Enhancing Financial Efficiency through Anomaly Detection and Predictive Modelling
Overview: A prominent telecom company faced challenges in managing and optimizing expenses due to the complexity and volume of its data. The goal was to identify areas of potential overspending and implement cost-saving measures without compromising service quality.
Solution: The company implemented the TEDSS (Telecom Executive Decision Support System) product, an AI-driven solution designed to amalgamate various AI/ML algorithms into a unified model for anomaly detection and predictive modelling. TEDSS employed deep learning (DL) and machine learning (ML) algorithms to analyse site-level traffic patterns, subscriber trends, and total expenses.
Approach
- Unified AI/ML Model: TEDSS integrated multiple AI/ML algorithms to form a cohesive framework capable of addressing multifaceted challenges and investigating unusual patterns in expenses data.
- Expense Analysis: Utilizing DL and ML algorithms, TEDSS analyzed site-level traffic patterns and subscriber trends, comparing them with total expenses to identify areas of potential overspending.
- Predictive Models: Advanced AI-driven predictive models, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), and gradient boosting machines (GBMs), were employed to generate comprehensive reports for expense management.
- Actionable Insights: Through sophisticated AI analysis, TEDSS pinpointed cost reduction areas without compromising quality or performance, ensuring optimal resource allocation.
Outcome
- Identified Overspending: The telecom company successfully identified and addressed areas of overspending, leading to significant cost savings by 35%.
- Enhanced Decision-Making: TEDSS provided actionable insights, enabling the company to make informed decisions to enhance financial efficiency.
- Proactive Management: The advanced predictive models facilitated proactive expense management and budgetary planning, driving financial efficiency across operations
