revenue-assurance

Revenue Assurance and Fraud Management (RAFM) with AI Assistance

GitHub: https://github.com/fenar/TME-AIX/tree/main/revenueassurance<br>

Project Overview

This project aims to deliver an RAFM prediction (if that particular telco transaction is fraudulent or not) with a AI model assistance with; <br> (1) Balanced Random Forest,<br> The model has trained on semi-synthetic telecom data to predict fraud cases and identify potential anomalies. The goal is to provide proactive revenue management and enhance revenue workflows.

Data

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Results:

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Steps to Test

(A) Potential Fraud Test: <br>

curl -X POST -H "Content-Type: application/json" -d '{
    "Call_Duration": 300,
    "Data_Usage": 10000,
    "Sms_Count": 50,
    "Roaming_Indicator": 1,
    "MobileWallet_Use": 1,
    "Plan_Type_prepaid": 1,
    "Plan_Type_postpaid": 0,
    "Cost": 500,
    "Cellular_Location_Distance": 100,
    "Personal_Pin_Used": 0, 
    "Avg_Call_Duration": 50,
    "Avg_Data_Usage": 8000
}' http://localhost:5000/predict

(B) Potential Non-Fraud Test: <br>

curl -X POST -H "Content-Type: application/json" -d '{
    "Call_Duration": 10,
    "Data_Usage": 300,
    "Sms_Count": 5,
    "Roaming_Indicator": 0,
    "MobileWallet_Use": 1,
    "Plan_Type_prepaid": 1,
    "Plan_Type_postpaid": 0,
    "Cost": 50,
    "Cellular_Location_Distance": 3,
    "Personal_Pin_Used": 1,
    "Avg_Call_Duration": 12,
    "Avg_Data_Usage": 350
}' http://localhost:5000/predict