Machine Learning Applications in Telecom and Banking

The uses of machine learning (ML) in the banking and telecommunication sectors are investigated over the course of this research paper. The results of the article indicate that by means of enhanced customer experience, identification of fraudulent behaviour, risk management, and operational efficiency, machine learning algorithms are changing these sectors. This article covers several machine learning methods including supervised and unsupervised learning, deep learning, reinforcement learning, and others together with their particular uses in the banking and telecommunications sectors especially. To show how machine learning is affecting different sectors, case papers, real-world case studies, and samples abound. Furthermore included in the article are possible future trends and advancements in the field as well as the difficulties and restrictions related to the application of machine learning solutions.

Authors

Institutions

Publication Details

Journal
Integrated Journal for Research in Arts and Humanities
Published
2024-11-20
DOI
https://doi.org/10.55544/ijrah.4.6.8
Citations
12
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
5.89
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine Learning Applications in Telecom and Banking

Sai Krishna Shiramshetty, Naveen Bagam, Mouna Mothey, Sri Nikhil Annam et al.
12 citations
Integrated Journal for Research in Arts and Humanities
Stock Market Forecasting Methods
5.89
article

Machine Learning Applications in Telecom and Banking

Sai Krishna Shiramshetty, Naveen Bagam, Mouna Mothey, Sri Nikhil Annam, Santhosh Bussa
article en
12 citations

Abstract

The uses of machine learning (ML) in the banking and telecommunication sectors are investigated over the course of this research paper. The results of the article indicate that by means of enhanced customer experience, identification of fraudulent behaviour, risk management, and operational efficiency, machine learning algorithms are changing these sectors. This article covers several machine learning methods including supervised and unsupervised learning, deep learning, reinforcement learning, and others together with their particular uses in the banking and telecommunications sectors especially. To show how machine learning is affecting different sectors, case papers, real-world case studies, and samples abound. Furthermore included in the article are possible future trends and advancements in the field as well as the difficulties and restrictions related to the application of machine learning solutions.

Integrated Journal for Research in Arts and HumanitiesVol. 4(6)
Film Independent (US)
Openalex Percentile: Top 4%
Stock Market Forecasting Methods
5.89
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.