 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 22135
Article Published : 8686
Total Authors : 22851
Total Reviewer : 819
Total Countries : 160
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | CLOUD-NATIVE AI FOR RISK ASSESSMENT IN BANKING INSURANCE |
| Authors Name: | SRIDHAR MADASAMY M |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_189403
|
| Published Paper Id: | IJRTI2403070 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Risk assessment is pivotal in banking and insurance, guiding applicant classification. Underwriting processes determine application decisions and policy pricing. As data volumes surge and analytics progress, automation accelerates underwriting for swift application processing. This study endeavors to augment risk assessment in banking and insurance through predictive analytics. Employing a real-world dataset with anonymized attributes exceeding one hundred, the research conducts comprehensive analysis to propose solutions for enhanced risk assessment in bank insurance operations. Initiating with dataset collection, the framework ensures the acquisition of comprehensive data sources pertinent to risk evaluation. Subsequently, data pre-processing procedures are implemented to cleanse the dataset of noise, outliers, and missing values, essential for ensuring data integrity and consistency. Dimensionality reduction techniques are then applied to streamline the dataset and enhance computational efficiency. Specifically, to find and extract important characteristics helpful for risk assessment, Linear Discriminant Analysis (LDA) based feature extraction techniques and Improved Artificial Bee Colony (IABC) based feature selection techniques are used. Leveraging cloud infrastructure facilitates the scalability and flexibility required for processing large-scale datasets efficiently. The multi-model selection phase encompasses the application of various algorithms, including Recurrent Neural Networks (RNN), Convolutional Artificial Neural Networks (CANN), and Multiple Linear Regression (MLR), for predictive modeling. Each algorithm brings unique strengths to the table, enabling comprehensive risk assessment from different angles. The framework aims to optimize risk assessment processes in banking and insurance domains, ultimately enhancing decision-making accuracy and operational efficiency. |
| Keywords: | |
| Cite Article: | "CLOUD-NATIVE AI FOR RISK ASSESSMENT IN BANKING INSURANCE", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.9, Issue 3, page no.485 - 492, March-2024, Available :http://www.ijrti.org/papers/IJRTI2403070.pdf |
| Downloads: | 000205259 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2403070
Registration ID:189403
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: 485 - 492 Country: Pondicherry, Tamilnadu, India Research Area: Management Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2403070 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2403070 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||