This study examines the influence of risk management practices on supplier selection at the Lower Benue River Basin Development Authority (LBRBDA) in Makurdi, highlighting the importance of integrating structured risk management into public procurement processes. A descriptive survey design was employed, with quantitative data collected from 101 procurement stakeholders, yielding 88 valid responses and a response rate of 87.1%. Data were analyzed using SPSS version 26, with multiple regression applied to assess the relationship between risk management practices and supplier selection. Results revealed that risk management practices significantly impact supplier selection decisions (R = 0.872, R² = 0.761, F = 91.417, p < 0.001). Among the predictors, risk identification emerged as the most influential (β = 0.628), followed by risk mitigation (β = 0.451), risk monitoring and control (β = 0.301), and risk assessment (β = 0.187). These findings underscore that a comprehensive risk management framework strengthens the supplier selection process, enhancing transparency, efficiency, and effectiveness in public procurement. The study contributes to procurement literature by providing empirical evidence on the distinct roles of risk identification, assessment, mitigation, and monitoring in shaping supplier selection outcomes within a Nigerian river basin development authority. It further offers practical insights for public procurement agencies seeking to embed risk-aware practices into decision-making processes, thereby improving accountability and long-term performance in supplier management.

The objective of the present study is to implement the alternative stochastic binomial trees for the evaluation and estimation of the main sensitivity measures of convertible bonds, thus filling a gap in scientific literature. The paper proposes the implementation of the Haahtela, Jarrow-Rudd and Tian numerical schemes and explores the characteristics, convergence properties and reliability of these evaluation tools. A comprehensive case-study considering the German market, which is an extremely active market in the issuance and trading of these hybrid instruments, is also illustrated.

Money laundering is one of the most relevant global challenges, with significant repercussions on the economy and international security. Identifying suspicious transactions is a key element in the fight against the phenomenon, but the task is extremely complex due to the constant evolution of the strategies adopted by criminals and the great amount of data to be analyzed daily. This study proposes a hybrid method that integrates Machine Learning models with heuristic rules, with the aim of identifying fraudulent transactions more effectively. The dataset used, SAML, includes millions of bank transactions and presents a strong imbalance between classes (fraudulent vs regular transactions). The entire process was carried out through a self-code platform designed to optimize data management, processing and analysis. The heuristic rules were evaluated using the covering and error metrics and then integrated into the Logic Learning Machine (LLM) task. The effectiveness of the approach was verified by comparing two main configurations: one based exclusively on the use of LLM and the other combining LLM and heuristic rules. The results obtained highlight that the integration of heuristic rules improves the performance of the model, confirming the synergy between Machine Learning and expert knowledge. This study confirms the effectiveness of the hybrid approach and emphasizes the importance of the union between automated analysis and human insight to address the challenges posed by money laundering.

The aim of the study was to develop an enterprise risk management framework for Ethiopian commercial banks. This approach is undertaken to enhance the risk management systems and practices and foster the soundness and stability of the Ethiopian banking system. The study employed a multi-stage mixed methods research design that includes content analysis, survey study and Delphi techniques. The study established an enterprise risk management framework that comprises seventy-one constructs and seven factors. The factors include Vision, mission, core values and strategy, Risk management environment, Risk management function, Risk Management tools and process, Risk appetite and tolerance limit, Alignment and integration, and Enhanced value.

The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models (LLMs), has ushered in a new era of possibilities for the financial sector. This paper explores the integration of these cutting-edge technologies into financial sector risk management, examining both the potential applications and the necessary regulatory frameworks. We provide a comprehensive analysis of how generative AI and LLMs can revolutionize risk assessment, fraud detection, market analysis, and regulatory compliance. The study delves into the technical aspects of these AI models, their implementation challenges, and the implications for existing risk management practices. Furthermore, we propose a novel framework for the responsible adoption of AI in financial risk management, addressing concerns related to model interpretability, data privacy, and algorithmic bias. Our findings suggest that while generative AI and LLMs offer unprecedented opportunities for enhancing risk management capabilities, they also necessitate a recalibration of regulatory approaches to ensure financial stability and consumer protection. This research contributes to the growing body of literature on AI in finance and provides actionable insights for practitioners, policymakers, and researchers in the field.

The EU Corporate Sustainability Reporting Directive (CSRD) presents both challenges and criticisms, but it will also offer more ESG information for financial institutions' investments and exposures. Banks and investors are realizing that addressing the supervisory expectations about non-financial disclosure and risk management represents a significant challenge. This article starts by briefly exploring the regulatory requirements for the thematic areas covered by the European Sustainability Reporting Standards (ESRS). Next, we present a snapshot of Significant Italian banks’ awareness and coverage of ESRS-related topics, by conducting a content analysis focused on their non-financial reporting. We also outline some major governance and risk assessment actions that banks should take with regard to the CSRD. These insights can provide valuable considerations and guidance for financial institutions navigating the complex terrains of ESG risk management and reporting.

From an Internal Audit perspective, the integration of Artificial Intelligence (AI) into credit risk modelling through Machine Learning (ML) algorithms presents significant challenges due to the complexity and multidimensional nature of these models. While AI enhances predictive performance and accuracy, its inherent lack of transparency and explainability increases the risk of control deficiencies, potentially leading to financial losses, misrepresentation of information, unfair discrimination against debtors, and non compliance with EU regulations. This paper introduces a comprehensive audit framework designed to establish robust internal controls over AI-driven credit risk models. Aligned with the Model Risk Management (MRM) lifecycle, we propose a structured set of audit tests and controls, organized by thematic area, to assess key aspects such as model design and performance, governance, reliability, and regulatory compliance. Additionally, we provide practical examples in emerging areas to illustrate their application. These audit procedures aim to identify critical vulnerabilities while ensuring adherence to regulatory standards, including EBA/REP/2023/28 and the evolving requirements of the EU AI Act.

This paper addresses the challenges associated with pricing exotic options, specifically path-dependent ones, with a focus on the limitations of standard Monte Carlo simulations and the advantages provided by Conditional Monte Carlo methods, introduced by Babsiri and Noel in 1998. Path dependent options, such as first and second-generation barrier and lookback options, require continuous monitoring of asset prices throughout their lifetime, making accurate pricing computationally demanding and prone to errors when using traditional Monte Carlo methods. This work begins by presenting different exotic options, offering a detailed comparison between the exact pricing formulas and the results obtained from Crude Monte Carlo simulations. The Conditional Monte Carlo method is then applied to address the bias introduced by discrete monitoring intervals in the simulations, a critical issue in path-dependent options. A market case based on the valuation of a Bonus Cap certificate has also been shown.

The research aimed to identify and evaluate the risks associated with IT projects, particularly focusing on their impacts. Despite numerous efforts, a significant number of software projects still fail to achieve success; however, these risks can be effectively managed. This study outlines methodologies for examining how different risks influence software projects, using statistical analyses and models to uncover causal relationships. A survey was also conducted to assess critical risk factors, highlighting three key factors that have the greatest influence. The findings suggest that addressing these factors can improve decision-making, thereby increasing the likelihood of project success.