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Fraud Detection using an ensemble technique - Intel optimized DBSCAN clustering followed by Light Gradient Boosted Model (LGBM)

Credit card transactions can be stratified using DBSCAN clustering. The stratified data then can be used to train an LGBM model to identify fraudulent transactions. The Intel AI Analytics Toolkit (AI Kit) gives data scientists, AI developers, and researchers familiar Python tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel architectures. The components are built using oneAPI libraries for low-level compute optimizations.We will use the The Intel Extension for Scikit-learn and daal4py to accelerate the clustering and inference stages of this solution.

Table of Contents

Contents

• Purpose
• Reference Solution
• Comparing Performance Benefits
• Key Takeaways
• Appendix

Purpose

For many banks, retaining a high number of profitable customers is the number one business goal. Credit Card fraud, however, poses a significant threat to this goal. In terms of substantial financial losses, trust and credibility, this is a concerning issue to both banks and customers alike. Due to the steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks. The number of fraudulent transactions has been on an increasing trajectory. Machine learning (ML) can play a vital role in detecting fraudulent transactions and considering the scale at which these transactions happen, an ML approach is a commonly implemented solution. The automation pipeline needs, to be accurate, offer fast inferencing times and have a lower memory usage. These objectives can be realized using an ensemble modeling technique, which will include clustering followed by supervised ML.

Reference solution

For the supervised ML portion we can use Light Gradient Boosted Model (LGBM) which is a lightweight version of gradient boosted models. daal4py can be used to reduce the inference time. Prior to LGBM, we will stratify the data using DBSCAN clustering. This will help improve the accuracy of the supervised ML model.

This section provides key implementation details on the proposed reference solution for the target use case. It is organized as follows:

1. Proposed reference end-to-end architecture
2. Setting up the stock environment
3. Executing the reference architecture pipeline components (Data Ingestion, DBSCAN clustering, supervised ML training, hyperparameter tuning, inference)
4. Optimizing the reference solution pipeline using libraries from the Intel AI Analytics Toolkit.

Proposed Architecture

A schematic of the proposed reference architecture is shown in the following figure. We start off with Data Ingestion followed by DBSCAN clustering. The raw data is highly skewed with fraudulent transactions making up <0.2% of the total data. Training a model on this will likely result in a biased model. We address this using DBSCAN Clustering. It helps group transactions that are similar in the feature space together. data pertaining to select clusters which have the maximum ratio of fraudulent transactions are then chosen for LGBM based supervised ML training and hyperparameter tuning. The dataset post clustering will be significantly undersampled from 200K to less than 1000 but the ratio of fraudulent transactions will be improved to >30%. And we will see - because of the improved stratification - the performance of a model trained on an undersampled (clustered) dataset is still better than that of a model trained on the full dataset.

Post clustering we can perform Hyperparameter tuning with cross validation to optimize the model configuration and further enhance prediction accuracy on test data - it will directly provide the best performing model configuration for running inference.

This trained LGBM model can then be used for Streaming/Batch Prediction.

The model can then be saved on a server which can respond to requests from a client. <br>

Stock Packages Used

• Python v3.9
• Pandas v1.4.3
• numpy v1.23.1
• scikit-learn v1.1.2
• LightGBM v3.3.2

Setting up Stock Environment

Clone the git repository using the following command:

git clone https://www.github.com/oneapi-src/credit-card-fraud-detection

Once repo is cloned, navigate to the parent directory. The script setupenv.sh is provided to automate the setup of the conda environments necessary. Once you clone the git repo on your workstation/virtual machine (VM), in the parent directory, execute the following command

./setupenv.sh

This will prompt for the selection of stock/intel packages. Select 1 for stock

Example option selection for creating stock/intel environment as given below

Select technology distribution:
    1. stock
    2. intel

Once the environment is setup, activate the stock environment using the following command.

conda activate FraudDetection_stock

You can then move ahead to training an LGBM model using stock packages

Data Ingestion

Please download the data using the instructions provided in the /data folder and save it as creditcard.csv in the same location. The dataset has details of more than 280,000 credit card transactions with 30 columns serve as the features for model build and a "Class" label of 0 (legitimate transaction) and 1 (fraudulent transaction). The data is read as a pandas dataframe and split into train/test portions in the training and hyperparameter tuning scripts. The training set will be used clustering and LGBM training whereas the test set will be used as "new" data for inference while evaluating accuracy.

Clustering + Training/Hyperparameter Tuning

The clustering and training portion of the benchmarking can be run using the python script run_benchmarks_train.py. The script reads data, performs DBSCAN clustering and filters data belonging to a cluster which has the maximum proportion of fraudulent transactions.

The script then trains an LGBM model on the full dataset (~200K data points with <0.2% Fraud Rate) as well as the clustered data (<1000 data points with >30% Fraud Rate). Both trained models are saved for inference - doing so will help us quantify the benefit of using clustering as opposed to using the full dataset directly for model training. This script will also report on the execution time for these steps.

The run benchmark script takes the following arguments:

usage: run_benchmarks_train.py [-l LOGFILE] [-i]

optional arguments:
  -l LOGFILE, --logfile LOGFILE
                        log file to output benchmarking results to
  -i, --intel           use intel accelerated technologies where available

To run with stock technologies, logging the performance to logs, we would run (after creating the appropriate environment as above):

python ./src/run_benchmarks_train.py -l ./logs/stock_training.log

The hyperparameter tuning exercise can be run by following the same procedure as described for training. It goes through the same steps prior to the supervised ML portion of the pipeline (ingestion & clustering). Following these, instead of training, the script would perform hyperparameter tuning over a predefined parameter dictionary. Only substiturion would be to execute the script run_benchmarks_hyper.py instead of run_benchmarks_train.py. This execution expects the same arguments as the training case does.

Once again, the execution times will be reported and the trained models (using full and clustered data) will be saved for use by the prediction benchmarking script. Following are examples of how the hyperparameter tuning jobs can be triggered.

For stock technologies

python ./src/run_benchmarks_hyper.py -l logs/stock_hyper.log

The following is a brief description of the outputs of clustering/training/hyperparameter tuning portion of the pipeline:

Expected Input Output for Training/Hyperparameter Tuning

Input:

Output:

Model Inference - Batch

The saved models then can be used for batch inference. For this purpose, we will exectue the run_benchmarks_predict.py. It takes the following arguments:

usage: run_bechmarks-predict.py [-l LOGFILE] [-i] [-mc clusteredmodel] [-mc fullmodel] [-s]

optional arguments:
  -l  --logfile             log file to output benchmarking results to
  -i, --intel               use Intel optimized libraries where available
  -mc --clusteredmodel      pkl file of model created using clustered data
  -mf --fullmodel           pkl file of model created using full data
  -s  --streaming           run streaming inference if true

To run with stock technologies, we would run:

python ./src/run_benchmarks_predict.py -mc Clustered_LGBM_Classifier.pkl -mf Full_LGBM_Classifier.pkl -l ./logs/stock_batch.log

For streaming inference execute the following command:

python ./src/run_benchmarks_predict.py -s -mc Clustered_LGBM_Classifier.pkl -mf Full_LGBM_Classifier.pkl -l ./logs/stock_streaming.log

Expected Input and Output for Inference

Input:

Output:

Optimizing the Reference solution using libraries from the Intel AI Analytics Toolkit

The reference solution architecture for an Intel-optimized pipeline is largely unchanged except the execution using Intel optimized libraries and the inference using daal4py module. <br>

The expected output for the optimized is also similar to what we observer for the stock pipeline except for the accelerated times. This will be discussed more in the section for Comparing Performance Benefits

Intel Packages Used

• intelpython3_full v2022.1.0
• Python v3.9
• Pandas v1.3.5
• numpy v1.21.4
• Intel Extension for Scikit-Learn v2021.6.0
• LightGBM v3.3.2
• daal4py v2021.6.0

Intel® Distribution for Python

The Intel® Distribution for Python provides an accelerated version of Python that leverages the expanded instruction set of Intel® hardware as well as Intel® Performance Libraries to improve near native-performance.

Intel Extension for Scikit-learn

Intel® Extension for Scikit-Learn* provides a seamless way to accelerate the stock scikit-learn packages for machine learning. This extension package dynamically patches scikit-learn estimators to use Intel® oneAPI Data Analytics Library (oneDAL) as the underlying solver, which helps accelerate training and inference performance.

daal4py

daal4py is part of the Intel oneAPI DAL library. daal4py converted LGBM models offer signigicant advantage in inferencing over stock models. This can be vital while serving a model on an edge location which has to handle a high influx of requests from a client.

Setting up Intel Environment

Follow the same instructions as the ones for setting up a stock environment. Execute the following command

./setupenv.sh

This will prompt for the selection of stock/intel packages. Select 2 for intel.

Select technology distribution:
    1. stock
    2. intel

Once the environment is setup, activate the intel environment using the following command.

conda activate FraudDetection_intel

You can then move ahead to training an LGBM model using intel packages.

Training/Hyperparameter Tuning

There will be only one change here compared to the command for training a model with the stock packages, the addition of an argument which enables the use of intel-optimized packages, which in case of training/hyperparameter tuning would be Intel Extension for Scikit-Learn. Once again here the training/hyperparameter tuning exercise will be run for the full dataset (~200K data points with <0.2% Fraud Rate) as well as the clustered data (<1000 data points with >30% Fraud Rate).

To run with intel technologies, logging the performance to logs, we would run (after activating the intel environment):

python ./src/run_benchmarks_train.py -i -l ./logs/intel_training.log

For hyperparameter tuning, execute the following command:

python ./src/run_benchmarks_hyper.py -i -l logs/intel_hyper.log

Model Inference

Model inference in an intel environment will leverage the daal4py module which will convert the existing LGBM model into an optimized version. The optimized model will then be used for batch/streaming prediction. The benefit of using this daal4py version of the model is key to the solution as it means faster inference times, which we will see from the plots in the results section.

For batch inference, execute the following command:

python ./src/run_benchmarks_predict.py -i -mc Clustered_LGBM_Classifier.pkl -mf Full_LGBM_Classifier.pkl -l ./logs/stock_batch.log

For streaming inference execute the following command

python ./src/run_benchmarks_predict.py -s -i -mc Clustered_LGBM_Classifier.pkl -mf Full_LGBM_Classifier.pkl -l ./logs/stock_batch.log

Comparing Performance Benefits

In this section, we illustrate the benchmarking results comparing the Intel-optimized libraries vs the stock alternative as well as the performance of the two LGBM models (one trained using post-clustering data and one trained using the full dataset). The execution is broken down into the following tasks

1. Stratifying fraudulent transaction using DBSCAN clustering using stock and Intel-optimized libraries
2. Training/Hyperparameter tuning an LGBM model on the full dataset as well as post-clustering dataset
3. Predicting outcomes over batch data using an LGBM model trained using post-clustered data and full data as well as using stock and Intel-optimized libraries. For inference, the Intel-optimized libraries involves converting the model to a daal4py version
4. Repeating the inference exercise but for streaming data

Intel optimizations can be applied to DBSCAN Clustering through Intel Extension for Scikit-Learn and LGBM inference after converting a pre-trained model to a daal4py model.

Key Results

1. There is a 30% acceleration on using the Intel Extension for Scikit-learn for DBSCAN clustering compared to stock scikit-learn
2. Using daal4py accelerates performance by up to 3.86x for batch inference, which is critical in model development. More importantly it is 4.15x faster for streaming inference. This means inference can happen either much quicker or on a less powerful edge location when the model is deployed in the field.
3. The performance benefit of using post-clustering data translates into a higher accuracy. For a model trained using clustered data, we get a higher f1_score (0.92) compared to that for a model trained using the full dataset (0.89). This is significant because of the scale at which financial transactions occur. About 100 million credit card transactions occur every day in the US. Assuming 0.2% (200000) transactions are fraudulent, a 3% better accuracy would mean up to 6000 more transactions would be correctly classified, daily.

Note on Inference Numbers: Once a 70/30 train-test split is done on the original dataset, the test dataset has ~85K samples. As you can see from the chart for inference benchmarks, we have reported data for 170K, 425K and 850K. This was done via duplicating the test dataset purely to investigate how a larger datasize would affect scalability of the performance because in the real world, it is likely that the dataset sizes would be much larger than the ones chosen here.

1. Executing DBSCAN clustering using stock scikit-learn vs. Intel® Extension for Scikit-Learn

2. Prediction (batch and streaming) from a stock LGBM model vs conversion to an Intel oneAPI optimized (daal4py) version of the model. Models were trained using clustered dataset

3. Accuracy benefit of training a model post DBSCAN clustering

Key Takeaways

From the reference kit, we can observe that the Intel Extension for Scikit-learn can accelerate DBSCAN clustering for stratifying the data by 30%. Clustering greatly helps training a more accurate model. For inference, using daal4py instead of stock can accelerate handling batch data by up to 3.86x and streaming data by 4.15x.

Fraud detection can be a compute intensive operation for inference workloads given the scale of credit card transactions occuring in the market. The performance improvement offered by daal4py for inferencing on streaming data on an LGBM model essentially means an enterprise can reduce their computational resources for inference (likely at the edge) at least by a factor of 4. This will greatly reduce their total cost of ownership.

Appendix

Experiment Setup

Note on the Hardware Infrastructure: The Azure based D8s_v5 machine was chosen as a typical VM used by customers. Please do not consider this as a recommendation for the workloads benchmarked here. Users are free to choose other architectures as well. However, please keep in mind that the performance gains may vary with the machine charactersistics such as generation, base speed, processor count, memory etc.

Dataset and Base code

The dataset used for this demo is a set of ~280K credit card transactions made in September 2013 by European cardholders. The base code was also sourced from Kaggle and can be found at the following location: <br> https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

Additional Remarks

Clustering was performed using two "most important features". The choice can greatly depend on the data and was made by executing a separate training pipeline. Details on that exercise are outside the scope of this project.

For LGBM training, we noticed a trade-off. If we chose to train a highly complex model, the clustering benefit disappears as the complex model can handle the large dataset by itself. But it will result in longer training times. That being said, clustering parameters were chosen empirically to maximize the proportion of fraudulent transactions. Memory limitations on the machine prevented us to increase the ratio beyond ~30%. Given more memory, it is likely that clustering-based stratification can provide accuracy benefits even for a more complex model.

Operating System: We recommend using an Linux OS for executing the reference solution presented here, e.g. RHEL or Ubuntu.

Notes

Please see this data set's applicable license for terms and conditions. Intel Corporation does not own the rights to this data set and does not confer any rights to it.

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