IMPACT OF ARTIFICIAL INTELLIGENCE ON FINANCIAL DECISION MAKING

Abstract

AI is changing finance fast - making choices quicker plus more accurate while lowering risks, but it also adds hard ethical puzzles along with technical roadblocks. Even so, pinning down responsibility gets messy when systems start acting alone. This study tackles the chaos using four clear aims instead of fluffy claims. One aim explores how rules can control Ai behavior, especially tied to fairness or environmental effects. A different one studies mixing human insight with machine tools so employees aren't pushed aside. It digs into actual hurdles messing up reliable AI where it really matters. Every part links clearly - no filler, no noise.
The method took a close look at earlier research, often using clear steps like those from PRISMA 2020, while also digging into raw data. Data from 76 individuals was reviewed via regression analysis - this showed links between AI knowledge and work status. ANOVA came into play when exploring opinions on leadership shifts and team dynamics. For tech-related challenges, basic stats gave insight into people’s perceptions.
Some findings suggested that understanding AI had almost no effect on jobs - only 0.1% of changes linked to it. Regarding handling AI, opinions split sharply; a good number thought well-built AI cuts ESG risk (p=.000), whereas others felt monitoring helps firms follow ESG rules (p=.027). A common idea emerged about tech problems, particularly how hard technical barriers slow AI adoption (mean 1.73). As for employee adaptation, responses stayed alike across groups, somewhat leaning toward believing AI lifts performance.
The key thing here is how it focuses on handling fresh ESG risks with AI - while looking into better ways humans and machines can work together. For real change, banks and lenders need strong ethics rules plus practical oversight - not just paper plans but actual systems - to deal with issues like skewed algorithms while remaining transparent and responsible. Doing AI well means tackling tech hurdles: improving data quality, refining monitoring tools, also finding smart paths to blend AI into older software setups. Each step counts if you want trust from investors, regulators, customers - all needing proof that AI in finance works reliably and sticks around.