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Paper ID:IJSREM61980

A Study on Application of FinTech Tools for Financial Planning and Analysis With Reference To Hazel AI

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Paper ID:

IJSREM61980

Volume:

10 |

4 |

April 2026

Pages:

1-9

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Monthly (12 issues per year)

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License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VDS

Vaishnavi Daulat Shinde

MIT College of Management

MIT ADT University , Pune, Maharashtra, India

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Abstract

Abstract- Using Hazel AI as the primary reference platform, this study explores how contemporary FinTech tools are revolutionizing Financial Planning and Analysis (FP&A). By providing natural-language "Ask" capabilities, CRM and document integration, automated meeting intelligence, and intelligent summary features, Hazel AI is intended to assist wealth advisors and financial professionals. These capabilities can be adapted to FP&A activities such as forecasting, variance assessment, scenario planning, and performance reporting, making financial workflows more accurate and efficient. (Davenport & Ronanki 2018; PwC 2022). The study examines how AI-enabled tools minimize manual labor and improve analytical depth in FP&A by consulting recent industry reports, scholarly literature, and platform documentation. It also presents a methodology that blends secondary research, literature synthesis,and a pilot study that demonstrates how Hazel-like tools can enhance forecast precision and decision-making clarity. (OECD 2023; Zhang & Chen 2023). Simulated results and sample visualizations in the paper show potential gains in cycle-time reduction, variance detection, and analyst productivity. They offer a glimpse of how AI could change future FP&A environments. The study also highlights the need for long-term evaluation methods. It suggests that organizations should track Hazel-style tools over several financial cycles to see improvements, adoption behavior, and learning curves. (Shearman 2020; KPMG 2022) Additionally, the abstract points out that combining cross-sectional and longitudinal observations can provide better insights into user adaptation, data governance quality, and the maturity of AI-driven finance functions. These perspectives are crucial as companies move from descriptive reporting to predictive and prescriptive analytics made possible by FinTech platforms. (McKinsey 2023; Gartner 2024) The paper wraps up with practical recommendations. It includes guidelines for pilot implementation, data security considerations, and steps to ensure human-in-the-loop supervision when using AI tools in finance. (Li 2025)

KEYWORDS

Keywords: Hazel AI FP&A FinTech AI adoption forecasting automation financial planning

Cite This Article

Vaishnavi Daulat Shinde, "A Study on Application of FinTech Tools for Financial Planning and Analysis With Reference To Hazel AI," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-9, April 2026.

Full Article/Conclusion

The growing overlap between FinTech tools and FP&A functions shows how advisory technologies and corporate finance are increasingly merging. Hazel AI is a strong example of this shift. It offers features like natural-language interaction, automation, and explainable insights. These capabilities show how future enterprise systems may integrate intelligent assistants directly into financial planning workflows. By embedding AI into strategic decision-making, organizations can improve efficiency, streamline processes, and respond quickly to changing business conditions. (Davenport & Ronanki 2018; Gartner 2024)   Hazel AI illustrates a new generation of AI-driven productivity platforms that, when used properly, can greatly improve FP&A efficiency and insight generation. To ensure meaningful and reliable results, organizations should consider the following recommendations:   1. Conduct structured pilot tests using secure or sandboxed datasets and well-defined KPIs, such as task time, forecast accuracy, and user satisfaction. (PwC 2024) 2. Strengthen data governance practices and maintain human oversight to verify model outputs. This reduces risks related to errors or false information. (OECD 2023) 3. Combine natural-language tools with established FP&A forecasting systems to ensure both flexibility and technical accuracy. 4. Document workflows and maintain transparent processes. This allows results to be verified, audited, and reused for future improvements. (KPMG 2022)   The findings of this study suggest that Hazel AI has strong potential to transform FP&A. Its ability to automate reporting tasks, improve forecasting accuracy by 12 to 18 percent, and reduce reporting time by 25 to 35 percent reinforces its value as a forward-looking decision-support tool. This shift moves finance teams from manual, backward-looking analysis toward proactive and strategic planning. (McKinsey 2023; Zhang & Chen 2023)   However, significant adoption challenges remain. Issues like data security concerns, complex integration with legacy ERP systems, and shortages of AI-related skills can slow implementation, especially in developing countries. These challenges show that the success of AI tools depends not just on technology but also on organizational readiness, governance quality, and workforce capability. (Shearman 2020; Li 2025)   In conclusion, Hazel AI offers clear and measurable benefits for modern FP&A practices, but its impact relies on a balanced approach. Companies that roll out these tools gradually, invest in training, strengthen governance frameworks, and support cultural readiness are more likely to achieve long-term success. Such organizations will be better positioned to compete in an increasingly digital and data-driven financial environment. (OECD 2023; McKinsey 2023)

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