Enhancing Financial Decision-Making: Tax Saving Recommendations and Fraud Detection Using XGBoost and Random Forest

DOI

10.2991/978-94-6463-858-5_288How to use a DOI?

Keywords

Fraud Detection; Tax Saving Recommendation; XGBoost; Random Forest; Financial Advisory; Predictive Analytics; Financial Strategies; Machine Learning; Personalized Tax Optimisation

Abstract

Personalized tax optimization and fraud detection pose a challenge to financial decision-making due to the constraints of conventional methods. This research provides a composite machine learning framework combining XGBoost with SHAP for personalized tax- saving recommendations and Random Forest with SMOTE for credit card fraud detection with improved robustness. The models were rigorously applied with high accuracy, precision, recall, and interpretability, with a substantial improvement over conventional methods. A phased workflow of data preprocessing, feature engineering, model training, and performance metrics evaluation was established. Results validated outstanding reliability and performance, significantly improving users’ financial management skills. Although constrained by data dependence and integration limitations, prospective incorporation into a real-time web-based platform and ongoing model enhancements hold out the possibility of increased flexibility and ease of use. This research goes a long way in supporting customized financial decision-making and secure financial transactions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Heer Dhandhukia
AU  - Aryan Dalvi
AU  - Sarthak Girish
AU  - Ankita Nagmote
PY  - 2025
DA  - 2025/11/04
TI  - Enhancing Financial Decision-Making: Tax Saving Recommendations and Fraud Detection Using XGBoost and Random Forest
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3440
EP  - 3454
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_288
DO  - 10.2991/978-94-6463-858-5_288
ID  - Dhandhukia2025
ER  -