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---
license: apache-2.0
---
# 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
<br>
## Results:
<br>
## 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
``` |