Supply Chain Analytics for Cost Reduction and Profit Optimization in Financial Institutions

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Authors: Hema Madhavi Kommula
Cited by: 0

Abstract

In order to identify cost-cutting opportunities and increase financial success throughout the working world, financial institutions continue to make use of the analysis of supply chain data. The integration of preemptive computer analysis and industry wisdom enables institutions to transform procurement operations, supplier management, and work processes through data-driven decision-making. High-tech analytical methodologies applied to supplier valuation, contract optimization, and asset allocation reveal previously hidden ineffectiveness in support of tactical sourcing initiatives. The establishment and elimination of operational redundancies in the back office, compliance projects, and administrative procedures are made easier by method mining technologies and real-time monitoring systems. The Provide Chain Performance Index provides a significant contribution to the assessment of the risk of lending by providing active fitness prosody that complements conventional economic evaluations in the context of loan underwriting and portfolio supervision –investment benefits from the performance of the workforce that reveals supervisory excellence and aggressive placement. Real-time Analytics competencies provide foreseeable accountability through continuous monitoring of work and immediate threat detection. Increasing innovations involving AI, cryptotech, and the Internet of Things platforms promise a significant increase in analytical capacity, allowing for a broad perspective and a high level of sophisticated optimization techniques, which will lead to a competitive advantage in a changing economic support environment.

Fields of Study & Subjects

Big Data and Business Intelligence Impact of AI and Big Data on Business and Society Supply Chain and Inventory Management

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
31 January 2026