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This research explores the potential for Ernst & Young (EY) to leverage Robotic Process Automation (RPA) in the financial sector with a focus on Accounts Payable, Accounts Receivable, and General Ledger processes. Thematic analysis revealed prevalent manual processes indicating that automation could significantly enhance efficiency and strategically allocate human resources. The key themes include operational inefficiencies, financial process optimization, skills development and training, employee well-being, efficiency and strategic solutions, risk in automation, and building agile teams.
The study identified challenges such as communication gaps, employee unhappiness, and concerns about job security within EY's finance team. To address these challenges, various models and frameworks were proposed aligning with identified themes. Solutions include the application of Six Sigma for operational inefficiencies, the 70:20:10 model for skills development, Agile Risk Management for handling risks in automation, Lewin's Change Management Model for addressing change-related challenges, and the Scrum framework for building an agile RPA team.
The discussion highlights the relevance and application of each model emphasizing the need for a systematic approach to problem-solving. The proposed models offer tailored solutions to specific challenges within EY's financial processes, providing a roadmap to navigate complexities and enhance operational efficiency and employee well-being through RPA adoption.
Keywords:
Robotic Process Automation, RPA in Finance, Ernst & Young, Accounts Payable, Accounts Receivable, General Ledger, Financial Process Optimization, Operational Inefficiencies, Employee Well-being, Skills Development, Risk Management, Agile Teams, Six Sigma, 70:20:10 Model, Lewin’s Change Management, Scrum Framework, Digital Transformation
Cite Article:
"Utilizing RPA in the Financial Sector- An EY Case Study on Cost Reduction and Efficiency", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a367-a409, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509046.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator