preetisheoran/nfpc-parquet-dataset

AML Mule Account Detection Challenge

Data Format: All files are in Apache Parquet format (Snappy compression). Use pandas.read_parquet(), pyarrow.parquet, or any Parquet-compatible reader. Transaction files are split across batch-N/ subdirectories.

Primary Objective/Problem Statement

Identify mule accounts used for money laundering from banking transaction and account data. Given labelled training data and unlabelled test accounts, predict which test accounts are mules.

Evaluation

• Exact Submission Guidelines will be provided shortly
• Evaluation Criteria is listed below:
  • 40% weightage for Model/Feature Ingenuity
    • Rewarded for creative and innovative ideas in types of models/features used, multiple models/algorithms in series/parallel may be used to generate final predictions
  • 20% weightage for model performance scores
    • Rewarded for high accuracy/precision scores, calculated scores such as AUC-ROC and F1 scores
  • 15% weightage for avoidance of red-herrings in data
    • Rewarded for succesfully avoiding several red-herrings injected in the training data
  • 15% weightage for additional insights such as Temporal IOU scores, other insights
  • 10% weightage for report quality
    • Rewarded for clear and concise insights presented in the report with data-driven logic.

Data Files

Provided to You

File	Rows	Size	Description
customers.parquet	159,000	2.3 MB	Customer demographics and KYC information
accounts.parquet	160,000	6.7 MB	Account-level attributes
transactions/	~400,000,000	8.2 GB	Individual transaction records (5-year window: Jul 2020 - Jun 2025), 396 parts in 4 batches
demographics.parquet	~159,000	4.6 MB	Customer demographics (name, gender, address, phone)
accounts-additional.parquet	~160,000	0.9 MB	Scheme codes per account
transactions_additional/	~400,000,000	8.4 GB	Extended transaction fields, 311 parts in 4 batches
branch.parquet	~9,000	0.3 MB	Branch-level metadata
customer_account_linkage.parquet	160,000	1.8 MB	Maps customers to their accounts
product_details.parquet	159,000	2.9 MB	Aggregated product holdings per customer
train_labels.parquet	96,000	0.6 MB	Training labels — is_mule: 1 = mule, 0 = legitimate
test_accounts.parquet	64,000	0.4 MB	Account IDs you need to predict on

Total: 720 files, 16.2 GB

Note: transactions/ and transactions_additional/ are split into batch subdirectories (batch-1/, batch-2/, etc.) with up to 100 part files each. To read all parts:

import pandas as pd
from glob import glob

# Read all transaction parts
parts = sorted(glob("transactions/batch-*/part_*.parquet"))
df = pd.concat([pd.read_parquet(p) for p in parts], ignore_index=True)

Schema

customers.parquet

Column	Description
customer_id	Unique customer identifier
date_of_birth	Date of birth (YYYY-MM-DD)
relationship_start_date	Date customer relationship began
pan_available	PAN card on file (Y/N)
aadhaar_available	Aadhaar on file (Y/N)
passport_available	Passport on file (Y/N)
mobile_banking_flag	Mobile banking registered (Y/N)
internet_banking_flag	Internet banking registered (Y/N)
atm_card_flag	ATM/debit card issued (Y/N)
demat_flag	Demat account linked (Y/N)
credit_card_flag	Credit card held (Y/N)
fastag_flag	FASTag linked (Y/N)
customer_pin	Residential PIN code
permanent_pin	Permanent address PIN code

accounts.parquet

Column	Description
account_id	Unique account identifier
account_status	active or frozen
product_code	Product code
currency_code	Currency (1 = INR)
account_opening_date	Date account was opened
branch_code	Branch identifier
branch_pin	Branch location PIN code
avg_balance	Average balance (can be negative for overdraft)
product_family	S (Savings), K (K-family), O (Overdraft)
nomination_flag	Nominee registered (Y/N)
cheque_allowed	Cheque facility available (Y/N)
cheque_availed	Cheque book opted (Y/N)
num_chequebooks	Number of cheque books issued
last_mobile_update_date	Date of last mobile number change
kyc_compliant	KYC compliant (Y/N)
last_kyc_date	Date of last KYC verification
rural_branch	Rural branch (Y/N)
monthly_avg_balance	Monthly average balance
quarterly_avg_balance	Quarterly average balance
daily_avg_balance	Daily average balance
freeze_date	Date the account was frozen (null if never frozen)
unfreeze_date	Date the account was unfrozen (null if never unfrozen)

Note: customer_id is not in this file. Use customer_account_linkage.parquet to join accounts to customers.

transactions (partitioned)

Column	Description
transaction_id	Unique transaction identifier
account_id	Account the transaction belongs to
transaction_timestamp	ISO format timestamp
mcc_code	Merchant Category Code
channel	Transaction channel (see below)
amount	Amount in INR (negative values indicate reversals)
txn_type	D (Debit) or C (Credit)
counterparty_id	Counterparty identifier

Transaction channels: UPC (UPI Credit), UPD (UPI Debit), END (E-commerce/POS), IPM (IMPS), STD (Standing instruction debit), P2A (Pay-to-account), FTD (Fund transfer debit), NTD (NEFT debit), MCR (Mobile credit), FTC (Fund transfer credit), MAC (Mobile app), TPD (Third-party debit), APD (Auto-pay debit), CHQ (Cheque), ATW (ATM withdrawal), TPC (Third-party credit), STC (Standing instruction credit), OCD (Over-counter deposit), RCD (Recurring deposit credit), IFD (Internal fund debit), ETD (Electronic transfer debit), NWD (Network debit), CSD (Cash deposit), IFC (Internal fund credit), PCA (Payment card authorization), MAD (Mandate debit), CHD (Clearing house debit), RTD (Return debit), CCL (Credit card linked), OPI (Online payment initiation), CTC (Clearing transfer credit), SID (System-initiated debit), ASD (Auto-sweep debit), IAD (Inter-account debit), SCW (Smart card withdrawal).

customer_account_linkage.parquet

Column	Description
customer_id	Customer identifier
account_id	Account identifier

A single customer may hold multiple accounts.

product_details.parquet

Column	Description
customer_id	Customer identifier
loan_sum	Total outstanding loan amount (can be negative)
loan_count	Number of active loans
cc_sum	Total credit card outstanding (can be negative)
cc_count	Number of credit cards
od_sum	Total overdraft facility amount (can be negative)
od_count	Number of overdraft accounts
ka_sum	Total balance across K-family accounts
ka_count	Number of K-family accounts
sa_sum	Total balance across savings accounts
sa_count	Number of savings accounts

train_labels.parquet

Column	Description
account_id	Account identifier (training set only)
is_mule	1 = mule account, 0 = legitimate account
mule_flag_date	Date the account was flagged as a mule (empty for legitimate accounts)
alert_reason	Reason the account was flagged (empty for legitimate accounts)
flagged_by_branch	Branch code that reported the activity (empty for legitimate accounts)

Note: Labels may contain noise/red-herrings. Not all labels are guaranteed to be correct.

test_accounts.parquet

Column	Description
account_id	Account IDs to generate predictions for

demographics.parquet

Column	Description
customer_id	Customer identifier
name	Full customer name
gender	M or F
address_last_update_date	Date of last address update
address	Street address and city
phone_number	Phone number
passbook_last_update_date	Date of last passbook update
joint_account_flag	Joint account (Y/N)
nri_flag	Non-Resident Indian (Y/N)

accounts-additional.parquet

Column	Description
account_id	Account identifier
scheme_code	Government scheme code (PMJDY, PMSBY, PMJJBY, APY, SCSS, SSA, REGULAR)

transactions_additional (partitioned)

Column	Description
transaction_id	Transaction identifier (joins to transactions)
mnemonic_code	Transaction type mnemonic (same as channel)
latitude	Transaction location latitude
longitude	Transaction location longitude
ip_address	Source IP address
balance_after_transaction	Running account balance after this transaction
part_transaction_type	CI (Customer Induced), BI (Bank Induced), IP (Interest Paid), IC (Interest Collected)
atm_deposit_channel_code	CDM or CRM for ATM deposits, empty otherwise
transaction_sub_type	CLT_CASH, LOAN, or NORMAL

branch.parquet

Column	Description
branch_code	Branch identifier
branch_address	Branch street address
branch_pin_code	Branch location PIN code
branch_city	City
branch_state	State code
branch_employee_count	Number of employees at the branch
branch_turnover	Branch turnover metric
branch_asset_size	Branch asset size
branch_type	urban, semi-urban, or rural

Known Mule Behavior Patterns

The following money laundering patterns are known to exist in real-world banking data. Mule accounts in this dataset may exhibit one or more of these behaviors:

1. Dormant Activation — Long-inactive accounts suddenly showing high-value transaction bursts
2. Structuring — Repeated transactions just below reporting thresholds (e.g., amounts near 50,000)
3. Rapid Pass-Through — Large credits quickly followed by matching debits (funds barely rest in the account)
4. Fan-In / Fan-Out — Many small inflows aggregated into one large outflow, or vice versa
5. Geographic Anomaly — Transactions from locations inconsistent with the account holder's profile
6. New Account High Value — Recently opened accounts with unusually high transaction volumes
7. Income Mismatch — Transaction values disproportionate to account balance or customer profile
8. Post-Mobile-Change Spike — Sudden transaction surge after a mobile number update (potential account takeover)
9. Round Amount Patterns — Disproportionate use of exact round amounts (1K, 5K, 10K, 50K)
10. Layered/Subtle — Weak signals from multiple patterns combined, no single strong indicator
11. Salary Cycle Exploitation — Laundering disguised within natural salary credit and bill payment cycles at month boundaries
12. Branch-Level Collusion — Clusters of suspicious accounts at the same branch with shared counterparties and coordinated timing
13. MCC-Amount Anomaly — Transactions with amounts that are statistical outliers for their merchant category code

Relationships

customers ──(customer_id)──> customer_account_linkage ──(account_id)──> accounts
    |                                                                       |
(customer_id)                                                          (account_id)
    |                                                                       |
    v                                                                       v
demographics                                                          transactions
                                                                            |
                                                                     (transaction_id)
                                                                            |
                                                                            v
                                                                 transactions_additional

customers ──(customer_id)──> product_details
accounts  ──(account_id)──> train_labels / test_accounts
accounts  ──(account_id)──> accounts-additional
accounts  ──(branch_code)──> branch

Submission Format

account_id,is_mule,suspicious_start,suspicious_end
ACCT_000000,0.02,,
ACCT_000003,0.87,2023-11-15T09:30:00,2024-02-20T16:45:00
...

One row per account in test_accounts.parquet:

• is_mule: Probability score between 0 and 1
• suspicious_start: ISO timestamp of the beginning of the suspected suspicious activity window (empty if predicted legitimate)
• suspicious_end: ISO timestamp of the end of the suspected suspicious activity window (empty if predicted legitimate)

The time window should capture when you believe the mule activity occurred. Primary scoring is on is_mule. Time window accuracy is scored separately as a bonus metric using temporal IoU (Intersection over Union) against the ground truth activity period.

Good luck.