AI-Powered Finance Management Platform

Abstract

This paper integrates the latest studies in AI-driven personal finance management using a cloud-based platform architecture, integrating real-time data ingestion and pre-trained AI models. Automated expense tracking, budgeting, portfolio optimization, and predictive decision-making are dominant themes. We categorize available studies into domains like AI-driven advisory systems, portfolio optimization, explainable risk models, and data-driven analytics. Our review points out techniques employing streaming APIs and event-based workflows for scale, focusing on privacy, personalization, and trust of the user. For instance, a portfolio optimizer based on AI rebalances portfolios dynamically with real-time market information and AI advisors deliver customized suggestions analyzing market mood and transactions Ethical deployment and explainability are highlighted as critical to user adoption. The paper presents a taxonomy of AI-finance solutions and contrasts representative systems and suggests a workflow diagram of the platform.

Introduction

AI and machine learning are reshaping personal finance and wealth management by enabling real-time analysis, automation, and personalization. Financial applications now use ML, NLP, and pre-trained models to offer tailored budgeting, investing, and risk management advice. The system architecture emphasizes scalability, automation, and privacy, leveraging serverless databases (e.g., Supabase) and event-driven processors (e.g., Inngest). Explainable AI (XAI) is incorporated to ensure transparency in sensitive decisions like credit scoring.

II. Literature Survey

A broad literature review outlines AI’s transformative role in:

• Fraud detection and customer service via NLP and anomaly detection.

• Risk and credit assessment using XAI techniques like SHAP and LIME.

• Portfolio optimization with ML (e.g., XGBoost, Gradient Boosting).

• Personal finance education and debt management via AI advisors.

Key results include:

• Up to 97.6% accuracy in credit risk assessment with XAI.

• Improved financial literacy, fraud reduction, and user trust through automation and personalized insights.

III. Taxonomy of AI-in-Finance Applications

AI use in finance is categorized into five key areas:

1. Advisory Systems: Personalized financial advice and budgeting tools (e.g., AI advisors).

2. Portfolio Optimization: ML models for real-time investment rebalancing and forecasting.

3. Explainable AI (XAI): Models that justify decisions in credit scoring and compliance.

4. Data Analytics: Predictive insights for financial trend forecasting and risk detection.

5. System Architectures: Event-driven workflows, modular AI integration, and scalable platforms.

IV. Comparative Analysis

Common themes include customization, real-time adaptability, and ethics, with growing use of NLP (e.g., GPT-2) for financial data interpretation.

V. Methodology

The platform is designed around:

• Event-driven architecture using Inngest or ArcJet.

• Modular processing of transactions, market data, and user inputs.

• ML Models for:

  • Expense classification

  • Budget forecasting

  • Portfolio optimization

  • XAI-based explanation (e.g., SHAP)

• Cloud scalability and user-controlled privacy via encryption and consent-based sharing.

• User dashboards and alerts for real-time engagement.

This integrates insights from the literature into a responsive, ethical, and explainable financial system.

VI. Future Scope

Proposed extensions include:

1. Blockchain & DeFi: For immutable transactions and smart-contract savings.

2. Mobile Apps: With offline mode, push alerts, and biometric login.

3. Regulatory Compliance Module: AI-driven checks and reporting.

4. Federated Learning: Private model training across institutions.

5. Voice/AR Interfaces: For intuitive, hands-free financial interactions.

6. Gamification: Social finance goals and anonymous comparisons.

7. Advanced Analytics: Graph-based ML and sentiment analysis for long-term planning.

All are aimed at enhancing trust, accessibility, and user engagement, while adhering to ethical and regulatory standards.

Conclusion

The intersection of cloud?native architectures and artificial intelligence is bringing with it a new age for personal finance based on perpetual, data?driven decision support, rather than occasional human review. Event?driven pipelines allow for real?time ingestion of market and transaction data, while scalable serverless functions and microservices make it so even the most complex machine learning models can run at web scale. Consequently, consumers are able to take advantage of up-to-the-minute insights—e.g., real-time budget warnings, real-time spending projections, and automated anomaly detection—that would have been out of the question under legacy batch?based systems. However, with great technical might comes increased responsibility. Explainable AI platforms are no longer desirable niceties but essential elements that unshroud algorithmic reasoning from end users and regulators as well. In?built privacy protections ranging from fine?grained access controls and encryption?at?rest to differential?privacy methods at model?training time are indispensable for shielding sensitive financial information from abuse. Ethical governance frameworks will need to direct the development and deployment of these systems to ensure they contain bias, honor consumer self-determination, and meet changing regulatory requirements in jurisdictions. In the future, the most effective personal finance tools will be those that merge automated intelligence and human oversight—each contributing their respective strengths. Hybrid advisory frameworks, where routine analysis is performed by AI and humans concentrate on nuanced, empathetic advice, will be the standard. In addition, as generative AI advances, we can expect conversational interfaces that talk with users naturally, providing financial education and actionable feedback. Ultimately, by democratizing access to advanced analytics and personalized advice, AI-fueled finance platforms can not only optimize individual performance but also drive increased financial inclusion and resilience for diverse populations.

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Copyright

Copyright © 2025 Mrs. Deepthi C G, Jithesh J Shetty, I B Shreeya Jain, Lakshmi C R, Kiran Kumar D R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.