Datasets:
license: cc-by-4.0
language:
- ru
pretty_name: MBank ATM Dataset (Bishkek 2024–2025)
tags:
- finance
- atm
- time-series
- anomaly-detection
- rfm
- kyrgyzstan
task_categories:
- time-series-forecasting
- tabular-classification
- tabular-regression
size_categories:
- 10M<n<100M
configs:
- config_name: fact_transactions
data_files:
- split: train
path: facts/fact_transactions/**/*.parquet
- config_name: fact_cash_ops
data_files: facts/fact_cash_ops.parquet
- config_name: fact_atm_status
data_files: facts/fact_atm_status.parquet
- config_name: dim_atm
data_files: dimensions/dim_atm.parquet
- config_name: dim_calendar
data_files: dimensions/dim_calendar.parquet
- config_name: dim_customer
data_files: dimensions/dim_customer.parquet
- config_name: dim_card
data_files: dimensions/dim_card.parquet
- config_name: dim_district
data_files: dimensions/dim_district.parquet
- config_name: labels_fraud
data_files: labels/fraud.parquet
- config_name: labels_cash_events
data_files: labels/cash_events.parquet
- config_name: labels_faults
data_files: labels/faults.parquet
- config_name: labels_cold_start
data_files: labels/cold_start.parquet
- config_name: labels_events
data_files: labels/events.parquet
MBank ATM Dataset (Bishkek, 2024–2025)
Per-second dataset of a network of 120 ATMs over 731 days (2024-01-01 … 2025-12-31). Card-processing style records (ISO-8583-inspired), grounded in the real-world context of Kyrgyzstan: ERA5 weather, the USD/KGS rate of the National Bank of the KR, and ATM coordinates from OpenStreetMap.
Intended as a training ground: demand forecasting, ATM clustering, RFM analysis,
anomaly detection. Every anomaly has a checkable "answer key" in labels/.
Google Colab — install & load (in the browser)
In a fresh Colab cell, install the libraries and download the dataset:
# 1. Libraries (pandas/pyarrow ship with Colab; only the HF client is needed)
!pip install -q huggingface_hub datasets
# 2. Download the whole dataset in one call
from huggingface_hub import snapshot_download
path = snapshot_download("aiacademy-kg/mbank-atm-bishkek", repo_type="dataset")
print("Dataset at:", path)
# 3. Read tables with plain pandas
import pandas as pd
dim_atm = pd.read_parquet(f"{path}/dimensions/dim_atm.parquet")
calendar = pd.read_parquet(f"{path}/dimensions/dim_calendar.parquet")
fraud = pd.read_parquet(f"{path}/labels/fraud.parquet")
print(dim_atm.shape, calendar.shape)
Transactions (12M rows) — read only the columns/months you need, not all at once:
import pyarrow.dataset as ds
tx = ds.dataset(f"{path}/facts/fact_transactions", partitioning="hive")
march = tx.to_table(
filter=(ds.field("year") == 2024) & (ds.field("month") == 3),
columns=["timestamp", "atm_id", "dispensed_amount", "response_code"],
).to_pandas()
print(march.shape)
Or via 🤗 datasets (each table is a separate subset):
from datasets import load_dataset
tx = load_dataset("aiacademy-kg/mbank-atm-bishkek", "fact_transactions", split="train")
atm = load_dataset("aiacademy-kg/mbank-atm-bishkek", "dim_atm", split="train")
# subsets: fact_transactions, fact_cash_ops, fact_atm_status, dim_atm, dim_calendar,
# dim_customer, dim_card, dim_district, labels_fraud, labels_cash_events, …
If the dataset is private, authenticate first:
from huggingface_hub import login; login("hf_YOUR_TOKEN").
Quick start (local)
import pandas as pd
import pyarrow.dataset as ds
# Reference tables
dim_atm = pd.read_parquet("dataset/dimensions/dim_atm.parquet")
cal = pd.read_parquet("dataset/dimensions/dim_calendar.parquet")
# Transaction fact — 12M rows, partitioned by year/month. Read columnar / filtered:
tx = ds.dataset("dataset/facts/fact_transactions", partitioning="hive")
march = tx.to_table(filter=(ds.field("year") == 2024) & (ds.field("month") == 3),
columns=["timestamp","atm_id","dispensed_amount","response_code"]).to_pandas()
# Anomaly ground truth
fraud = pd.read_parquet("dataset/labels/fraud.parquet")
⚠️
fact_transactionsis 12M rows (~727 MB). Don't load it whole — read the columns you need and filter by theyear=/month=partitions.
Folder layout
dataset/
├── README.md ← this file
├── DATA_DICTIONARY.md ← full field dictionary (types, values)
├── MANIFEST.md ← auto-inventory: tables, row counts, sizes
├── dimensions/ ← reference tables (star schema)
│ ├── dim_district.parquet (+csv) 16 zones of Bishkek
│ ├── dim_calendar.parquet (+csv) 731 days: holidays, weather, FX rate
│ ├── dim_atm.parquet (+csv) 120 ATMs (passport)
│ ├── dim_customer.parquet 550k customers
│ └── dim_card.parquet 700k cards (on-us)
├── facts/
│ ├── fact_transactions/ 12M transactions, partitioned year=/month=
│ ├── fact_cash_ops.parquet 8.8k cash replenishments
│ └── fact_atm_status.parquet 9.8k status/downtime intervals
├── labels/ ← anomaly ground truth
│ ├── fraud.parquet skimming + geo-impossibility
│ ├── cash_events.parquet depletions (out_of_cash)
│ ├── faults.parquet network faults / stuck terminals
│ ├── cold_start.parquet new ATMs (truncated history)
│ ├── events.parquet network-wide surges (FX shocks)
│ └── drift_spec.parquet year-2 concept-drift parameters
└── external/ ← external real-world inputs (reference)
├── weather_daily.parquet Open-Meteo ERA5 weather
├── fx_usd_kgs.parquet NBKR exchange rate
└── atm_locations.parquet OSM coordinates
Star schema (table relationships)
┌──────────────┐
│ dim_calendar │ date
└──────┬───────┘
│ date
┌────────────┐ ┌──────┴────────────┐ ┌──────────────┐
│ dim_district│──│ fact_transactions │──│ dim_atm │
└────────────┘ │ (12M) │ └──────────────┘
district_id └──────┬─────┬──────┘ atm_id
card_id│ │customer_id
┌─────┴──┐ ┌┴───────────┐
│dim_card│ │dim_customer│
└────────┘ └────────────┘
Keys and relationships (JOIN):
| From | Field | To | Note |
|---|---|---|---|
| fact_transactions | atm_id |
dim_atm | ATM |
| fact_transactions | district_id |
dim_district | zone (denormalized) |
| fact_transactions | date |
dim_calendar | day (weekday/holiday/weather/FX) |
| fact_transactions | card_id |
dim_card | card (on-us only; off-us → OFFUS-*, not in dim_card) |
| fact_transactions | customer_id |
dim_customer | customer (null for off-us) |
| dim_card | customer_id |
dim_customer | cardholder |
| fact_cash_ops / fact_atm_status | atm_id |
dim_atm | ATM |
on-us vs off-us:
is_on_us=True— MBank card (present indim_card/dim_customer).is_on_us=False— other bank / tourist:card_idlikeOFFUS-*, emptycustomer_id, an interchangefee_amountis charged.
Anomaly ground truth (labels/)
Every anomaly is parameterized and logged — each task has a checkable answer.
| File | Anomaly | What to look for | Method |
|---|---|---|---|
cash_events |
Depletion 7.1 | withdrawal drop + 99 spike |
rules, DBSCAN over day-ATM |
faults |
Network fault 7.2 / stuck 7.8 | 91/92/96 runs, flatlined telemetry |
downtime detection, variance |
events |
Surge 7.3 | network-wide day anomaly (FX shock) | collective outlier |
cold_start |
New ATM 7.4 | truncated history | model robustness |
drift_spec |
Concept drift 7.6 | year-1 model degrades on year-2 | drift monitoring |
fraud |
Skimming 7.7 / geo 7.10 | night off-us runs, card at far ATMs | IsolationForest, velocity rules |
fraud/cash_events reference concrete txn_id/atm_id+interval in the fact
table, so labels can be matched exactly to transactions.
External inputs (external/)
The dataset is grounded in real-world context of Kyrgyzstan — these series actually drive behavior:
| File | Source | Effect in the data |
|---|---|---|
weather_daily |
Open-Meteo ERA5 (CC BY 4.0) | frost/snow → drop at street ATMs |
fx_usd_kgs |
National Bank of the KR | rate jump → cash run |
atm_locations |
OpenStreetMap (ODbL) | ATM coordinates (placement skeleton) |
Verified: frost on 16–17 Feb 2024 → street ATMs −22%; FX shock on 20 Mar 2025 (−1.41 KGS) → volume ×3.
Full field descriptions — in DATA_DICTIONARY.md.