Basic Structure on Artificial Intelligence: A Revolution in Risk Management and Compliance

Bedi, Pradeep; Goyal, S. B.; Kumar, Jugnesh

doi:10.1109/iciss49785.2020.9315986

“…Risk management and compliance are also key areas where AI is set to make significant contributions. Improved risk assessment capabilities, enabled by AI, will allow for more accurate predictions of loan defaults, market risks, and investment outcomes, enhancing the overall stability and reliability of banking services (Bedi et al, 2020). In terms of regulatory compliance, AI will play a crucial role in helping banks navigate the complex and ever-changing regulatory landscape, ensuring adherence to laws and regulations while minimizing operational risks (Bartram et al, 2019).…”

Section: Future Of Ai In Bankingmentioning

confidence: 99%

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

2024

Journal of Informatics Education and Research

0

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This paper presents an in-depth study of the evolving role of Artificial Intelligence (AI) in the Indian banking sector, emphasizing its transformative impact on service delivery, operational efficiency, and customer engagement.Highlighting key advancements and integration of AI, the paper emphasizes on AI's crucial role in modernizing traditional banking practices and addressing contemporary challenges. It offers a balanced view by discussing both the opportunities AI presents for enhancing banking services and the challenges, including technical complexities and regulatory considerations. The future outlook section anticipates the continued growth of AI in banking, focusing on its potential to further personalize banking experiences and improve risk management. The study aims to provide a holistic view of AI's integration in Indian banking, serving as a resource for understanding the dynamic interplay between technological innovation and financial services.

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“…On the other hand, in Spanish papers, dominant keywords include artificial intelligence, software, software engineering, project management, management, project, agile methodologies, risk, fuzzy and expert systems, while in Turkish papers, the dominant keywords are risk management, software, software engineering, management, agile methodologies, Scrum, software project management. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Maximum number of items (1-50).…”

Section: Figure 3 -The Trend Of Publication Of Papers For the Period ...mentioning

confidence: 99%

“…Artificial intelligence improves risk assessment by understanding the occurrence of risks in a specific project context [16]. Namely, the combination of machine learning with Monte Carlo simulation, applied in the analytical approach to risk management, can help improve risk assessment and simulation in the project risk management process [17].…”

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Implementing artificial intelligence tools for risk management in software projects

Antić

2023

Tehnika

0

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In recent years, there has been a significant surge in interest in the incorporation of artificial intelligence (AI) within the field of software engineering. This phenomenon can be attributed to the fact that AI has become ubiquitous and increasingly accessible, thereby finding effective application across various pivotal facets of software systems. Its contribution extends not only to the creation of novel functionalities but also to the enhancement of existing processes within software projects, often resulting in substantially improved adaptability to specific user requirements. Within this paper, we provide an overview of the application of AI tools within one of the critical domains of software project management - risk management. To achieve this objective, a bibliometric analysis of literature pertaining to risk management in software projects employing AI tools has been conducted. The primary aim of this study is to identify and analyze key trends, authors, journals, and keywords within this multidisciplinary domain, in order to gain a better understanding of the progress and relevance of research concerning risk management in software projects utilizing AI tools. The methodology encompasses a review of pertinent databases and the identification of relevant publications using keywords associated with software projects, risk management, and artificial intelligence. Quantitative parameters such as the number of published works, author collaborations, citation frequency, and the distribution of articles over time and across journals are analyzed.

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“…Second, compared with Algorithms 1 and 2, the overall detection results are relatively average for each type of target data, and the overall recognition rate fluctuates around 70%. 9 data is a summary of the methodological part through code writing, algorithm calling, and output of the next stages of detection values [64][65][66]. Work is divided into five stages: machine learning, recognition ratio, sample testing, frequency fluctuation, and variance.…”

Section: Summary Of Preliminary Experimentsmentioning

confidence: 99%

Implementation of Artificial Intelligence Image Emotion Detection Mechanism Based on Python Architecture for Industry 4.0

Jinnuo

¹

,

Goyal

²

,

Tesfayohanis³

et al. 2022

Journal of Nanomaterials

7

0

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Since the beginning of the 21st century, with the development of information technology, researchers in various fields have gradually increased their research on human emotion and behavior. The current research mechanism used in emotion and behavior research is artificial intelligence technology. Through the literature survey and data analysis in related fields, it is found that the acquisition of human emotions and behaviors will be carried out through facial feature algorithm for point capture and combined with machine learning for output detection and analysis. Among them, the detection process requires machine learning of artificial intelligence first. This paper firstly analyzes and summarizes the advantages of Python programs at this stage and completes the preliminary work of system construction by setting and installing platform parameters. In the research process, this paper uses the existing algorithm to apply the σ E value algorithm to the samples and conducts preliminary tests. The overall detection values in the test data are relatively average, and there are still differences in the samples. At the same time, we compare the U E and T E detection algorithms according to the output Y value of the algorithm in the machine learning. The detection rate of some emoticons in the U E algorithm is high, but the detection rate of other emoticons is low. Finally, according to the limitation of the output method in the mathematical formula, a new algorithm σ x of taking the weighted sum and taking the logarithm and then taking the square root is proposed again. According to the statistical analysis, the overall average value of the final algorithm has been improved, and the overall detection rate is about 80%; compared with the T E and U E algorithms, the overall detection frequency fluctuates less. The σ x algorithm in the frequency fluctuation data table in the paper is also superior to the existing algorithms in machine learning, sample testing, and data in the frequency fluctuation. Our next direction will be to use the Python main program to perform AI automatic facial emotion detection work by combining the new algorithm σ x with the V value, DWT, and CNN algorithm in the facial recognition feature through machine learning.

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Basic Structure on Artificial Intelligence: A Revolution in Risk Management and Compliance

Cited by 7 publications

References 9 publications

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

The Landscape of AI in Indian Banking Sector: A Theoretical Perspective

Implementing artificial intelligence tools for risk management in software projects

Implementation of Artificial Intelligence Image Emotion Detection Mechanism Based on Python Architecture for Industry 4.0

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