README.md

---
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: transactions
  default: true
  data_files:
  - split: train
    path: data/synsep_full_dataset.csv
- config_name: accounts
  data_files:
  - split: train
    path: data/accounts.csv

dataset_info:
- config_name: transactions
  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: train
    num_examples: 1839560
- config_name: accounts
  description: >
    Per-account metadata for the 10,000 synthetic SynSEPA accounts — persona,
    home country, behavioural parameters, and known-beneficiary lists.
  features:
  - name: account_id
    dtype: string
  - name: persona
    dtype: string
  - name: description
    dtype: string
  - name: home_country
    dtype: string
  - name: sender_iban
    dtype: string
  - name: txn_per_month_min
    dtype: int64
  - name: txn_per_month_max
    dtype: int64
  - name: typical_amount
    dtype: float64
  - name: foreign_txn_prob
    dtype: float64
  - name: weekend_factor
    dtype: float64
  - name: fraud_target_types
    dtype: string
  - name: known_beneficiaries
    dtype: string
  - name: known_beneficiary_count
    dtype: int64
  splits:
  - name: train
    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/)
[![Dataset: Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-EpiphanyTech%2FSynSEPA-blue)](https://huggingface.co/datasets/EpiphanyTech/SynSEPA)
[![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 |

---

## 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 = {Hugging Face},
  url       = {https://huggingface.co/datasets/EpiphanyTech/SynSEPA},
  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.*