FDB: Fraud Dataset Benchmark

By Prince Grover, Zheng Li, Jianbo Liu, Jakub Zablocki, Hao Zhou, Julia Xu and Anqi Cheng

The Fraud Dataset Benchmark (FDB) is a compilation of publicly available datasets relevant to fraud detection (arXiv Link). The FDB aims to cover a wide variety of fraud detection tasks, ranging from card not present transaction fraud, bot attacks, malicious traffic, loan risk and content moderation. The Python based data loaders from FDB provide dataset loading, standardized train-test splits and performance evaluation metrics. The goal of our work is to provide researchers working in the field of fraud and abuse detection a standardized set of benchmarking datasets and evaluation tools for their experiments. Using FDB tools we evaluate 4 AutoML pipelines including AutoGluon, H2O, Amazon Fraud Detector and Auto-sklearn across 9 different fraud detection datasets and discuss the results.

Datasets used in FDB

Brief summary of the datasets used in FDB. Each dataset is described in detail in data source section.

#	Dataset name	Dataset key	Fraud category	#Train	#Test	Class ratio (train)	#Feats	#Cat	#Num	#Text	#Enrichable
1	IEEE-CIS Fraud Detection	ieeecis	Card Not Present Transactions Fraud	561,013	28,527	3.50%	67	6	61	0	0
2	Credit Card Fraud Detection	ccfraud	Card Not Present Transactions Fraud	227,845	56,962	0.18%	28	0	28	0	0
3	Fraud ecommerce	fraudecom	Card Not Present Transactions Fraud	120,889	30,223	10.60%	6	2	3	0	1
4	Simulated Credit Card Transactions generated using Sparkov	sparknov	Card Not Present Transactions Fraud	1,296,675	20,000	5.70%	17	10	6	1	0
5	Twitter Bots Accounts	twitterbot	Bot Attacks	29,950	7,488	33.10%	16	6	6	4	0
6	Malicious URLs dataset	malurl	Malicious Traffic	586,072	65,119	34.20%	2	0	1	1	0
7	Fake Job Posting Prediction	fakejob	Content Moderation	14,304	3,576	4.70%	16	10	1	5	0
8	Vehicle Loan Default Prediction	vehicleloan	Credit Risk	186,523	46,631	21.60%	38	13	22	3	0
9	IP Blocklist	ipblock	Malicious Traffic	172,000	43,000	7%	1	0	0	0	1

Installation

Requirements

• Kaggle account

• AWS account

• Python 3.7+

• Python requirements

autogluon==0.4.2
h2o==3.36.1.2
boto3==1.20.21
click==8.0.3
click-plugins==1.1.1
Faker==4.14.2
joblib==1.0.0
kaggle==1.5.12
numpy==1.19.5
pandas==1.1.2
regex==2020.7.14
scikit-learn==0.22.1
scipy==1.5.4
auto-sklearn==0.14.7
dask==2022.8.1

Step 1: Setup Kaggle CLI

The FraudDatasetBenchmark object is going to load datasets from the source (which in most of the cases is Kaggle), and then it will modify/standardize on the fly, and provide train-test splits. So, the first step is to setup Kaggle CLI in the machine being used to run Python.

Use intructions from How to Use Kaggle guide. The steps include:

Remember to download the authentication token from "My Account" on Kaggle, and save token at ~/.kaggle/kaggle.json on Linux, OSX and at C:\Users<Windows-username>.kaggle\kaggle.json on Windows. If the token is not there, an error will be raised. Hence, once you’ve downloaded the token, you should move it from your Downloads folder to this folder.

Step 2: Clone Repo

Once Kaggle CLI is setup and installed, clone the github repo using git clone https://github.com/amazon-research/fraud-dataset-benchmark.git if using HTTPS, or git clone [email protected]:amazon-research/fraud-dataset-benchmark.git if using SSH.

Step 3: Install

Once repo is cloned, from your terminal, cd to the repo and type pip install ., which will install the required classes and methods.

FraudDatasetBenchmark Usage

The usage is straightforward, where you create a dataset object of FraudDatasetBenchmark class, and extract useful goodies like train/test splits and eval_metrics.

from fdb.datasets import FraudDatasetBenchmark

# all_keys = ['fakejob', 'vehicleloan', 'malurl', 'ieeecis', 'ccfraud', 'fraudecom', 'twitterbot', 'ipblock'] 
key = 'ipblock'

obj = FraudDatasetBenchmark(key=key)
print(obj.key)

print('Train set: ')
display(obj.train.head())
print(len(obj.train.columns))
print(obj.train.shape)

print('Test set: ')
display(obj.test.head())
print(obj.test.shape)

print('Test scores')
display(obj.test_labels.head())
print(obj.test_labels['EVENT_LABEL'].value_counts())
print(obj.train['EVENT_LABEL'].value_counts(normalize=True))
print('=========')

Notebook template to load dataset using FDB data-loader is available at scripts/examples/Test_FDB_Loader.ipynb

Reproducibility

Reproducibility scripts are available at scripts/reproducibility/ in respective folders for afd, autogluon and h2o. Each folder also had README with steps to reproduce.

Benchmark Results

Dataset key	AUC-ROC
	AFD OFI	AFD TFI	AutoGluon	H2O	Auto-sklearn
ccfraud	0.985	0.99	0.99	0.992	0.988
fakejob	0.987	-	0.998	0.99	0.983
fraudecom	0.519	0.636	0.522	0.518	0.515
ieeecis	0.938	0.94	0.855	0.89	0.932
malurl	0.985	-	0.998	Training failure	0.5
sparknov	0.998	-	0.997	0.997	0.995
twitterbot	0.934	-	0.943	0.938	0.936
vehicleloan	0.673	-	0.669	0.67	0.664
ipblock	0.937	-	0.804	Training failure	0.5

ROC Curves

The numbers in the legend represent AUC-ROC from different models from our baseline evaluations on AutoML.

Data Sources

1. IEEE-CIS Fraud Detection

   • Link: https://www.kaggle.com/c/ieee-fraud-detection/overview
   • Feature info: Card, address, email, product id, aggregates
   • Fraud category: Card Not Present Transaction Fraud
   • Provider: Vesta Corporation

2. Credit Card Fraud Detection

   • Link: https://www.kaggle.com/mlg-ulb/creditcardfraud/
   • Feature info: PCA features, time, amount (highly imbalanced)
   • Fraud category: Card Not Present Transaction Fraud
   • Provider: Machine Learning Group - ULB

3. Fraud ecommerce

   • Link: https://www.kaggle.com/vbinh002/fraud-ecommerce
   • Feature info: Signup time, purchase time, purchase value, ip, browser, age
   • Fraud category: Card Not Present Transaction Fraud
   • Provider: Binh Vu

4. Simulated Credit Card Transactions generated using Sparkov

   • Link: https://www.kaggle.com/kartik2112/fraud-detection
   • Feature info: Cc_num, merchant, txn_date, category, zip, location
   • Fraud category: Card Not Present Transaction Fraud
   • Provider: Kartik Shenoy

5. Twitter Bots Accounts

   • Link: https://www.kaggle.com/code/davidmartngutirrez/bots-accounts-eda/data?select=twitter_human_bots_dataset.csv
   • Feature info: Followers/following count, geo-enabled, description etc.
   • Fraud category: Bot Attacks
   • Provider: David Martín Gutiérrez

6. Malicious URLs dataset

   • Link: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
   • Feature info: URL (malicious include defacement, phishing and malware)
   • Fraud category: Malicious Traffic
   • Provider: Manu Siddhartha

7. Real / Fake Job Posting Prediction

   • Link: https://www.kaggle.com/shivamb/real-or-fake-fake-jobposting-prediction
   • Feature info: Textual information and meta-information about the jobs
   • Fraud category: Content Moderation
   • Provider: Shivam Bansal

8. Vehicle Loan Default Prediction

   • Link: https://www.kaggle.com/avikpaul4u/vehicle-loan-default-prediction
   • Feature info: numeric, categorical, classification(binary)
   • Fraud category: Credit Risk
   • Provider: Avik Paul

9. IP Blocklist

   • Link: http://cinsscore.com/list/ci-badguys.txt
   • Feature info: Malicious IP address
   • Fraud category: Malicious Traffic
   • Provider: CINSscore.com

Citation

@misc{grover2022fdb,
      title={FDB: Fraud Dataset Benchmark}, 
      author={Prince Grover and Zheng Li and Jianbo Liu and Jakub Zablocki and Hao Zhou and Julia Xu and Anqi Cheng},
      year={2022},
      eprint={2208.14417},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

License

This project is licensed under the MIT-0 License.

Acknowledgement

We thank creators of all datasets used in the benchmark and organizations that have helped in hosting the datasets and making them widely availabel for research purposes.