| --- |
| 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: |
|
|
| ```python |
| # 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:** |
|
|
| ```python |
| 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):** |
|
|
| ```python |
| 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) |
|
|
| ```python |
| 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_transactions` is 12M rows (~727 MB). Don't load it whole — read the |
| > columns you need and filter by the `year=/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 in `dim_card`/`dim_customer`). |
| > `is_on_us=False` — other bank / tourist: `card_id` like `OFFUS-*`, empty `customer_id`, |
| > an interchange `fee_amount` is 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](DATA_DICTIONARY.md). |
| |
