AI-Driven Modernization: Transforming Financial Services Applications

This article presents a comprehensive framework for orchestrating digital transformation in the financial services industry through AI-driven application modernization approaches. It examines the current landscape of banking technologies, highlighting the challenges posed by legacy systems and traditional modernization methods. The article establishes a structured transformation framework that integrates strategic, technical, organizational, and risk dimensions specifically tailored to financial institutions. By leveraging artificial intelligence throughout the modernization lifecycle, from initial assessment to continuous optimization, financial organizations can make more informed decisions, predict and mitigate risks, and substantially improve modernization outcomes. The article proposes a multi-dimensional approach to measuring transformation success and calculating ROI, emphasizing outcome-based metrics that prioritize business value over technical outputs. Through evidence-based analysis across multiple financial institutions, this article demonstrates that AI-enhanced modernization strategies deliver superior results compared to traditional approaches, providing actionable insights for accelerating digital transformation while minimizing risk and maximizing value creation.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-16
DOI
https://doi.org/10.5281/zenodo.17135603
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Driven Modernization: Transforming Financial Services Applications

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

AI-Driven Modernization: Transforming Financial Services Applications

article en

Abstract

This article presents a comprehensive framework for orchestrating digital transformation in the financial services industry through AI-driven application modernization approaches. It examines the current landscape of banking technologies, highlighting the challenges posed by legacy systems and traditional modernization methods. The article establishes a structured transformation framework that integrates strategic, technical, organizational, and risk dimensions specifically tailored to financial institutions. By leveraging artificial intelligence throughout the modernization lifecycle, from initial assessment to continuous optimization, financial organizations can make more informed decisions, predict and mitigate risks, and substantially improve modernization outcomes. The article proposes a multi-dimensional approach to measuring transformation success and calculating ROI, emphasizing outcome-based metrics that prioritize business value over technical outputs. Through evidence-based analysis across multiple financial institutions, this article demonstrates that AI-enhanced modernization strategies deliver superior results compared to traditional approaches, providing actionable insights for accelerating digital transformation while minimizing risk and maximizing value creation.

Zenodo (CERN European Organization for Nuclear Research)
Bank of New York Mellon (US)
Openalex Percentile: Top 40%
Geochemistry and Geologic Mapping
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