Credit risk management (CRM) is a fundamental pillar of financial systems, attracting attention from practitioners and researchers. Traditional credit assessment methods have limitations in today's complex, fast-changing financial environment. Advances in machine learning (ML) and behavioral data analytics offer new possibilities for improving CRM through better models and performance. This paper provides a systematic review of ML applications in credit default prediction and early warning systems, critically synthesizing recent literature. It discusses three major dimensions: the evolution of ensemble learning algorithms, the use and issues of behavioral data in feature engineering, and advances in model explainability (XAI). The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles." It makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements. The review also addresses class imbalance, data privacy, and ethical issues, with mitigation strategies. The review offers theoretical guidance and practical implications for financial institutions to improve risk control and build reliable early warning systems, outlining directions for future research.

Entertainment industry marketing has emerged as one of the most dynamic research domains in the digital era. Based on a systematic review of 14 frontier studies published between 2023 and 2025, this article constructs a three-dimensional analytical framework that integrates emotional mechanisms, content production models, and brand ecosystems. The findings reveal that: (1) emotional mechanisms serve as the core explanatory variable spanning three research pathways—fan economy, social media marketing, and brand crossover collaboration; (2) entertainment products, characterized by six fundamental features (hedonic, narrative, cultural, creative, innovative, and digital), demand distinctive marketing paradigms distinct from conventional goods; (3) content production is undergoing a structural shift from individual creativity toward systematic, industrialized, full-process control models; and (4) brand crossover collaboration has evolved from simple IP licensing into a complex mechanism encompassing cultural symbol translation, consumer identity construction, and ecosystem coordination. At the theoretical level, this article proposes an integrative framework built upon Behrens et al.'s six-feature model, synthesizing emotional capitalization, content industrialization, and brand ecologization. At the practical level, it reveals the timing effects of emotional appeals, the decisive role of content quality, and the boundary conditions of the fan economy efficacy. Research trends indicate that generative AI, immersive technologies, and cross-platform integration will become key growth areas, while the negative effects of fan economy, cross-platform comparative research, and longitudinal tracking constitute critical research gaps.

Valuation Adjustment Mechanisms (VAMs) come with many questions as to their enforceability, risk allocation and means of dispute-resolution under the current regulatory frameworks for private equity investments. The study dissects how the risk is allocated between the investors and the portfolio companies, key contractual features and maps the practical dispute-resolution routes by analysing selected case studies and a relevant regulatory guidance. The paper also recommends clause designs for the actionable sections of the paper and due diligence checklist for equity investments for better risk management and compliance. The results shed light on the design decisions of VAMs in the context of regulatory development, point out some of the typical challenges in terms of enforceability, and provide practical recommendations to practitioners, investors and regulators. The contribution is in the idea and the execution of connecting theory and practice, turning regulatory understanding into drafting guidance and due-diligence processes, and thus improving clarity, predictability and resilience in PE transactions.