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Purpose: Business intelligence and decision support systems are now recognized as critical enterprise infrastructure. Data is increasingly being used by business enterprises to react to key operational and strategic operations of their customers, markets, and stakeholders. Theoretical framework: Business intelligence has advanced as the volume of data generated by smart technologies and the Internet has increased exponentially. Design/Methodology/Approach: When using AI in the business world, privacy concerns arise when sensitive data is transmitted to a third-party vendor who has no connection to the company. Financial decision support data is managed by these AI service providers. Even while it may seem like a drawback, artificial intelligence technology actually has several advantages. Findings: The results of this research show that a network can improve the financial management of a corporation. Using many applications of AI technology helps bring down the overall cost of operations. When it comes to financial services, both the number of IT workers needed and the number of necessary pieces of hardware (servers) can be reduced, resulting in a marginal drop in capital expenditures. Research, Practical & Social implications: When it comes to financial services, both the number of IT workers needed and the number of necessary pieces of hardware (servers) can be reduced, resulting in a marginal drop in capital expenditures. It is now simpler and quicker to get your hands on relevant financial information, which should lead to greater efficiency. Originality/value: With our proposed approach, the accuracy of financial decision support in company increases to 99.84% from 88.94%, while the implementation time and cost are reduced.
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Stored only when both papers are in the corpus — 20 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.