Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges

Sen, Jaydip; Sen, Rajdeep; Dutta, Abhishek

doi:10.5772/intechopen.101120

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…This learning process consists of two main stages, namely training and testing, with the aim of acquiring intelligence through experience. The field of machine learning focuses on developing computer programs that can automatically self-improve based on the experiences gained [11].…”

Section: Machine Learningmentioning

confidence: 99%

Applying Artificial Intelligence to Classify the Maturity Level of Coffee Beans During Roasting

Herman Suryana,

Kusuma Raharja

2023

Int. J. Eng. Scie. and Inform. Technology.

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Coffee is a highly popular beverage worldwide. The quality of coffee is often judged based on its aroma and taste. Good coffee quality is influenced by various parameters during the coffee bean roasting process. Roasting is a crucial step where green coffee beans are heated at high temperatures, undergoing chemical reactions such as hydrolysis, polymerization, and pyrolysis. The color changes during the roasting process are caused by melanoidin, which results from Maillard and caramelization reactions, also impacting the flavor profile. Therefore, it is essential to accurately classify the level of coffee bean maturity. In the development of supercomputer technology, particularly with high-speed GPU microprocessors and large memory capacities, artificial intelligence algorithms have been widely implemented in various applications. Research on smart machines has been conducted to create systems resembling human intelligence. One of its applications is in recognizing the maturity level of coffee beans during roasting. In this study, image segmentation using ROI (Region Of Interest) and RGB color features are utilized to identify the characteristics of each coffee bean image. Additionally, CNN (Convolutional Neural Network) is employed for the classification stage, and this model is implemented into an Android smartphone device to detect the type of coffee bean being roasted. After the training process with 100 epochs, the model achieved a loss of 0.12 and a training accuracy of 94.79%. The model is capable of classifying images from the test data with an average accuracy of 85.83% and a loss value of 0.35.

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Section: Machine Learningmentioning

confidence: 99%

Applying Artificial Intelligence to Classify the Maturity Level of Coffee Beans During Roasting

Herman Suryana,

Kusuma Raharja

2023

Int. J. Eng. Scie. and Inform. Technology.

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“…AI automates manual and repetitive tasks involved in financial reporting, such as data entry, reconciliation, and report generation, reducing the risk of errors and accelerating the reporting process (Jejeniwa et al, 2024). AI algorithms analyze vast volumes of financial data with speed and precision, uncovering patterns, trends, and insights that may not be apparent through traditional analysis methods (Sen et al, 2022). By extracting actionable intelligence from structured and unstructured data sources, AI enables organizations to make informed decisions and identify strategic opportunities.…”

Section: Introductionmentioning

confidence: 99%

Transforming Financial Reporting with AI: Enhancing Accuracy and Timeliness

Bernard Owusu Antwi,

Beatrice Oyinkansola Adelakun,

Augustine Obinna Eziefule

2024

Int. j. adv. economics

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The landscape of financial reporting is undergoing a profound transformation fueled by advancements in Artificial Intelligence (AI) technologies. This review explores the revolutionary impact of AI on financial reporting, with a specific focus on enhancing accuracy and timeliness. AI-driven technologies such as machine learning, natural language processing, and predictive analytics are reshaping traditional financial reporting processes. These technologies enable organizations to automate routine tasks, analyze vast volumes of financial data, and extract valuable insights with unprecedented speed and accuracy. By leveraging AI, organizations can streamline data collection, validation, and analysis, thereby reducing manual errors and improving the overall quality of financial reports. One of the key advantages of AI in financial reporting is its ability to identify patterns and anomalies in financial data that may go unnoticed by human analysts. Machine learning algorithms can detect irregularities in financial transactions, flag potential risks, and enhance fraud detection capabilities, thus bolstering the integrity and reliability of financial reports. Furthermore, AI-powered natural language processing (NLP) algorithms enable organizations to extract relevant information from unstructured data sources such as financial statements, regulatory filings, and news articles. By analyzing textual data, NLP algorithms can generate insights into market trends, competitive dynamics, and regulatory developments, providing decision-makers with valuable intelligence to inform financial reporting decisions. In addition to improving accuracy, AI plays a crucial role in enhancing the timeliness of financial reporting. By automating time-consuming tasks such as data entry, reconciliation, and financial statement preparation, AI enables organizations to expedite the reporting process and deliver financial information to stakeholders in a more timely manner. This not only meets regulatory deadlines but also enables stakeholders to make informed decisions based on up-to-date financial information. Moreover, AI facilitates real-time monitoring of financial performance metrics, enabling organizations to proactively identify emerging trends, risks, and opportunities. Predictive analytics algorithms can forecast future financial outcomes, enabling organizations to anticipate market changes and adjust their strategies accordingly, thereby enhancing agility and responsiveness in financial reporting. The integration of AI technologies is transforming financial reporting practices, enhancing both accuracy and timeliness. By automating routine tasks, analyzing vast datasets, and providing valuable insights, AI enables organizations to produce high-quality financial reports that meet the needs of stakeholders in a dynamic and rapidly evolving business environment. As AI continues to evolve, its role in financial reporting will only become more prominent, driving efficiency, transparency, and accountability across the financial reporting ecosystem. Keywords: Artificial Intelligence, Financial Reporting, Accuracy, Timeliness, Machine Learning.

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“…Customers now enjoy ease and convenience in banking transactions that were non-existent in the previous decades, (Silva et al , 2013; Illia et al , 2015) using service innovations like Automatic Teller Machines, mobile banking and of late internet banking. The COVID-19 pandemic only served to increase the adoption of artificial intelligence (AI)-enabled internet banking among hitherto hesitant users of the medium (Sen, 2021). The self-conducting of banking transactions on an AI-enabled device is shown to have increased strategic advantage, especially in a developing country like India (Klein and Mayer, 2011; Reeves and Sabharwal, 2013).…”

Section: Introductionmentioning

confidence: 99%

Exploring India’s Generation Z perspective on AI enabled internet banking services

Hameed

Nigam²

2022

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Purpose India is a rapidly developing economy with a rapidly expending internet infrastructure and among the largest Generation Z population. This generation is tech savvy and the access to technology and network creates a conducive environment for such usage. Internet banking for the same reasons is growing leaps and bounds. The introduction of artificial intelligence (AI) has created disruptions in the traditional banking also. This paper aims to analyze the comfort level and usage of AI-enabled banking services by Generation Z. Design/methodology/approach The data is collected from 272 Generation Z members. The differential aspects, that is, the relationship of independent variables with dependent variables (AI-enabled internet banking), were analyzed using the structural equation modeling approach. Findings Defining factors for AI-enabled internet banking were identified. The results of factors were consistent with previous studies. It was found that the usage of AI-enabled internet banking services is insignificant, indicating that Generation Z does not perceive any advantage in using AI-enabled internet banking services. Research limitations/implications This paper does not incorporate age groups other than Generation Z. Further research could throw light on the difference based on age groups. Further research is required to deeply understand why Generation Z does not perceive AI-enabled internet services as very important. Practical implications It has been observed that internet banking is important for Generation Z, but they also place greater importance on interpersonal communication. Banks need to consider this in designing their internet banking services. Originality/value This paper addresses the gap between comfort with and usage of AI-enabled internet banking services, by Generation Z. This paper indicates that the comfort with AI-enabled internet banking services does not translate to usage.

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Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges

Cited by 10 publications

References 33 publications

Applying Artificial Intelligence to Classify the Maturity Level of Coffee Beans During Roasting

Applying Artificial Intelligence to Classify the Maturity Level of Coffee Beans During Roasting

Transforming Financial Reporting with AI: Enhancing Accuracy and Timeliness

Exploring India’s Generation Z perspective on AI enabled internet banking services

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