Algorithms in Future Capital Markets

Koshiyama, Adriano; Firoozye, Nick; Treleaven, Philip

doi:10.2139/ssrn.3527511

Cited by 11 publications

(11 citation statements)

References 46 publications

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“…It is when these programs trade with each other en masse that unintended market shocks may arise such as the exacerbation of flash crashes or speculative bubbles [33]. Koshiyama et al's Algorithms in Future Capital Markets further supports the idea of unique risks emerging from excessive algorithmic complexity in trading execution by identifying intensified volatility, flash crashes, rogue algorithms, and algorithm uniformity threats through examining how novel LSTMs, GANs, Transfer and Meta Learning procedures are applied within the use-case [35].…”

Section: Evaluation and Discussion Of Financial Sector Use-casesmentioning

confidence: 99%

Survey of EU ethical guidelines for commercial AI: case studies in financial services

2021

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A macro perspective examining the general nature of AI implementations and how enforcement should be structured under the new frontier of AI technologies is severely needed. The paper critically analyzes real and potential ethical impacts of AI-enabled systems as well as the standard process regulators, researchers, and firms use to assess the risks of these technologies. Three real-world cases are detailed as each type of AI implementation highlights varying degrees of ethical impacts. The paper surveys whether the current September 2020 European Parliament standard titled "European framework on ethical aspects of artificial intelligence, robotics and related technologies" will handle all cases effectively. Future regulators enforcing principles must be able to discern how each AI implementation should be handled as it is evident that there is a spectrum of ethical applicability on the use-case level. The cases examined are in using AI to automate the mortgage application process, find matching attributes for trade reconciliation tasks, and optimizing order entries within trading algorithms. The focus of the cases provided is in the financial industry as firms in this sector, on average, spend the highest percentage of their total revenue on information technology projects. Though the EU framework is thorough in recommending an ex-ante approach, providing risk classifications, and detailing effective measures to mitigate harm from AI-enabled systems, it falls short in identifying certain macroeconomic harms caused by AI-enabled systems and tracking the applications responsible within complex, interlinked systems.

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Section: Evaluation and Discussion Of Financial Sector Use-casesmentioning

confidence: 99%

Survey of EU ethical guidelines for commercial AI: case studies in financial services

2021

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“…At its most basic, an algorithm is simply a set of instructions designed to perform a specific task. Our main referent in this article is to data science algorithms, which include those that fall under AI paradigms such as machine learning or knowledge-based systems, used in human resources applications such as psychometric testing and recruitment ( Koshiyama et al 2020 ).…”

Section: Notesmentioning

confidence: 99%

Systematizing Audit in Algorithmic Recruitment

et al. 2021

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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.

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“…An accurate role model and illustration of future data-driven insurance is the introduction of algorithmic trading in the Capital Markets (Treleaven, et al, 2013) over the past 18 years. Notable is firstly the collection and analysis of increasingly huge volumes of real-time and historic data (e.g., financial, economic, social, alternative), and secondly the increasing use of AI algorithms that can switch dynamically to respond to changes in the market microstructure (Koshiyama et al, 2020). Insurance companies will seek increasingly to collect and control their data; and also automate to interact directly with their clients.…”

Section: Challenges For Insurance Industry Under Ai Revolutionmentioning

confidence: 99%

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confidence: 99%

Algorithms in future insurance markets

Śmietanka¹,

Koshiyama²,

Treleaven³

2021

IJDSBDA

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This paper reviews the impact of data science and artificial intelligence (AI) on future 'datadriven' insurance markets. The impact of insurance automation (driven by so-called Black Swan 1 events such as Covid-19) mirrors the impact of algorithmic trading that changed radically the capital markets (Koshiyama et al., 2020). The data science technologies driving change include: Big data, AI analytics, Internet of Things, and Blockchain technologies. These technologies are important since they underpin the automation of the insurance markets and risk analysis, and provide the context for the algorithms, such as AI machine learning and computational statistics, which provide powerful analytics capabilities. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decision-making. The current main disrupting forms of learning include deep learning, adversarial learning, federated learning, transfer and meta learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they are now more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., long short-term memory, generative adversarial networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future insurance markets, so it is important to understand their computational strengths and weaknesses. The contribution of this paper is to review the data science technologies and specifically AI algorithms, their computational strengths and weaknesses, and discuss their future impact on the insurance markets.

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Algorithms in Future Capital Markets

Cited by 11 publications

References 46 publications

Survey of EU ethical guidelines for commercial AI: case studies in financial services

Survey of EU ethical guidelines for commercial AI: case studies in financial services

Systematizing Audit in Algorithmic Recruitment

Algorithms in future insurance markets

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