Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
May-June 2026

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Ratio Analysis and AI-Driven Profitability of JPMorgan Chase

Author(s) Mr. Patel Muhammad Afan, Ms Poonum S Raibagi
Country India
Abstract This research paper examines how artificial intelligence, technology spending, and financial ratio performance are reshaping the profitability profile of JPMorgan Chase across the 2020–2027 period. The study is based on the structure reflected in the user-provided journal-style format and develops the paper from the user’s existing research project on JPMorgan Chase, including the stated problem, objectives, literature base, methodology, analytical discussion, findings, and conclusions.
The paper adopts a secondary-data, quantitative approach and relies on the project’s stated use of SEMsC filings, earnings materials, Google Finance references, and present value analysis to assess whether AI expenditure functions as a cost burden or a strategic profitability driver for the bank. The core argument developed in the original project is that JPMorgan Chase’s large-scale technology investment is better interpreted as a strategic moat that compresses the efficiency ratio, supports risk management, and strengthens long-run earnings capacity rather than simply increasing operating cost.
The analysis in the source project positions efficiency ratio compression, AI-enabled fraud reduction, process automation, data scale, and digital customer expansion as central explanatory variables behind the bank’s improving performance outlook. The paper concludes that AI-led transformation is not only improving internal operational productivity at JPMorgan Chase but is also altering the competitive structure of modern banking by rewarding scale, proprietary data, and algorithmic capability.
Keywords JPMorgan Chase, artificial intelligence, profitability, ratio analysis, banking efficiency, digital transformation, present value, financial performance.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-16
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E-ISSN 3048-7641
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