Merge pull request NVIDIA#165 from BradReesWork/ai_fraud

Create a folder with link to the Credit Card Fraud AI workflow

credit_fraud_detection/README.md

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+# <p style="text-align: center;">Graph Neural Network Based Model Building<br>for Credit Card Fraud Dectection</p>
+<p align="center">
+  <img src="./Splash.jpg" width="70%"/>
+</p>
+
+What is presented in this open-source folder are example Jupyter notebooks of
+three steps within a larger Credit Card Fraud Detection Workflow.  Those steps
+being: (a) data prep, (b) model building, and (c) inference. Those three steps
+are presented as independent Jupyter Notebooks.
+
+What this example does not show, and which needs to be highlighted, is the
+complexity of scaling the problem. The sample datasets are of trivial single-GPU
+size. The NVIDIA RAPIDS suite of AI libraries has been proven to scale while still
+providing leading performance.
+
+
+# [Find the Notebooks here](https://github.com/nv-morpheus/morpheus-experimental/tree/branch-24.10/ai-credit-fraud-workflow)</br></br>
+
+
+## Background
+Transaction fraud is
+[expected to exceed $43B by 2026](https://nilsonreport.com/articles/card-fraud-losses-worldwide/)
+and poses a significant challenge upon financial institutions to detect and prevent
+sophisticated fraudulent activities. Traditionally, financial institutions
+have relied upon rules based techniques which are reactive in nature and
+result in higher false positives and lower fraud detection accuracy. As data
+volumes and attacks have become more sophisticated, accelerated machine and
+graph learning techniques become mandatory and is a more proactive approach. 
+AI for fraud detection uses multiple machine learning models to detect anomalies
+in customer behaviors and connections as well as patterns of accounts and
+behaviors that fit fraudulent characteristics.
+
+Fraud detection has been a challenge across banking, finance, retail and
+e-commerce. Fraud doesn’t only hurt organizations financially, it can also
+do reputational harm. It’s a headache for consumers, as well, when fraud models
+from financial services firms overreact and register false positives that shut
+down legitimate transactions. Financial services sectors are developing more
+advanced models using more data to fortify themselves against losses
+financially and reputationally. They’re also aiming to reduce false positives
+in fraud detection for transactions to improve customer satisfaction and win
+greater share among merchants.
+
+As data needs grow and AI models expand in size, intricacy, and diversity,
+energy-efficient processing power is becoming more critical to operations in
+financial services. Traditional data science pipelines lack the necessary
+acceleration to handle the volumes of data involved in fraud detection,
+resulting in slower processing times, which limits real-time data analysis
+and detection of fraud. To efficiently manage large-scale datasets and deliver
+real-time performance for AI in production, financial institutions must shift
+from legacy infrastructure to accelerated computing.
+
+The Fraud Detection AI workflow offers enterprises an end-to-end solution using
+the NVIDIA accelerated computing platform for GPU-accelerated data processing
+and AI deployment, enabling real-time analysis and detection of fraudulent
+activities. It is important to note that there are several types of fraud.
+The initial focus is on supervised credit card transaction fraud. Other areas
+beyond fraud that could be converted to products include:New Account Fraud,
+Account Takeover, Fraud Ring Detection, Abnormal Behavior, and Anti-Money
+Laundering.  
+
+## High-Level Architecture
+The general fraud architecture, as depicted below at a very high-level, uses
+Morpheus to continually inspect and classify all incoming data. What is not
+shown in the diagram is what a customer should do if fraud is detected, the
+architecture simply shows tagged data being sent to downstream processing.
+Those processes should be well defined in the customers’ organization.
+Additionally, the post-detected data, what we are calling the Tagged Data
+Stream, should be stored in a database or data lake.  Cleaning and preparing
+the data could be done using Spark RAPIDS. 
+
+Fraud attacks are continually evolving and therefore it is important to always
+be using the latest model. That requires the model(s) to be updated often as
+possible. Therefore, the diagram depicts a loop where the GNN Model Building
+process is run periodically, the frequency of which is dependent on model
+training latency. Given how dynamic this industry is with evolving fraud
+trends, institutions who train models adaptively on a frequent basis tend to
+have better fraud prevention KPIs as compared to their competitors.
+
+</br>
+<p align="center">
+  <img src="./High-Level.jpg" width="50%"/>
+</p>
+</br>
+
+### This Workflow
+The above architecture would be typical within a larger financial system where incoming data run through the inference engine first and then periodically a new model build. However, for this example, the workflow is will start with model building and end with Inference. The workflow is depicted below:
+
+</br>
+<p align="center">
+  <img src="./this-workflow.jpg" width="60%"/>
+</p>
+</br>
+
+ 1.	__Data Prep__: the sample dataset is cleaned and prepared, using tools like NVIDIA RAPIDS for efficiency. Data preparation and feature engineering has a significant impact on the performance of model produced. See the section of data preparation for the step we did get the best results
+    - Input: The sample dataset
+    - Output: Two files; (1) a data set for training the model and a (2) dataset to be used for inference.
+
+2. __Model Building__: this process takes the training data and feeds it into cugraph-pyg for GNN training. However, rather than having the GNN produce a model, the last layer of the GNN is extracted as embeddings and feed into XGBoost for production of a model.
+    - Input: Training data file
+    - Output: an XGBoost model and GNN model that encodes the data
+
+3. __Inference__: The test dataset, extracted from the sample dataset, is feed into the Inference engine. The output is a confusion matrix showing the number of detected fraud, number of missed fraud, and number of misidentified fraud (false positives) 
+
+
+<br/>
+
+
+## Copyright and License
+Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+<br/>
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.