Shamakhi, Mashael EssaZhao, BinruZhang, HanxiongKhoo, Shee YeeGepp, Adrian2025-11-292025https://hdl.handle.net/20.500.14154/77219This research provides an integrated examination of four critical areas in modern finance: the impact of Environmental, Social, and Governance (ESG) performance on syndicated loan terms, the influence of behavioural factors on investment decisions, the effectiveness of Credit Rating Agencies (CRAs) in assessing financial risk, and analytical models used to detect financial statement fraud. The ESG analysis shows no statistically significant effect on loan pricing, maturity, or covenant strictness, despite minor economic tendencies. The behavioural finance review highlights how social interactions, media sentiment, and AI-based tools amplify investor biases and market volatility. The evaluation of CRAs identifies persistent challenges related to rating accuracy, conflicts of interest, and delayed downgrades, even after post-crisis regulatory reforms. Finally, the fraud analytics component assesses models such as the Beneish M-Score, the Dechow F-Score, and machine learning methods like RUSBoost, demonstrating the limitations of traditional scoring models under extreme class imbalance. Collectively, the study underscores the interconnected roles of human behaviour, information quality, and quantitative analytics in shaping financial market outcomes and emphasizes the need for transparent methodologies, robust regulatory frameworks, and advanced analytical tools to enhance financial stability.71enESG performanceBehavioural financeCredit Rating AgenciesFinancial fraudInvestor biasesThesis