 crores transfers between Delhi, Mumbai, Dubai, and Hong Kong. Students will reconstruct transaction flows and identify key operators.

## Case Details

Function Focus: Fraud Pattern Analysis — 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: Hawala Money Transfer Network 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:
- transaction_id
- date
- sender_name
- sender_location
- receiver_name
- receiver_location
- amount_rs
- currency
- intermediary_count
- route
- purpose_stated
- bank_involved
- layer_number
- flagged_pattern
- investigation_status

Tasks:
1. Profile the distribution of key numeric columns; identify outliers, tail values, or groups that look systematically different.
2. Flag candidate fraudulent records by rule-of-thumb thresholds you define yourself. Write the rules down before applying them.
3. Look for coordinated patterns (shared attributes, networks, repetition) that single-record checks would miss.
4. Estimate the financial exposure of the flagged set and state your confidence and the biggest assumption.
5. Only after the manual pass, compare your flags against an automated model/threshold and explain the gaps.

Expected Output:
A one-page fraud assessment memo: flagged record list with rules, network/pattern evidence, exposure estimate, confidence statement.

Evaluation Criteria:
Correctness of threshold rules, detection of coordinated patterns, accuracy of exposure estimate, honest confidence statement.

## Data Sources

| transaction_id | date | sender_name | sender_location | receiver_name | receiver_location | amount_rs | currency | intermediary_count | route | purpose_stated | bank_involved | layer_number | flagged_pattern | investigation_status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| H001 | 2026-01-10 | Rajesh K. | Delhi | Ahmed M. | Dubai | 2500000 | INR/USD | 3 | Delhi-Dubai-Mumbai | Trade payment | True | 1 | Structured deposits | Under investigation |
| H002 | 2026-01-12 | Priya S. | Mumbai | Hassan R. | Sharjah | 1800000 | INR/USD | 2 | Mumbai-Sharjah-Delhi | Family remittance | True | 1 | Rapid round-tripping | Active case |
| H003 | 2026-01-15 | Vikram P. | Chennai | Omar K. | Kuala Lumpur | 3200000 | INR/MYR | 4 | Chennai-Singapore-KL-Mumbai | Export payment | False | 2 | Multiple beneficiaries | Active case |
| H004 | 2026-01-18 | Anita G. | Kolkata | Faisal A. | Dhaka | 950000 | INR/BDT | 2 | Kolkata-Dhaka-Delhi | Medical treatment | False | 1 | None | Closed - legitimate |
| H005 | 2026-01-20 | Sunil J. | Jaipur | Abdullah Z. | Riyadh | 4200000 | INR/SAR | 5 | Jaipur-Dubai-Riyadh-Mumbai-Delhi | Real estate investment | True | 3 | Layering detected | Active case |
| H006 | 2026-02-01 | Deepak M. | Mumbai | Karim B. | Istanbul | 2800000 | INR/TRY | 3 | Mumbai-Istanbul-Dubai | Gold trade | True | 2 | Trade-based ML | Under investigation |
| H007 | 2026-02-05 | Meena L. | Bangalore | Tariq H. | Muscat | 1500000 | INR/OMR | 2 | Bangalore-Muscat-Mumbai | Education fees | True | 1 | None | Closed - legitimate |
| H008 | 2026-02-10 | Rohit V. | Ahmedabad | Yusuf N. | London | 5600000 | INR/GBP | 4 | Ahmedabad-Dubai-London-Mumbai | Business investment | True | 3 | Shell company trail | Active case |
| H009 | 2026-02-15 | Kavita D. | Pune | Imran S. | Doha | 1200000 | INR/QAR | 2 | Pune-Doha-Mumbai | Medical expense | False | 1 | None | Closed - legitimate |
| H010 | 2026-02-20 | Manish T. | Surat | Bilal K. | Hong Kong | 7800000 | INR/HKD | 6 | Surat-Dubai-HK-Singapore-Mumbai | Diamond trade | True | 4 | Complex layering | Active case |


Full dataset: https://github.com/arora200/aplly_case_db/datasets/068-fraud-hawala-network (synthetic, 15 records)

## Solution Frameworks
Pattern & anomaly detection, network/graph analysis, risk scoring, layered defense review, distribution/tail analysis

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

## What You'll Learn
- Spot outliers and coordinated patterns by hand
- Write and test your own detection rules
- Estimate financial exposure honestly

## Tags
Hawala, Financial Crime, Network Analysis, Money Laundering

## Registration Links
Register as Solver / Register as Evaluator