Enabling Secure Transactions: A Comprehensive Fraud Detection System for Banking and Financial Services Integration
Binding seamless Technology with Finance
GeneralPublished on: Fri Feb 10 2023
The client is a leading telecom provider firm based in the United States. They wanted to build an AI-enabled predictive analytics platform to generate reports for telecom KPIs and predictive analytics like the RSRP & RSRQ prediction, Signal Strength prediction, and Gap Fill prediction for Virtual Drive Tests of networks for any telecom market of the world.
The major challenge faced by our client was generating and analyzing the insights from various telecom providers. They had the data and insights only for the networks maintained by them & for the specific markets. The client wanted a platform to analyze the historical data from multiple sources and gain insights into customer behavior which can further help them provide a more customized experience through better prediction & forecasting.
To meet the client’s requirement, Hexaview developed a Big Data Analytics and ML Predictive platform. The platform gathers raw data from multiple telecom crowd-data provider companies. This data is further parsed and stored in a data warehouse with the help of various big data processing tools like Hadoop, Hive, Impala, and Apache Spark.
The KPI prediction models were trained and deployed for Signal Strength Prediction and Gap Fill Prediction for Virtual Drive test networks.
1. Languages Used
Backend:- Python, HDFS, Hive, Impala, Apache Spark, Tensorflow, cuDF, cuML
Frontend:- ReactJS, High Chart
2. Algorithm(s) Used – DBSCAN , SVM , RNN
3. Libraries – Apache Spark, Tensorflow, cuDF, cuML, Parquet
Around 250- 300 GB of Crowd Data of the telecom industry is processed and maintained in the warehouse every day.
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