README.md

---
license: apache-2.0
---
# Revenue Assurance and Fraud Management (RAFM) with AI Assistance

## 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
![Revenue Assurance Data Structure](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/revenueassurance/data/rev_ass_data.png)<br>
## Results:
![Revenue Assurance Accuracy](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/revenueassurance/data/rev_ass_models_accuracy.png)<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
```