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README.md

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+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- time-series-forecasting
+size_categories:
+- 1M<n<10M
+tags:
+- fraud-detection
+- payments
+- synthetic
+- SEPA
+- anomaly-detection
+- finance
+- tabular
+pretty_name: SynSEPA — Synthetic SEPA Instant Payment Fraud Dataset
+
+configs:
+- config_name: default
+  data_files:
+  - split: transactions
+    path: data/synsep_full_dataset.csv
+  - split: accounts
+    path: data/accounts.csv
+
+dataset_info:
+  description: >
+    SynSEPA is a synthetic SEPA Instant Credit Transfer dataset containing 1.84M
+    transactions across 10,000 accounts, with 4 APP fraud typologies injected
+    following EPC 2025 definitions. Calibrated against EBA-ECB 2025 fraud statistics.
+  features:
+  - name: transaction_id
+    dtype: string
+  - name: account_id
+    dtype: string
+  - name: persona
+    dtype: string
+  - name: timestamp
+    dtype: string
+  - name: sender_iban
+    dtype: string
+  - name: beneficiary_iban
+    dtype: string
+  - name: beneficiary_country
+    dtype: string
+  - name: country_type
+    dtype: string
+  - name: amount
+    dtype: float64
+  - name: remittance_category
+    dtype: string
+  - name: remittance_text
+    dtype: string
+  - name: hour_of_day
+    dtype: int64
+  - name: day_of_week
+    dtype: int64
+  - name: is_weekend
+    dtype: int64
+  - name: time_since_last_txn
+    dtype: float64
+  - name: is_new_beneficiary
+    dtype: int64
+  - name: is_fraud
+    dtype: int64
+  - name: fraud_type
+    dtype: string
+  splits:
+  - name: transactions
+    num_examples: 1839560
+  - name: accounts
+    num_examples: 10000
+---
+
+# SynSEPA: A Synthetic SEPA Instant Payment Dataset for APP Fraud Detection Research
+
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
+[![Paper: SEPAGen](https://img.shields.io/badge/Paper-SEPAGen-blue)]()
+[![Fraud Rate: 0.39%](https://img.shields.io/badge/Fraud%20Rate-0.39%25-red)]()
+[![Transactions: 1.84M](https://img.shields.io/badge/Transactions-1.84M-green)]()
+
+---
+
+## Overview
+
+**SynSEPA** is the first publicly available synthetic dataset specifically designed for
+Authorised Push Payment (APP) fraud detection in SEPA Instant Credit Transfer payments.
+
+No public SEPA fraud dataset previously existed. SynSEPA fills this gap by providing
+a large-scale, statistically calibrated synthetic dataset grounded in official EU
+regulatory statistics from the EBA-ECB 2025 Payment Fraud Report and the EPC 2025
+Payment Threats and Fraud Trends Report.
+
+The dataset is released as part of the **SEPAGen** research project:
+*"Transformer-Based Generative Anomaly Detection for APP Fraud in SEPA Instant Payments"*
+
+---
+
+## Why SynSEPA?
+
+### The Problem
+- SEPA Instant payments settle in **under 10 seconds** and are **irreversible**
+- APP fraud — where victims are manipulated into authorising payments — caused
+  **€2.5 billion in EU losses in 2024** (EBA-ECB 2025), up 24% YoY
+- **No public SEPA fraud dataset** existed for training or benchmarking ML models
+- Real SEPA fraud data is proprietary, privacy-sensitive, and inaccessible to researchers
+
+### The Solution
+SynSEPA generates realistic SEPA payment sequences with:
+- **4 APP fraud typologies** as defined by the EPC 2025 report
+- **4 customer personas** representing the EU retail banking population
+- **Statistical calibration** against EBA-ECB 2025 fraud report benchmarks
+- **Sequential structure** enabling sequence-based generative model training
+
+---
+
+## Dataset Statistics
+
+| Property | Value |
+|---|---|
+| Total transactions | 1,839,560 |
+| Normal transactions | 1,832,448 (99.61%) |
+| Fraud transactions | 7,112 (0.39%) |
+| Unique accounts | 10,000 |
+| Simulation period | January – December 2024 |
+| Countries covered | 10 EU/EEA countries |
+| Avg transactions per account | 184 |
+| Min transactions per account | 69 |
+
+### Fraud Breakdown
+
+| Typology | Transactions | Victims | Avg Txns/Victim |
+|---|---|---|---|
+| Romance Scam | 4,232 | 720 | 5.9 |
+| Bank Impersonation | 1,440 | 1,440 | 1.0 |
+| Invoice / Mandate Fraud | 900 | 900 | 1.0 |
+| CEO / BEC Fraud | 540 | 540 | 1.0 |
+| **Total** | **7,112** | **3,600** | **1.98** |
+
+### Account Personas
+
+| Persona | Accounts | % | Fraud Targets |
+|---|---|---|---|
+| Regular Employee | 5,054 | 50.5% | Impersonation, Romance |
+| Student | 1,972 | 19.7% | Romance, Impersonation |
+| Retiree | 1,519 | 15.2% | Impersonation, Romance |
+| Small Business | 1,455 | 14.5% | Invoice, CEO |
+
+---
+
+## File Structure
+
+```
+synsep-dataset/
+├── data/
+│   ├── synsep_full_dataset.csv    ← Main dataset (1.84M transactions, ~298 MB)
+│   └── accounts.csv               ← Account metadata (10,000 accounts, ~3.9 MB)
+├── code/
+│   ├── generate/                  ← Scripts to regenerate the dataset from scratch
+│   └── examples/
+│       └── sample_baseline.py     ← Quick start: Isolation Forest baseline
+├── README.md                      ← This file
+└── dataset-metadata.json          ← Kaggle dataset metadata
+```
+
+---
+
+## Quick Start
+
+```bash
+pip install pandas scikit-learn matplotlib numpy
+python code/examples/sample_baseline.py
+```
+
+This runs an Isolation Forest baseline on the dataset and prints AUROC, PR-AUC,
+and a classification report at 1% FPR — a useful starting benchmark.
+
+---
+
+## Schema — synsep_full_dataset.csv
+
+### Core SEPA Fields
+
+| Column | Type | Description | Example |
+|---|---|---|---|
+| `transaction_id` | string | Unique transaction ID | TXN_00000001 |
+| `account_id` | string | Sender account ID | ACC_000042 |
+| `persona` | string | Account type | employee |
+| `timestamp` | datetime | Transaction datetime | 2024-03-15 14:23:01 |
+| `sender_iban` | string | Sender IBAN (synthetic) | DE89370400440532013000 |
+| `beneficiary_iban` | string | Beneficiary IBAN (synthetic) | FR7630006000011234 |
+| `beneficiary_country` | string | Beneficiary country code | FR |
+| `amount` | float | Transaction amount (EUR) | 245.50 |
+| `remittance_category` | string | Payment category | grocery |
+| `remittance_text` | string | Payment reference text | Rent Mar 2024 |
+
+### Engineered Features
+
+| Column | Type | Description | Example |
+|---|---|---|---|
+| `country_type` | string | domestic / eu_cross_border / non_eu | domestic |
+| `hour_of_day` | int | Hour of transaction (0-23) | 14 |
+| `day_of_week` | int | Day of week (0=Monday, 6=Sunday) | 2 |
+| `is_weekend` | int | Weekend flag (0/1) | 0 |
+| `time_since_last_txn` | float | Seconds since previous transaction | 86400.0 |
+| `is_new_beneficiary` | int | New IBAN never seen before (0/1) | 0 |
+
+### Labels (for evaluation only — never used in unsupervised training)
+
+| Column | Type | Description | Values |
+|---|---|---|---|
+| `is_fraud` | int | Fraud label | 0 = normal, 1 = fraud |
+| `fraud_type` | string | Fraud typology | none / impersonation / invoice / romance / ceo |
+
+---
+
+## Schema — accounts.csv
+
+| Column | Type | Description |
+|---|---|---|
+| `account_id` | string | Unique account ID |
+| `persona` | string | Account type (employee/student/retiree/business) |
+| `home_country` | string | Sender's home country |
+| `sender_iban` | string | Account IBAN |
+| `txn_per_month_min/max` | int | Transaction frequency range |
+| `typical_amount` | float | Typical transaction amount |
+| `foreign_txn_prob` | float | Cross-border transaction probability |
+| `fraud_target_types` | JSON | Which fraud typologies target this account |
+| `known_beneficiaries` | JSON | List of regular beneficiary IBANs |
+
+---
+
+## APP Fraud Typologies in SynSEPA
+
+### Why these four typologies?
+
+The four fraud types in SynSEPA were selected directly from the **EPC 2025 Payment
+Threats and Fraud Trends Report** (EPC162-24 v2.0), which is the authoritative
+classification of APP fraud in the SEPA ecosystem published by the European Payments
+Council. These are not hypothetical categories — they represent the dominant, documented
+patterns of Authorised Push Payment fraud occurring across EU/EEA markets today.
+
+The selection criteria were:
+
+1. **Coverage of the victim population** — the four typologies together target all four
+   account personas in SynSEPA (retail employees, students, retirees, and small
+   businesses), ensuring the dataset is not biased toward a single demographic.
+
+2. **Diversity of behavioural signatures** — each typology produces a distinct statistical
+   footprint in the transaction data (amount level, timing, beneficiary geography,
+   sequence length), making the dataset useful for evaluating whether a model can
+   distinguish between fundamentally different fraud mechanisms rather than just
+   detecting one kind of outlier.
+
+3. **Relevance to SEPA Instant specifically** — typologies were chosen where the
+   irrevocability and speed of SEPA Instant payments are part of the fraud mechanism
+   itself (e.g. impersonation fraudsters urgently pressure victims to transfer before
+   the bank can intervene; romance scammers exploit the ease of cross-border SEPA
+   transfers). Typologies that primarily exploit card networks or slower payment rails
+   were excluded.
+
+4. **Volume calibration against EBA-ECB 2025** — victim counts and fraud volumes per
+   typology were set to reflect the proportions reported in the EBA-ECB 2025 Joint
+   Report on Payment Fraud, so the dataset mirrors the real EU fraud landscape.
+
+### Typology Details
+
+#### 1. Bank / Authority Impersonation
+Fraudster poses as the victim's bank fraud team or a law enforcement agency, creating
+urgency around a supposed security threat. Victim is convinced to transfer funds to a
+"safe account" controlled by the fraudster.
+
+- **Target personas:** Employees, Retirees, Students
+- **Behavioural signature:** Single large transaction (3–10× the victim's normal amount),
+  new domestic IBAN never seen in history, urgent remittance text ("safe account transfer",
+  "security hold"), sent shortly after the last normal transaction
+- **Why hard to detect:** Amount is large but the IBAN is domestic, and the timing follows
+  a normal inter-transaction gap — it does not look like an unusual payment channel
+
+#### 2. Invoice / Mandate Fraud
+Fraudster intercepts a legitimate supplier invoice (via email compromise or postal
+interception) and replaces the beneficiary IBAN with one they control. The victim
+pays what they believe is a routine business invoice.
+
+- **Target personas:** Small Business only
+- **Behavioural signature:** Amount closely mirrors the victim's typical supplier payment
+  (deliberately subtle), new IBAN despite a familiar-looking remittance reference,
+  business accounts only
+- **Why hard to detect:** This is the most subtle typology — the amount is not anomalous,
+  the remittance text looks normal, and only the IBAN is new. A purely amount-based
+  detector will miss it entirely.
+
+#### 3. Romance Scam
+Fraudster cultivates a fake online relationship over weeks or months, then gradually
+requests money under emotional pretexts (medical emergency, travel costs, investment
+opportunity).
+
+- **Target personas:** Employees, Students, Retirees
+- **Behavioural signature:** 4–8 escalating payments to the same foreign IBAN over a
+  5–12 week period, emotional or personal remittance text, cross-border destination
+- **Why hard to detect:** Each individual payment may not look anomalous in isolation —
+  it is the multi-week sequence of escalating payments to the same new foreign IBAN that
+  constitutes the fraud signal. This typology tests whether a model can detect
+  account-level behavioural drift over time.
+
+#### 4. CEO / Business Email Compromise (BEC)
+Fraudster impersonates a company's CEO or senior executive via email and instructs a
+finance employee to make an urgent, confidential international transfer.
+
+- **Target personas:** Small Business only
+- **Behavioural signature:** Large amount, new non-EU IBAN, Friday afternoon timing
+  (when management is less available to verify), confidential remittance text
+- **Why hard to detect:** The payment instruction appears to come from internal authority.
+  The fraud signal is in the combination of a large non-EU transfer on a Friday with a
+  new IBAN — no single feature is sufficient.
+
+---
+
+## Generation Methodology
+
+SynSEPA was generated using a 3-step pipeline (code in `code/generate/`):
+
+### Step 1 — Account Generation
+10,000 synthetic accounts assigned to one of 4 personas. Each account receives:
+- A syntactically correct IBAN for their home country
+- Behavioural parameters (frequency, amount ranges, active hours)
+- A list of 5-20 known beneficiary IBANs
+
+### Step 2 — Normal Transaction Generation
+12 months (Jan-Dec 2024) of transaction history per account, with:
+- Transaction frequency sampled from persona distribution
+- Timestamps weighted by persona's active hours and day-of-week patterns
+- Beneficiaries drawn 80% from known list, 20% new
+- Remittance categories and text sampled from persona's profile
+- All engineered features computed (time_since_last_txn, is_new_beneficiary, etc.)
+
+### Step 3 — Fraud Injection
+APP fraud transactions injected following EPC 2025 typology signatures:
+- Victim accounts selected by eligible persona type
+- Fraud transaction parameters derived from victim's normal history
+- Romance scam sequences span 4-8 payments over 5-12 week periods
+
+---
+
+## Statistical Calibration
+
+SynSEPA is calibrated against the following EBA-ECB 2025 benchmarks:
+
+| Metric | EBA Benchmark | SynSEPA | Status |
+|---|---|---|---|
+| Fraud rate (volume) | ~0.200% | 0.387% | Within range |
+| Cross-border rate (normal) | ~11% | 11.1% | Matches |
+| Weekend transaction rate | <25% | 19.8% | Passes |
+| Business hours concentration | >60% | 89.1% | Passes |
+| Fraud cross-border rate | > normal | 96.4% vs 11.1% | Correct |
+
+> Note: Fraud rate (0.387%) exceeds EBA's 0.200% volume benchmark primarily
+> because romance scams generate multiple transactions per victim (avg 5.9).
+> This reflects the multi-event nature of romance fraud as documented by the EPC.
+
+---
+
+## Validation Results
+
+SynSEPA passed **41 of 42** automated validation checks covering:
+- Basic dataset composition and size
+- Fraud typology distribution
+- Persona behavioural profiles
+- Temporal patterns (hours, weekdays, time between transactions)
+- Fraud typology signature verification
+- Cross-border transaction rates
+- Sequence integrity (ordering per account)
+- Amount distribution shape
+
+---
+
+## Intended Uses
+
+Appropriate uses:
+- Training and evaluating unsupervised or transformer based anomaly detection models
+- Benchmarking generative models (VAE, GAN, Transformer, Diffusion) on tabular fraud data
+- Sequence modelling research for financial fraud
+- Academic research in payment fraud detection
+- Developing and testing fraud detection pipelines without real customer data
+
+Inappropriate uses:
+- Training models intended for production deployment without further validation on real data
+- Any use involving real customer data or real SEPA infrastructure
+- Commercial fraud detection products without disclosure of synthetic data origin
+
+---
+
+## Limitations
+
+1. **Synthetic data gap** — All data is synthetic. Real fraud patterns may have
+   nuances not captured in EPC typology descriptions.
+
+2. **No device/session signals** — Real APP fraud detection also uses device
+   fingerprints, session behaviour, and browser signals. SynSEPA covers only
+   transaction-level signals.
+
+3. **Simplified IBAN structure** — IBANs are syntactically correct but do not
+   pass Mod-97 checksum validation.
+
+4. **Single year** — Covers January-December 2024 only. Seasonal effects beyond
+   this period are not represented.
+
+5. **Retiree cross-border rate** — Marginally exceeds 10% target (achieved 10.3%)
+   — a minor calibration artefact with no material impact on model training.
+
+---
+
+## Citation
+
+If you use SynSEPA in your research, please cite:
+
+```bibtex
+@dataset{synsep2025,
+  title     = {SynSEPA: A Synthetic SEPA Instant Payment Dataset
+               for APP Fraud Detection Research},
+  author    = {Bajaj, Gaurav},
+  year      = {2026},
+  publisher = {Kaggle / HuggingFace},
+  note      = {Generated as part of the SEPAGen project.
+               Calibrated against EBA-ECB 2025 Payment Fraud Report.}
+}
+```
+
+---
+
+## References
+
+1. EBA-ECB (2025). *Joint Report on Payment Fraud*. European Banking Authority /
+   European Central Bank. December 2025.
+
+2. EPC (2025). *Payment Threats and Fraud Trends Report* (EPC162-24 v2.0).
+   European Payments Council. November 2025.
+
+3. Lopez-Rojas, E. (2017). *Synthetic Financial Datasets for Fraud Detection*
+   (PaySim). Kaggle.
+
+---
+
+## License
+
+This dataset is released under the
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
+license. You are free to share and adapt the dataset for any purpose, provided
+appropriate credit is given.
+
+---
+
+*SynSEPA was generated entirely from synthetic data. No real customer data,
+real SEPA transactions, or real bank records were used in its creation.*