The effective implementation of the Risk Data Aggregation and Risk Reporting (RDARR) principles introduced in 2013 by BCBS 239 continues to pose significant challenges for banking institutions, particularly in ensuring consistent interpretation and application across complex reporting domains. More than a decade later, the European Central Bank (ECB) has reinforced and broadened the scope of these principles through increasingly prescriptive supervisory expectations, following persistent implementation gaps. This paper aims to interpret the RDARR principles and, through a practical example, outline the changes and requirements necessary to achieve full compliance for credit risk models. In doing so, this study first provides a mapping matrix to determine which credit risk models are RDARR-relevant, as this represents a necessary prerequisite for a consistent and risk-based application of the framework. Then, it focuses on Pillar I credit risk models, which are among the most advanced in terms of data quality and data architecture, owing to the regulatory frameworks established by the ECB and EBA. Building on this starting point, the paper analyses the main gaps in the application of RDARR principles and proposes a remediation framework for Pillar I models that attempts to mitigate the compliance burden often associated with RDARR. In particular, it leverages the introduction of an end-to-end data lineage concept as a key enabler to strengthen data governance and support the systematic integration of data quality controls aligned with ECB expectations. The proposed framework, developed for Pillar I models, serves as a benchmark for the broader set of credit risk models identified through the matrix, guiding their progressive alignment with RDARR principle

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.

On December 2017, the Basel Committee published the “Basel III: Finalising post-crisis reforms” (also known as Basel IV) that introduces the Standardised Measurement Approach (SMA) to define the Pillar I operational risk capital requirement that is foreseen to entry into force on the 1st of January 2025, replacing all the existing approaches. This approach not only introduces a new method to be used to calculate the operational risk capital requirement but details several updates that have to be applied to the main components of the framework such as Governance, Loss Data Collection and Risk Self-Assessment. With the entry into force of the SMA, banks have the chance to fully re-think their operational risk Management Framework (ORMF) integrating the different components and making it more efficient and effective in terms of data governance, process management and reporting. This paper describes the SMA methodology to be implemented to calculate the Pillar I operational risk capital requirement and provides an overview of the expected impact on the different components of the ORMF of the banks.

We analyze what are the main pricing components for performing loans. By exploiting a survey conducted by the authors in AIFIRM (2021), we provide empirical evidence about whether and to what extent various pricing criteria are related to interest income within the internal model framework. Our main findings are that banks’ interest income is positively related to the adoption of advanced internal risk-based models, the calculation of the break-even rate, and the implementation of the risk-adjusted profitability measures in the pricing, while it is negatively linked to higher market competition, a decentralized pricing function (allowing more customeroriented loans prices). The results make urgent to monitor and develop improve current risk models to support both central offices and the sales network in the process of formulating loan prices and monitoring the value consequently created.

anonymously to a double level of peer review. The first level is a review of eligibility, implemented on the paper by the members of the Editorial Board, who assess the adequacy of the paper to the typical topics of the magazine. The second level is a review of suitability for publication, implemented on the paper by two referees, selected within the Editorial Board, the Scientific Committee or externally among academics, scholars, experts on the subject who assess the content and the form.

In light of the finalization of the new regulatory framework for market with the adoption of the FRTB at EU level through the publication of CRR III, financial institutions are consolidating the implementations aimed to comply with the new regulatory requirements. The main purpose of this article is to analyze how banks are preparing for the go-live of IMA FRTB reporting – expected to be in January 2024 – focusing on the challenges that they are facing especially in terms of model transformations. In particular, an in-depth analysis will be carried out on the main methodological issues of the new regulatory context technicalities,in order to provide guidelines and market best practices on the Internal Model Approach (IMA) topics shared between Front Office, Risk Management as well as Control Structures.

Following the publication of the regulatory framework for the Fundamental Review of the Trading Book (FRTB) by both the Basel Committee (BCBS) and the EU Regulator, the Financial Institutions have started the mandatory actions to comply with the new regulatory requirements. This article aims to provide an overview of the key challenges that banks have had to face in recent years, with a particular focus on the most significant methodological key points and the main impacts on business from the technicalities of the new regulatory framework, in order to provide guidelines and best practices on Standardized Approach (SA) topics shared between Risk Management and Front Office

Our study highlights a literature map on Fintech and the risks associated with this technological innovation in the financial sector. Considering all the studies published from 2014 to 2021 in "Scopus", we resort to econometric techniques to create our map. Our results show the recent attention of academics and researchers, mainly belonging to the technological and IT areas, towards Fintech. In particular, the studies focus on the issue of emerging technologies applied to investment and credit processes linked to the assessment of customer insolvency risk. For this reason, the existing analyzes adopt a mainly technical approach with very limited attention to strategic, organizational and managerial aspects typical of financial intermediation. Future studies could investigate the issue of Fintech behavior and relations with incumbent banks, as well as the risks that the applications of emerging digital technologies have on the sound and prudent management of these operators. In addition, further analysis can capture the risks of Fintech for clients, taking into account financial education. These are important aspects for the growth of Fintechs themselves, for the sustainability of the incumbent banks, with which they increasingly collaborate, and obviously for the banking supervisory authorities, attentive to the stability, efficiency and competitiveness of the financial sector as a whole.

After acknowledging that one of the key factors that contributed to the Great Financial Crisis were the failures by banks in their Corporate Governance, standard-setting bodies have reinforced banks governance standards in order to reduce the shortcomings observed during the crisis. In March 2020, the Bank for International Settlement issued the paper “Bank Boards – a review of post-crisis Regulatory approaches” that, taking stock of specific aspects of the post-crisis Regulatory approaches used in 19 jurisdictions to strengthen Board oversight at banks, reviews the “Fitness and Propriety” assessment that these jurisdictions use to ensure that bank Board members are suitably qualified. Investigating the empirical evidence provided by scientific literature on the relationship among Corporate Governance and the profitability of the banks during the Great Financial Crisis, the results of this paper support some of the choices made by the Regulators to enhance the banks’ Corporate Governance in order to mitigate similar risks that banks could face subsequently.

In the last years Machine Learning (and the Artificial Intelligence), is experiencing a new rush thanks to the growth of volume and kind of data, the presence of tools / software with higher computational power and cheaper data storage size (e.g. cloud). In Credit Risk Management, the PD (Probability of Default) estimation has attracted lots of research interests in the past literature and recent studies have shown that advanced Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods tied to simplified Machine Learning techniques. The study empirically investigates the results of applying different advanced machine learning techniques in estimation and calibration of Probability of Default. The study has been done on big data sample with more than 800,000 Retail customers of a panel European Banks under ECB Supervision, with 10 years of historical information (2006 - 2016) and 300 variables to be analyzed for each customer. The study shows that neural network produces a higher population riskiness ranking accuracy, with 71% of Accuracy Ratio. However, the authors’ idea is that classification tree is more interpretable from an economic and credit point of view. In terms of model calibration, cluster analysis produces rating classes more stable and with a predicted risk probability aligned with the observed default rate.