Next-Gen Investment Systems: AI, Learning and Secure Trading

Vijayalakshmi M, G Yadu Praveer, Mithun Veeramaneni

2025

Abstract

The proposed procedure is based on the Prediction and investment help in the stock exchange through a powerful, completely integrated Demat level. Using deep learning algorithms and time series analysis, the system efficiently analyses stock market trends and offers accurate predictions that enable investors to make informed decisions. While time series analysis uses static historical stock data to uncover patterns and trends, more sophisticated deep-learning models (such as long short-term memory (LSTM) networks or recurrent neural networks (RNNs)) are able to achieve much greater levels of accuracy through their ability to encapsulate relationships in the data. Seamless Investment Experience with Intuitive Demat Platform. Here are the major features that comprise of real-time stock assessment, personalized portfolio management, and all-in-one risk evaluation tools. As such, they deploy strict data security measures and compliance with financial regulations to build user trust already during the registration phase. The system provides powerful financial forecasting capability while also helping users minimize the complexity of the investing process, resulting in improved financial performance. No = Major data driven & intuitive system to serve investment management.

Paper Citation

in Harvard Style

M V., Yadu Praveer G. and Veeramaneni M. (2025). Next-Gen Investment Systems: AI, Learning and Secure Trading. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 499-505. DOI: 10.5220/0013932000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Vijayalakshmi M and G Yadu Praveer and Mithun Veeramaneni},
title={Next-Gen Investment Systems: AI, Learning and Secure Trading},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={499-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013932000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Next-Gen Investment Systems: AI, Learning and Secure Trading
SN - 978-989-758-777-1
AU - M V.
AU - Yadu Praveer G.
AU - Veeramaneni M.
PY - 2025
SP - 499
EP - 505
DO - 10.5220/0013932000004919
PB - SciTePress