Datasets:

sarimahsan101
/

pakistan-financial-mule-network

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 5 new columns ({'reported_by_institution', 'report_day', 'txn_id', 'report_id', 'outcome'}) and 7 missing columns ({'institution', 'cnic', 'account_id', 'device_id', 'opened_day', 'sim_age_days', 'account_type'}).

This happened while the csv dataset builder was generating data using

hf://datasets/sarimahsan101/pakistan-financial-mule-network/fraud_reports.csv (at revision ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b), ['hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/accounts.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/fraud_reports.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/labels.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/BankAlfalah.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/HBL.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/MCB.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/Meezan.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/StandardChartered.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/UBL.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/Wallet_A.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/Wallet_B.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              report_id: string
              txn_id: string
              reported_by_institution: string
              report_day: int64
              outcome: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 886
              to
              {'account_id': Value('string'), 'institution': Value('string'), 'account_type': Value('string'), 'opened_day': Value('int64'), 'device_id': Value('string'), 'sim_age_days': Value('int64'), 'cnic': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 5 new columns ({'reported_by_institution', 'report_day', 'txn_id', 'report_id', 'outcome'}) and 7 missing columns ({'institution', 'cnic', 'account_id', 'device_id', 'opened_day', 'sim_age_days', 'account_type'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/sarimahsan101/pakistan-financial-mule-network/fraud_reports.csv (at revision ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b), ['hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/accounts.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/fraud_reports.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/labels.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/BankAlfalah.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/HBL.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/MCB.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/Meezan.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/StandardChartered.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/UBL.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/Wallet_A.csv', 'hf://datasets/sarimahsan101/pakistan-financial-mule-network@ffc0666b3c71e9f44a31b52ab0f969ee19dbde3b/transactions_by_institution/Wallet_B.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

account_id string	institution string	account_type string	opened_day int64	device_id string	sim_age_days int64	cnic string
acc_00000001	MCB	personal	-251	dev_00018290	1,523	42101-2719583-9
acc_00000002	HBL	personal	-254	dev_00030496	1,049	42101-1445199-9
acc_00000003	HBL	personal	-253	dev_00058879	1,221	42101-5667265-1
acc_00000004	BankAlfalah	personal	-223	dev_00020380	455	42101-6647119-2
acc_00000005	HBL	personal	77	dev_00034672	1,667	42101-1728977-8
acc_00000006	Meezan	business	-83	dev_00038428	1,713	42101-7067228-4
acc_00000007	BankAlfalah	personal	-217	dev_00010459	1,766	42101-4905582-2
acc_00000008	UBL	personal	20	dev_00048521	742	42101-4514944-5
acc_00000009	BankAlfalah	personal	-40	dev_00022432	1,108	42101-5107245-3
acc_00000010	Meezan	personal	71	dev_00028786	1,417	42101-6440561-1
acc_00000011	UBL	personal	-228	dev_00008676	447	42101-6279418-4
acc_00000012	MCB	personal	58	dev_00018727	557	42101-3342608-4
acc_00000013	BankAlfalah	personal	-146	dev_00076485	832	42101-7073292-4
acc_00000014	Wallet_B	personal	-341	dev_00014372	328	42101-3684052-7
acc_00000015	MCB	personal	-126	dev_00069353	529	42101-1192619-2
acc_00000016	MCB	personal	-37	dev_00044588	243	42101-5924115-7
acc_00000017	HBL	personal	-231	dev_00065613	1,575	42101-3997281-9
acc_00000018	Wallet_A	merchant	-38	dev_00066541	1,262	42101-4337174-3
acc_00000019	UBL	personal	67	dev_00000075	1,241	42101-6438436-8
acc_00000020	HBL	agent	39	dev_00031386	133	42101-5041154-2
acc_00000021	HBL	personal	-93	dev_00100380	272	42101-3154051-8
acc_00000022	Wallet_B	personal	-55	dev_00055462	448	42101-4374754-5
acc_00000023	UBL	personal	-101	dev_00059178	262	42101-5159166-4
acc_00000024	HBL	personal	-64	dev_00028865	29	42101-2191066-1
acc_00000025	UBL	merchant	9	dev_00067392	502	42101-5672073-8
acc_00000026	HBL	personal	73	dev_00075526	983	42101-5076817-8
acc_00000027	StandardChartered	personal	-145	dev_00046439	882	42101-7897151-8
acc_00000028	Wallet_A	personal	-35	dev_00012900	139	42101-7754864-6
acc_00000029	StandardChartered	personal	-91	dev_00058801	302	42101-8077999-3
acc_00000030	UBL	personal	56	dev_00105910	1,779	42101-2642635-1
acc_00000031	MCB	personal	-318	dev_00098772	1,753	42101-4965789-3
acc_00000032	Meezan	personal	7	dev_00021580	791	42101-1036161-7
acc_00000033	UBL	personal	-149	dev_00091304	1,511	42101-9164991-3
acc_00000034	HBL	personal	-88	dev_00007990	1,546	42101-6261415-1
acc_00000035	HBL	personal	67	dev_00020636	131	42101-9519948-2
acc_00000036	Wallet_A	personal	-245	dev_00052924	260	42101-5130808-1
acc_00000037	MCB	personal	-98	dev_00041468	549	42101-4426900-6
acc_00000038	UBL	personal	-212	dev_00059930	662	42101-2217071-1
acc_00000039	Meezan	business	9	dev_00070469	451	42101-9487336-5
acc_00000040	HBL	personal	-240	dev_00048435	598	42101-3646552-8
acc_00000041	StandardChartered	personal	-31	dev_00069330	31	42101-6022741-2
acc_00000042	Wallet_B	personal	-311	dev_00097311	1,148	42101-3607983-5
acc_00000043	UBL	personal	-14	dev_00083131	1,761	42101-5428914-9
acc_00000044	Meezan	merchant	11	dev_00083137	882	42101-5641923-1
acc_00000045	HBL	personal	-231	dev_00021179	1,533	42101-8412769-9
acc_00000046	BankAlfalah	business	-12	dev_00019537	1,132	42101-1604451-6
acc_00000047	MCB	personal	-208	dev_00047796	1,645	42101-1669324-6
acc_00000048	HBL	personal	-79	dev_00053265	1,286	42101-3592974-4
acc_00000049	Wallet_A	business	52	dev_00003249	382	42101-6573147-7
acc_00000050	StandardChartered	merchant	34	dev_00020867	1,627	42101-2813543-7
acc_00000051	Wallet_A	business	-130	dev_00045831	640	42101-4818418-4
acc_00000052	HBL	personal	-330	dev_00101362	586	42101-6891254-9
acc_00000053	UBL	business	-351	dev_00015119	549	42101-3995870-5
acc_00000054	HBL	personal	-205	dev_00057200	1,256	42101-9580255-2
acc_00000055	UBL	personal	-3	dev_00057155	18	42101-9722815-9
acc_00000056	MCB	agent	-265	dev_00047740	898	42101-2173965-6
acc_00000057	MCB	personal	-212	dev_00066470	648	42101-7851696-6
acc_00000058	Meezan	personal	-150	dev_00087154	791	42101-3919694-5
acc_00000059	Meezan	merchant	-258	dev_00056347	1,624	42101-6406444-8
acc_00000060	UBL	personal	-279	dev_00086357	188	42101-0000049-1
acc_00000061	MCB	personal	-245	dev_00088185	650	42101-4768844-4
acc_00000062	HBL	personal	78	dev_00100728	164	42101-8640615-7
acc_00000063	Wallet_A	personal	-169	dev_00064800	833	42101-5093379-3
acc_00000064	MCB	personal	13	dev_00102030	885	42101-4671545-3
acc_00000065	StandardChartered	personal	-80	dev_00032663	1,752	42101-3036059-8
acc_00000066	HBL	personal	-79	dev_00078048	664	42101-8425149-9
acc_00000067	Meezan	merchant	-284	dev_00097473	1,779	42101-8963715-8
acc_00000068	UBL	personal	-99	dev_00063518	1,298	42101-5013879-5
acc_00000069	Meezan	personal	-194	dev_00041905	1,121	42101-2351868-3
acc_00000070	HBL	personal	-256	dev_00008419	864	42101-7838382-6
acc_00000071	Meezan	personal	-150	dev_00051050	1,591	42101-1327691-7
acc_00000072	Meezan	agent	-166	dev_00109541	873	42101-4700025-8
acc_00000073	HBL	personal	-193	dev_00087671	1,405	42101-7783308-3
acc_00000074	StandardChartered	merchant	68	dev_00003535	821	42101-1454696-2
acc_00000075	MCB	personal	-340	dev_00034100	791	42101-6492066-4
acc_00000076	Meezan	personal	35	dev_00098568	1,718	42101-8072684-5
acc_00000077	StandardChartered	personal	-339	dev_00045871	474	42101-2151225-1
acc_00000078	BankAlfalah	agent	-355	dev_00081440	327	42101-5002120-3
acc_00000079	Meezan	personal	59	dev_00091681	539	42101-7189034-3
acc_00000080	MCB	agent	-282	dev_00040769	236	42101-1430812-5
acc_00000081	MCB	merchant	-264	dev_00009962	1,227	42101-5074336-2
acc_00000082	MCB	personal	-304	dev_00104364	1,174	42101-1689025-6
acc_00000083	Meezan	personal	-34	dev_00044726	40	42101-8047416-8
acc_00000084	HBL	agent	-130	dev_00092709	328	42101-8306261-3
acc_00000085	BankAlfalah	agent	-90	dev_00101534	1,005	42101-8799287-7
acc_00000086	StandardChartered	personal	-240	dev_00108886	192	42101-5679639-8
acc_00000087	UBL	personal	-171	dev_00044093	73	42101-9293086-6
acc_00000088	HBL	personal	43	dev_00036656	1,235	42101-5635017-9
acc_00000089	HBL	agent	-157	dev_00064039	1,152	42101-5031966-8
acc_00000090	MCB	personal	11	dev_00038567	468	42101-7784725-4
acc_00000091	UBL	personal	-94	dev_00045057	886	42101-6549757-6
acc_00000092	BankAlfalah	personal	-304	dev_00094541	409	42101-6294016-2
acc_00000093	BankAlfalah	agent	-267	dev_00028362	1,527	42101-9123504-5
acc_00000094	BankAlfalah	business	-221	dev_00013177	1,720	42101-4256752-5
acc_00000095	UBL	personal	-92	dev_00016592	576	42101-1763626-1
acc_00000096	Meezan	personal	-114	dev_00013447	40	42101-5770422-8
acc_00000097	Meezan	personal	32	dev_00062617	248	42101-2096280-7
acc_00000098	Meezan	personal	-288	dev_00019556	1,676	42101-6097514-2
acc_00000099	Wallet_B	personal	77	dev_00078136	1,634	42101-4786401-9
acc_00000100	UBL	merchant	-64	dev_00056208	640	42101-2010496-2

End of preview.

Pakistan Financial Ecosystem: Synthetic Multi-Bank Transaction Graph & Money Mule Benchmark

A high-fidelity, large-scale financial transaction network benchmark simulating interbank clearing (State Bank of Pakistan Raast, 1IBFT) across 8 major financial institutions.

Dataset Summary

Total Accounts (Nodes): 100,000
Total Transactions (Edges): 1,000,000
Simulation Duration: 90 days
Mule Accounts: 2,075 (2.08%)
Legitimate Look-Alike Hubs: 564 (0.56%)
Normal Accounts: 97,361 (97.36%)
Delayed Fraud Reports: 568

Class & Typology Distribution

Typology	Accounts	Prevalence
`normal`	97,361	97.36%
`layered_chain`	564	0.56%
`device_ring`	502	0.50%
`cnic_reuse`	383	0.38%
`fan_in_fan_out`	250	0.25%
`merchant`	188	0.19%
`kameti`	188	0.19%
`remittance`	188	0.19%
`smurfing`	188	0.19%
`dormant_active`	188	0.19%

File Structure

accounts.csv / accounts.parquet: Node features available at runtime (account_id, institution, account_type, opened_day, device_id, sim_age_days, cnic).
transactions.csv / transactions.parquet: Interbank transaction edges (txn_id, sender_id, receiver_id, amount, timestamp, channel, sender_institution, receiver_institution, is_confirmed_fraud, reported_day).
labels.csv / labels.parquet: Ground truth labels (is_mule, is_legit_hub, typology, cluster_id).
fraud_reports.csv / fraud_reports.parquet: Delayed reporting incidents simulating reporting lag.
transactions_by_institution/: Fragmented per-bank views to benchmark isolated silo detection vs. shared consortium graph detection.

Quickstart (Python / Pandas)

import pandas as pd

accounts = pd.read_csv("accounts.csv")
transactions = pd.read_csv("transactions.csv")
labels = pd.read_csv("labels.csv")

print(f"Loaded {len(accounts):,} accounts and {len(transactions):,} transactions.")

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Total file size:

283 MB