GNN FSI modelcard++

models/model-cards/gnn-fsi-model-card.md

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+
+# Model Overview
+
+### Description:
+* This model shows an application of a graph neural network for fraud detection in a credit card transaction graph. A transaction dataset that includes three types of nodes, transaction, client, and merchant nodes is used for modeling. A combination of `GraphSAGE` along `XGBoost` is used to identify frauds in the transaction networks. <br>
+
+## References(s):
+1. https://stellargraph.readthedocs.io/en/stable/hinsage.html?highlight=hinsage
+2. https://github.com/rapidsai/clx/blob/branch-22.12/examples/forest_inference/xgboost_training.ipynb
+3. Rafaël Van Belle, Charles Van Damme, Hendrik Tytgat, Jochen De Weerdt,Inductive Graph Representation Learning for fraud detection (https://www.sciencedirect.com/science/article/abs/pii/S0957417421017449)<br> 
+
+## Model Architecture:
+It uses a bipartite heterogeneous graph representation as input for `GraphSAGE` for feature learning and `XGBoost` as a classifier. Since the input graph is heterogeneous, a heterogeneous implementation of `GraphSAGE` (HinSAGE) is used for feature embedding.<br>
+**Architecture Type:** 
+* Graph Neural Network and Binary classification <br>
+
+**Network Architecture:** 
+* GraphSAGE and XGBoost <br>
+
+## Input
+Transaction data with nodes including transaction, client, and merchant.<br>
+**Input Parameters:**  
+* None <br>
+
+**Input Format:** 
+* CSV format<br>
+
+**Other Properties Related to Output:** 
+* None<br>
+
+## Output
+An anomalous score of transactions indicates a probability score of being a fraud.<br>
+**Output Parameters:**  
+* None <br>
+
+**Output Format:** 
+* CSV<br>
+
+**Other Properties Related to Output:** 
+* None <br> 
+
+## Software Integration:
+**Runtime(s):** 
+* Morpheus  <br>
+
+**Supported Hardware Platform(s):** <br>
+* Ampere/Turing <br>
+
+**Supported Operating System(s):** <br>
+* Linux <br>
+
+## Model Version(s): 
+* 1.0 <br>
+
+### How To Use This Model
+This model is an example of a fraud detection pipeline using a graph neural network and gradient boosting trees. This can be further retrained or fine-tuned to be used for similar types of transaction networks with similar graph structures.
+
+# Training & Evaluation: 
+
+## Training Dataset:
+
+**Link:**
+* [fraud-detection-training-data.csv](models/dataset/fraud-detection-training-data.csv)  <br>
+
+**Properties (Quantity, Dataset Descriptions, Sensor(s)):** 
+* A training data consists of raw 753 synthetic labeled credit card transaction data with data augmentation in a total of 12053 labeled transaction data. <br>
+
+**Dataset License:**  
+* [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0) <br>
+
+## Evaluation Dataset:
+**Link:**  
+* [fraud-detection-validation-data.csv](models/dataset/fraud-detection-validation-data.csv)  <br>
+
+**Properties (Quantity, Dataset Descriptions, Sensor(s)):** 
+* Data consists of raw 265 labeled credit card transaction synthetically created<br>
+
+**Dataset License:**
+*  [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)<br>
+
+## Inference:
+**Engine:** 
+* Triton <br>
+
+**Test Hardware:** <br>
+* Other   <br>
+
+# Subcards
+## Model Card ++ Bias Subcard
+
+### What is the gender balance of the model validation data?  
+* Not Applicable
+
+### What is the racial/ethnicity balance of the model validation data?
+* Not Applicable
+
+### What is the age balance of the model validation data?
+* Not Applicable
+
+### What is the language balance of the model validation data?
+* Not Applicable
+
+### What is the geographic origin language balance of the model validation data?
+* Not Applicable
+
+### What is the educational background balance of the model validation data?
+* Not Applicable
+
+### What is the accent balance of the model validation data?
+* Not Applicable
+### Describe measures taken to mitigate against unwanted bias.
+* Not Applicable
+## Model Card ++ Explainability Subcard
+
+### Name example applications and use cases for this model. 
+* The model is primarily designed for testing purposes and serves as a small pretrained model specifically used to evaluate and validate the GNN FSI pipeline. Its application is focused on assessing the effectiveness of the pipeline rather than being intended for broader use cases or specific applications beyond testing.
+### Fill in the blank for the model technique.
+* This model is designed for developers seeking to test the GNN fraud detection pipeline with a small pretrained model on a synthetic dataset.
+### Name who is intended to benefit from this model. 
+* The intended beneficiaries of this model are developers who aim to test the performance and functionality of the GNN fraud detection pipeline using synthetic datasets. It may not be suitable or provide significant value for real-world transactions. 
+### Describe the model output.
+* This model outputs fraud probability score b/n (0 & 1). 
+
+### List the steps explaining how this model works. (e.g., )  
+* The model uses a bipartite heterogeneous graph representation as input for `GraphSAGE` for feature learning and `XGBoost` as a classifier. Since the input graph is heterogeneous, a heterogeneous implementation of `GraphSAGE` (HinSAGE) is used for feature embedding.<br>
+
+### Name the adversely impacted groups (protected classes) this has been tested to deliver comparable outcomes regardless of:
+* Not Applicable
+
+### List the technical limitations of the model.
+* This model version requires a transactional data schema with entities (user, merchant, transaction) as requirement for the model.
+
+### What performance metrics were used to affirm the model's performance?
+* Area under ROC curve and Accuracy
+
+### What are the potential known risks to users and stakeholders? 
+* None
+
+### What training is recommended for developers working with this model?  If none, please state "none."
+* Familiarity with the Morpheus SDK is recommended for developers working with this model.
+### Link the relevant end user license agreement 
+* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-23.07/LICENSE)
+
+## Model Card ++ Saftey & Security Subcard
+
+### Link the location of the training dataset's repository (if able to share).
+* [training dataset](models/datasets/training-data/fraud-detection-training-data.csv)
+
+### Is the model used in an application with physical safety impact?
+* No
+
+### Describe physical safety impact (if present).
+* Not Applicable
+
+### Was model and dataset assessed for vulnerability for potential form of attack?
+* No
+### Name applications for the model.
+* Used for testing fraud detection application in Morpheus pipeline, under the defined dataset schema description.
+### Name use case restrictions for the model.
+* The model's use case is restricted to testing the Morpheus pipeline and may not be suitable for other applications.
+### Has this been verified to have met prescribed quality standards?
+* No
+
+### Name target quality Key Performance Indicators (KPIs) for which this has been tested.  
+* Not Applicable
+### Technical robustness and model security validated?
+* Not Applicable
+### Is the model and dataset compliant with National Classification Management Society (NCMS)?
+* Not Applicable
+
+### Are there explicit model and dataset restrictions?
+* No
+
+### Are there access restrictions to systems, model, and data?
+* No
+### Is there a digital signature?
+* No
+
+## Model Card ++ Privacy Subcard
+
+### Generatable or reverse engineerable personally-identifiable information (PII)?
+
+* Neither
+
+### Was consent obtained for any PII used?
+* Not Applicable (Data is extracted from synthetically created credit card transaction,refer[3] for the source of data creation)
+
+### Protected classes used to create this model? (The following were used in model the model's training:)
+
+* Not applicable
+
+### How often is dataset reviewed?
+* The dataset is initially reviewed upon addition, and subsequent reviews are conducted as needed or upon request for any changes.
+
+### Is a mechanism in place to honor data
+* Yes
+### If PII collected for the development of this AI model, was it minimized to only what was required? 
+* Not applicable
+
+### Is data in dataset traceable?
+* No
+### Scanned for malware?
+* No
+### Are we able to identify and trace source of dataset?
+* Yes
+
+### Does data labeling (annotation, metadata) comply with privacy laws?
+* Not applicable
+### Is data compliant with data subject requests for data correction or removal, if such a request was made?
+* Not applicable