Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Farias, Aquiles; Deodoro, Jose; Boukherouaa, El Bachir; AlAjmi, Khaled; Iskender, Ebru; Ravikumar, Rangachary; Shabsigh, Ghiath

doi:10.5089/9781589063952.087

Cited by 42 publications

(35 citation statements)

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“…Attacks using data poisoning need superior access to training and model input. After it is done correctly, infected models might go undetected until the malicious activity doesn't interfere with routine diagnostic tests [22]. Authorities in the financial sector are worried about AI/ML cybersecurity.…”

Section: Ai and Data Breachmentioning

confidence: 99%

Navigating the Data Security Landscape: Challenges and Solutions in Financial Markets amid Digitalization and Artificial Intelligence

Gupta,

Shah

2023

IJMRA

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Financial markets have the risk of data mishandling or leakage of information. Regarding consumer, privacy breach and information mismanagement affects the overall trust in the system. With faster digitalization and online usage of information, a large section of economic markets require that consumers reveal their individual information constituting a part of regulatory requirements and privacy agreements. Financial systems gather information such as identity, payment passwords, shopping choices, and other similar details that can be shared online. Financial data is prone to misuse and leakage through big data pricing discrimination and over-marketing. Furthermore, the adoption of artificial intelligence/ Machine learning (AI/ML) creates new, distinct cyber risks and expands possibilities for cyber-attacks. AI/ML systems are susceptible to new threats along with the usual cyber threats posed by human error or software malfunctions. Encroachment of personal details and disruption of law by certain organizations make it difficult for individuals to assess the challenges or problems encountered within financial markets. Therefore, the lack of systematic arrangement of the financial system results in financial losses and psychological disruption presenting as mistrust of the government’s accountability, impairment of the social balance, and erratic behavior. The present study aims to assess: 1) The scope of data security in financial markets; 2) the Challenges encountered by financial markets in maintaining data security, 3) the Role of Artificial intelligence in data breaches and Current methods (Technical and non-technical) utilized for data security.

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Section: Ai and Data Breachmentioning

confidence: 99%

Navigating the Data Security Landscape: Challenges and Solutions in Financial Markets amid Digitalization and Artificial Intelligence

Gupta,

Shah

2023

IJMRA

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“…At this step, min-max normalization is used to execute data type conversions, scaling, and normalization.This process ensures data consistency and uniformity. To enact these transformations, we closely follow the formula outlined (1).…”

Section: Rd Step: Transformation Of Datamentioning

confidence: 99%

“…The rapid advancements in artificial intelligence (AI) have revolutionized various domains, including finance and investment [1]. In recent years, the financial sector has undergone a fascinating and remarkable evolution, influenced by these transformative AI developments with its potential to significantly influence the entire financial ecosystem, the innovation of fintech has garnered substantial attention and discussions in recent years [2].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of technical indicators for forecasting the moroccan stock index using deep learning

Razouk,

El Mehdi Falloul,

Harkati

et al. 2023

IJEECS

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<span>Navigating the complex terrain of financial markets requires accurate forecasting tools, underscoring the need for effective forecasting methods to assist investors and policymakers alike. This paper explores deep learning techniques for forecasting the Moroccan all shares index (MASI), a prominent indicator of the Moroccan stock market. The study aims to evaluate the performance of technical indicators in enhancing the accuracy of MASI predictions. A comprehensive dataset of daily closing prices of the MASI index is collected and 26 technical indicators are computed from the historical price data. Deep learning models based on artificial neural networks (ANNs) are trained and optimized using the dataset. The performance of the models is evaluated using standard metrics such as mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Additionally, feature selection techniques are employed to identify the subset of technical indicators that contribute most significantly to the prediction accuracy. The findings provide insights into the effectiveness of deep learning models and the impact of technical indicators on MASI prediction accuracy. This research has important implications for investors, financial analysts, and policymakers, enhancing investment strategies and risk management approaches.</span>

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“…In [10], the authors explore a wide range of machine learning applications for enhancing efficiency in the financial sector. These technologies have the potential to automate processes, improve risk assessment and management, and enable more accurate and efficient decision-making.…”

Section: Introductionmentioning

confidence: 99%

SeqNAS: Neural Architecture Search for Event Sequence Classification

Udovichenko,

Shvetsov,

Divitsky

et al. 2024

IEEE Access

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Neural Architecture Search (NAS) methods are widely used in various industries to obtain highquality, task-specific solutions with minimal human intervention. Event Sequences (EvS) find widespread use in various industrial applications, including churn prediction, customer segmentation, fraud detection, and fault diagnosis, among others. Such data consist of categorical and real-valued components with irregular timestamps. Despite the usefulness of NAS methods, previous approaches only have been applied to other domains: images, texts or time series. Our work addresses this limitation by introducing a novel NAS algorithm -SeqNAS, specifically designed for event sequence classification. We develop a simple yet expressive search space that leverages commonly used building blocks for event sequence classification, including multi-head self attention, convolutions, and recurrent cells. To perform the search, we adopt sequential Bayesian Optimization and utilize previously trained models as an ensemble of teachers to augment knowledge distillation. As a result of our work, we demonstrate that our method surpasses state-of-the-art NAS methods and popular architectures suitable for sequence classification and holds great potential for various industrial applications.

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Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Cited by 42 publications

References 0 publications

Navigating the Data Security Landscape: Challenges and Solutions in Financial Markets amid Digitalization and Artificial Intelligence

Navigating the Data Security Landscape: Challenges and Solutions in Financial Markets amid Digitalization and Artificial Intelligence

Performance evaluation of technical indicators for forecasting the moroccan stock index using deep learning

SeqNAS: Neural Architecture Search for Event Sequence Classification

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