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revenue-assurance

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  This project aims to deliver an RAFM prediction (if that particular telco transaction is fraudulent or not) with a AI model assistance with; <br>
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  (1) Balanced Random Forest,<br>
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  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.
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  ## Data
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  ![Revenue Assurance Data Structure](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/revenueassurance/data/rev_ass_data.png)<br>
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  ## Results:
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  ![Revenue Assurance Accuracy](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/revenueassurance/data/rev_ass_models_accuracy.png)<br>
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  ## Steps to Test
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  (A) Potential Fraud Test: <br>
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  ```
 
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  This project aims to deliver an RAFM prediction (if that particular telco transaction is fraudulent or not) with a AI model assistance with; <br>
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  (1) Balanced Random Forest,<br>
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  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.
 
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  ## Data
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  ![Revenue Assurance Data Structure](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/revenueassurance/data/rev_ass_data.png)<br>
 
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  ## Results:
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  ![Revenue Assurance Accuracy](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/revenueassurance/data/rev_ass_models_accuracy.png)<br>
 
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  ## Steps to Test
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  (A) Potential Fraud Test: <br>
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  ```