REST API for FRAUD DETECTION
<<<<<<< HEAD The API acces points to be able to use the api , you need first install the requirements
pip install flask Then test the application by runing the code :
python api.py the app will be running on local host port 5000 with the endpoints :
/api/v0/verify This endpoint get a JSON object in a post method , that represents the new transaction
{ "Merhchant": "M856947123", "Customer": "C1093826151", "Category":"es_health", "Amount" : 149.62 } The return result looks like this :
{
"id": 0,
"prediction": "[0]"
}
/api/v0/info this endpoint give you the information you need to know about the API , the result look like this , you can add as many details as you like
The steps below are just one example. Your approach will vary depending on your goals and the data itself.
1-Use graph queries to uncover a suspicious pattern, such as multiple users coming from the same IP address. (Some of the techniques used in the first-party fraud example from blog 3 in this series will also apply.)
2-Use Community Detection algorithms to identify strongly connected communities engaged in known fraud across various accounts using email addresses, phone numbers, authorized users and previously flagged activity.
3-Use the Louvain Modularity graph algorithm to examine whether hierarchies exist among these communities. Set thresholds to separate petty thieves from fraud rings so that investigators prioritize their efforts.
4-Use a Centrality algorithm like PageRank to uncover influential individuals and to identify high frequency paths.
5-After verifying the pattern of one fraud ring, use a Similarity algorithm such as Jaccard to identify other potential fraud participants and rings across your data.
6-Once the approaches to find fraud rings have been validated by investigators, and a labeled and scored dataset has been created, you can use these graph-based features in a machine learning pipeline.
7-Extract the calculated node and relationship properties – graph features from the previous step – into your ML environment (e.g., into a Python notebook). Join those properties with any other relevant tabular data. Use variable selection and model-building techniques to pinpoint the most important features and use them to predict future fraudulent activities or entities.
8-Once you’re satisfied with your results, move your model into production. Write back any relevant findings to the Neo4j Graph Database to support further exploration.
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