Kuber: An AI-Driven Finance Management Solution Integrating Generative LLMs for Personalized Advisory

Abstract

Kuber is a personalized finance management platform that leverages artificial intelligence to deliver tailored financial advice. This paper presents the design and evaluation of Kuber, focusing on its AI-driven advisory engine (using Google’s Gemini model) and finance management methodology. The system collects a user’s financial information – income, expenses, debts, assets, and goals – and generates customized recommendations for budgeting, saving, debt repayment, and investment. Using advanced AI capabilities for data analysis and natural language generation, Kuber bridges the gap between complex financial planning and user-friendly advice. A case study of a 19-year-old user with moderate income and debt is discussed to illustrate how Kuber formulates an optimized plan: the user achieves a 70% savings rate, expedited debt clearance, and guided investments to meet both short-term purchases and long-term growth targets. The results demonstrate that AI-driven personalized advisory can make sound financial planning accessible and effective for individuals.

Introduction

Overview

Managing personal finances is difficult for many due to a lack of financial knowledge and the high cost of traditional human advisors. Artificial Intelligence (AI) offers a promising alternative by providing data-driven, real-time, and personalized financial advice. AI tools like robo-advisors are increasingly popular, managing nearly $870 billion in 2022, with projections to reach $1.4 trillion by 2024.

Kuber – AI-Powered Finance Platform

Kuber is developed as an AI-driven solution to make personal financial planning accessible, accurate, and personalized. It uses Google’s Gemini, a powerful generative AI, to simulate expert-level advice in budgeting, savings, debt management, and investment strategies.

System Architecture

Kuber operates through three layers:

1. Data Input Layer – Users provide details like income, expenses, debts, and financial goals.

2. Processing Layer – Calculates metrics such as savings rate, expense categories, and debt ratios.

3. AI Advisory Layer – Powered by Gemini, this layer generates tailored advice based on financial principles and user data.

AI Advisory Capabilities

Gemini produces a structured, easy-to-understand financial plan covering:

• Budget Analysis: Breaks down income vs. expenses.

• Savings Plan: Advises how much to save and where to allocate.

• Debt Management: Suggests payment strategies based on user goals and interest rates.

• Investment Advice: Tailored to the user’s risk tolerance.

• Goal Planning: Guidance on achieving short- and long-term goals (e.g., retirement, purchases).

Personalization and Validation

Kuber personalizes advice for each user:

• Dynamic prompts tailored to the user’s financial profile.

• Financial heuristics like the 50/30/20 rule, debt-to-income ratio, and emergency fund benchmarks are embedded.

• Validation mechanisms ensure AI suggestions are numerically feasible and contextually appropriate.

• The system can prompt AI corrections if unrealistic recommendations are detected.

User Case Study

A representative example involves a 19-year-old student earning INR 10,000/month with INR 20,000 in savings and INR 9,000 debt. Kuber’s recommendations:

• Expense Analysis: Identified a high 70% savings rate with only INR 3,000 spent monthly, showing disciplined spending habits.

• Debt Strategy: Suggested increasing debt payments from INR 1,000 to INR 4,000 per month to clear liabilities faster.

• Savings & Investment: Recommended channeling surplus into a diversified investment plan post-debt repayment, matched to the user's aggressive risk tolerance.

• Clarity & Education: The AI explained concepts like the importance of emergency funds and how to prioritize high-interest debt in simple terms.

Modeling & Prompt Engineering

To maximize Gemini's effectiveness:

• Prompts are structured and specific, including clear bullet-pointed data and context.

• Pre-computed indicators guide the AI’s focus.

• Post-generation checks ensure completeness and correctness of the output.

• The system encourages iterative interaction with the AI, refining advice if initial suggestions are lacking or unclear.

Conclusion

In this paper, we presented Kuber, an AI-driven finance management solution that employs the Gemini generative AI model to provide personalized financial advisory. The project demonstrates how advanced AI can be harnessed to interpret an individual’s financial data and produce a tailored action plan covering budgeting, saving, debt elimination, and investment strategies. The methodology centered on combining rule-based financial calculations with the flexibility and depth of a large language model, yielding results that are both numerically sound and contextually customized to the user. The example scenario of a young adult with modest income underscored several benefits of Kuber: the user received clear insights into their finances (e.g., expense breakdown and savings rate), was guided to expedite debt repayment, and was given a structured yet realistic plan to achieve personal goals like buying a desired item and boosting future income. Importantly, the advice was delivered in a conversational, motivating tone – an approach that can improve user engagement and adherence to the plan. This highlights the advantage of AI-driven advisory over traditional, static budgeting tools: Kuber not only crunches the numbers but also serves as a virtual financial coach, adapting its guidance as the user’s life circumstances evolve. The successful deployment of Kuber opens avenues for further development. Future work could integrate real-time financial data feeds (such as bank transaction updates) to automate input and continuously refine advice. Incorporating predictive analytics – for instance, forecasting future savings growth or investment returns – is another potential enhancement. Additionally, while our results focused on a single-user case, more extensive user testing would be valuable to evaluate Kuber’s effectiveness across diverse financial scenarios and to measure improvements in users’ financial health over time. Ensuring data security and user trust will remain paramount as we handle sensitive financial information and AI-generated advice. In conclusion, Kuber exemplifies a step towards accessible, AI-enhanced personal finance management. By marrying robust financial planning principles with state-of-the-art AI (Gemini), the solution empowers users to make informed decisions and progress toward their financial goals. The positive outcomes from the initial case study suggest that AI-driven personalized advisory can indeed function as a scalable, affordable alternative to human financial advisors for many people, fostering better money management habits and financial well-being.

References

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Copyright

Copyright © 2025 Tejas Sonawane, Vatsalya Tiwari, Dania Shah. 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.