Volume 17 (2024)

Each volume of Journal of Risk Management in Financial Institutions consists of four, quarterly, 100-page issues published both in print and online. The papers included in Volume 17 are listed below:

Volume 17 Number 1

Special issue: The Value of New Data and Technology to Risk Management Practitioners
Guest editors: Thomas Wilson, CEO, President and Country Manager, Allianz Ayudhya and Christian Pedersen, Managing Director, Strategy & Consulting, Accenture London

  • Editorial
    Thomas Wilson and Christian Pedersen, Guest-editors
  • Special Issue Papers
  • Opinion Papers
    On data and models: Is more always better?
    Thomas C. Wilson, Guest-editor

    With regards to data and model sophistication, the new mantra for financial services and FinTech seems to be ‘the more the better’, supported by attractive business cases in risk underwriting, fraud detection, customer lifetime value management, conditional investment risk/return optimisation and improving customer journeys, to name a few. However, more data and more sophisticated models are not always a universal panacea and may lead to bad business outcomes if not managed appropriately in the context of the desired business outcome. This paper summarises the evolving business cases for increasing data and models in the risk management domain and their associated risks. Making the associated risks transparent naturally leads to the conclusion that a timeless risk mitigation approach — common sense — is critically necessary to complement the more structured model risk management (MRM) framework that is evolving.
    Keywords: data; models; risk management; insurance; analytics; artificial intelligence

  • Trusted and open corporate data: Why adoption of the LEI/vLEI is key to enhancing risk management practices in the face of rapid digital transformation
    Stephan Wolf, CEO, Global Legal Entity Identifier Foundation

    As financial institutions increase their participation in the global digital economy, huge opportunities emerge: more efficient and accurate ways to fight fraud and crime through automated processes and real-time industry collaboration and action; the disinhibition of capital flows needed to fuel economic development; the growth of broader and trusted cross-border customer bases, partner networks and supply chains; and, as will be explored more fully through the presentation of a use case, the capability to advance environment stewardship. These are just some of many possibilities, yet new threats materialise as companies digitise and digitalise. Many are connected to the challenge of identity management and verifying the authenticity and integrity of associated entity reference data in digital environments. How do organisations verify the legitimacy of who they are interacting with online? Can they trust the origin and integrity of digital data associated with customers, partners and other stakeholders, and that the data they do have is current and accurate? Here, the Legal Entity Identifier (LEI) together with its digitally verifiable counterpart, the vLEI, can play a crucial enabling role. This paper examines the opportunities and risks that financial institutions face as they embark on digital transformation programmes. It explores the importance of high quality, verified and open legal entity data to enhanced risk management practices. An outline is given of how a universal ISO entity identification standard, the LEI and its digital counterpart, the vLEI, can be used to: verify the identity of companies, their corporate organisational structures and their authorised executives; and to connect an organisation to verified business data, other identifiers, company reports and multiple data sources. A risk management use case will be presented –— the use of the LEI as an environmental, social and governance data connector — to show how the LEI and vLEI can be harnessed by financial institutions to inform better business decision making and create enhanced, even automated, risk management practices within increasingly digital corporate ecosystems.
    Keywords: digital identity; identity management; open data; digital transformation; ESG reporting

  • Practice Papers
    Leveraging financial personality for inclusive credit scoring amidst global uncertainty
    Diederick van Thiel, Founder/CEO, AdviceRobo, and John Goedee, Professor by special appointment at the Department of Organization Studies, and Roger Leenders, Professor of Intra-Organizational Networks,Tilburg University

    The Ukraine war, high inflation and rising interest rates are jeopardising people's ability to afford essential items such as food and energy, causing a widespread sense of vulnerability worldwide. Consequently, access to finance has become increasingly challenging for vulnerable consumer groups, including young adults without established credit histories, senior citizens with fixed incomes, start-up entrepreneurs, sole traders, single parents, immigrants in Western markets. To address this issue, this study explores the potential use of individuals' financial personality for inclusive credit scoring in these uncertain environments. Examining a sample of low-income individuals in the USA and the Netherlands, our psychometric scoring models (PSMs) demonstrate that late payments can be attributed to factors such as financial capability, materialistic tendencies, impulsive buying behaviour, social desirability and attitudes towards debt. These findings provide evidence that PSMs offer a viable solution to advance financial inclusion for vulnerable customer segments amidst global uncertainty.
    Keywords: access to finance; inclusive finance; behavioural finance; psychometric credit scoring; financial crisis; responsible lending

  • Lost in noise? Some thoughts on the use of machine learning in financial market risk measurement
    Peter Quell, Head of Portfolio AnalyticsDZ BANK AG

    Machine learning has permeated almost all areas in which inferences are drawn from financial data. Nevertheless, in financial market risk measurement most machine learning techniques struggle with some inherent difficulties: Financial time series are very noisy, not stationary and mostly considerably short. This paper contains an easy to implement sequential learning algorithm that overcomes some of these disadvantages. It is based on a Kalman filtering mechanism for quite general stochastic processes and provides a first step in the direction of separating parameter dynamics from the ubiquitous noise component. The core idea here is to use some stylised facts inherent to financial markets time series such as time varying measures of volatility. The new approach is tested using real market data in two different settings. First, a hypothetical portfolio containing credit spread and equity risk is analysed over a time frame containing the outbreak of the global pandemic in 2020 and the beginning of the Russian attack on Ukraine in 2022. Another analysis is focused on US$/EUR exchange rate during a time span containing the global financial crisis of 2008 and the subsequent European sovereign crisis. In all test calculations the proposed sequential learning algorithm performs better than the historical simulation approach used by many firms in the banking industry to meet regulatory capital requirements. Due to its simplicity this method has a high degree of explainability and interpretability which will decrease the inherent model risk. The paper concludes with a discussion of model risk for machine learning in financial institutions. Compared to classical model risk frameworks, the emphasis must be put on the more prominent role of data. The simple approach described in this paper shows that machine learning in financial market risk does not have to get lost in noise.
    Keywords: market risk; machine learning; Basel regulation; Kalman filter; adaptive methods; model risk

  • The wicked problem of quantifying and managing non-financial risks: The role of digital technology in providing solutions
    Tom Butler, Professor, Business Information Systems, University College Cork and Robert Brooks, European Managing DirectorAccenture, Cyber Risk and Regulation

    The management of operational risk in financial institutions has all the characteristics of a ‘wicked problem’. Certainly, the repeated efforts of the Bank of International Settlements, (BIS) Basel Committee on Banking Supervision (BCBS) to have banks control and mitigate their operational risks speak to the tractability of extant approaches to addressing them effectively. The original ‘Principles for the Sound Management of Operational Risk’1 and its recent revisions,2 the BCBS ‘Principles for Effective Risk Data Aggregation and Risk Reporting’3 and the ‘Principles for Operational Resilience’,4 collectively offer a sound foundation for addressing this enduring problem. Why then are solutions so elusive for banks to implement? This paper first outlines the institutional and social web of conditions and factors that contribute to the existence of this ‘wicked problem’. It then identifies how AI-based digital technologies can once and for all effectively address the problem of operational risk in large banks. Nevertheless, as powerful as today's digital technologies are, they require an organising vision, particularly if they are to contribute to the management of operational risk. This paper informs such a vision and identifies a comprehensive artificial intelligence-based digital architecture to realise it.
    Keywords: operational risk; wicked problem; digital technology; artificial intelligence; enterprise data fabric

  • The potential impacts of the digital revolution on the operational risk profiles of banks
    Michael Grimwade, Managing Director, Operational Risk,​​​​​​​ ICBC Standard Bank Plc

    Society is undergoing a digital revolution. This is altering the business profiles of banks in terms of their systems, processes, controls and usage of third parties; their competitive landscape, ie competition from both digitising incumbents and new BigTech and FinTech entrants; and the behaviours of stakeholders, ranging from customers to cyber-criminals. This digital revolution is amplifying some of their existing risks, while also creating new risks, eg the potential for artificial intelligence (AI) tools to change behaviours over time (AI model drift). Some of these changes in operational risk profile will be transient, as they are associated with digital transformation, while others will be both ongoing and characterised by a high degree of dynamism. In this digitised endstate higher frequency/lower value human errors, may be replaced by lower frequency/higher impact systemic losses, arising from both catastrophic and silent failures. The influence of the digital revolution spans almost all of the Basel operational risk event categories, and may also lead to the enhancement of some controls (eg surveillance), while others may be undermined (eg by voice-spoofing). There is no silver bullet to mitigate these risks; instead, a portfolio of existing control frameworks need to be enhanced, including the following: change management; model risk management; third party vendor management; business continuity management, disaster recovery and operational resilience; and cybersecurity, with new controls required to address the new risks associated with AI. This will be a key factor in the operational risk losses of banks over the next decade.
    Keywords: digital revolution; digitisation; artificial intelligence; AI; BigTech; FinTech; operational risk

  • Risk Management Papers
    A generalised latent Poisson factor modelling approach for default correlations in credit portfolios
    Mohamed Saidane, Associate Professor, College of Business and Economics,​​​​​​​ Qassim University

    Default risk is one of the major concerns for lending institutions and banking regulators. This paper focuses on the analysis of default data, using a new approach based on generalised latent Poisson factor models. In this case, the correlation structure of the default events is driven by a small number of common latent factors. Conditional to these factors, the defaults become independent and each default sequence is fitted to a generalised linear model with Poisson response and log-link function. This model provides a flexible framework for the computation of the value-at-risk and the expected shortfall of a credit portfolio. The practical implementation of the proposed local Fisher scoring estimation algorithm is illustrated by a Monte Carlo simulation study. Then, a real scenario, with default data taken from a large database provided by Standard & Poor's, is used to analyse the empirical behaviours of the different risk measures. The achieved results show promising performance.
    Keywords: default correlation; factor analysis; generalised linear models; expectation-maximisation algorithm; credit value-at-risk; expected shortfall

  • The mediating role of firm risk: The case of the insurance sector in Saudi Arabia
    Shanar Shafi Alsuyayfi, PhD student, Roslan Ja’afar, Senior Lecturer, and Rasidah Mohd Said, Assistant Professor, UKM Graduate School of BusinessUniversiti Kebangsaan Malaysia

    This study aims to examine the mediating effect of firm risk on the relationships between board structure and firm performance. The multivariate panel data regression technique is employed to analyse the mediating impact of firm risk on 27 listed insurance companies on the Saudi Stock Exchange (Tadawul) from 2016 to 2021. The findings of this study indicate that firm risk partially mediates the relationship between audit independence and Tobin's Q. In contrast to the existing literature, the study reveals that boards composed of independent members may lack effectiveness in their monitoring role, leading to higher risk-taking behaviour. This paper contributes to the literature on corporate governance and firm performance by examining the association through the lens of firm risk.
    Keywords: firm risk; board structure; mediation; insurance sector; Saudi Arabia

  • Book Reviews
    Handbook of business and climate change by Anant K. Sundaram and Robert G. Hansen
    Reviewed by Krzysztof Jajuga, Professor, Department of Financial Investments and Risk Management, Wroclaw University of Economics and Business
  • Non-financial risk management: Emerging stronger after Covid-19 by Thomas Kaiser
    Reviewed by Krzysztof Jajuga, Professor, Department of Financial Investments and Risk Management, Wroclaw University of Economics and Business