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“The paper aims to educate both students and aspiring financial crime compliance professionals on the application of the Risk-based Approach in combatting money laundering …”
“… the present study developed the AI Categorization and Classification (AI-CC) Method as a central artifact to provide guidance on the use of AI within the profession. The target users of the AI-CC Method are regulators, standard setters, the strategic management of the Big Four, and individual auditors.”
The challenge for cyber insurers lies in the scarcity of data, hindering risk assessment and product development. Organizations fear sharing information due to the risk of further attacks. Balancing transparency with discretion is crucial. With better data sharing, insurers can offer tailored products, assess risks accurately, and enhance corporate compliance.
"The essay presents a vision of the AI-enhanced actuary, who leverages AI to build more accurate and efficient models, incorporates new data sources, and automates routine tasks while adhering to professional and ethical standards. We also discuss the challenges and speed-bumps along the way, including explainability, bias and discrimination risks, regulatory hurdles, and the need for actuaries to acquire AI knowledge and skills."
This workbook addresses the challenge of defining AI fairness, proposing a context-based and society-centered approach. It emphasizes equality and non-discrimination as core principles and identifies various types of fairness concerns across the AI project lifecycle. It advocates for bias identification, mitigation, and management through self-assessment, risk management, and fairness criteria documentation.
The objective of this paper is to compare the most common available Risk quantification models: Fault Tree Analysis, Failure Mode Effective Analysis, and FAIR (Factor Analysis of Information Risk) Model.
“The analysis reveals that boards are ineffective in cybersecurity risk oversight due to a lack of IT knowledge, and cybersecurity expertise is largely absent at the board level.”
Operational risk modeling requires flexible distributions for non-negative values, particularly those exhibiting heavy-tail behavior. Composite or spliced models, like composite Tukey-type distributions, are gaining attention for their ability to handle extreme and ordinary observations effectively. This paper explores the flexibility of such distributions, offering empirical validation with operational risk data.
The Basel II advanced measurement approach often yields counterintuitive operational risk capital due to extreme loss events. To address this, the semi-nonparametric (SNP) model by Chen and Randall (1997) can enrich parametric model distributions. SNP shows improvement over parametric models, providing more intuitive capital estimates consistent with extreme value theory.
The Probability of Insurance Recovery (PoIR) is a key parameter, yet its estimation lacks prior modeling efforts. This paper aims to introduce a methodology for assessing PoIR within firm risk frameworks.