Artificial Intelligence &amp; Machine Learning in Finance: A literature review

Lakhchini, Wassima; Wahabi, Rachid; Kabbouri, Mounime El; Bp, Casa; Hassan, Settat

doi:10.5281/zenodo.7454232

Cited by 4 publications

(3 citation statements)

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“…This study explores a broad spectrum of scholarly studies using machine learning to determine insurance rates, examining how these advanced algorithms can outperform traditional actuarial methods in terms of accuracy, efficiency, and adaptability to dynamic market conditions. The initial research laid a solid basis by demonstrating how machine learning may improve risk assessment accuracy and granularity, outperforming traditional actuarial approaches based on historical data and linear regression models [12]. These traditional procedures frequently proved insufficient in quickly responding to rising risk factors, and modifying premium rates was difficult due to their reliance on broad demographic groups and a limited range of variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approaches for Insurance Pricing: A Case Study of Public Liability Coverage in Morocco

2024

IJFMR

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As data-driven technologies continue to advance, the importance of machine learning (ML) techniques in enhancing the accuracy of insurance premium calculations cannot be overstated. That is especially crucial because traditional actuarial methods often fail to incorporate individual risk factors fully. This limitation significantly impacts the insurance industry's capacity to accurately determine premium prices, which in turn affects financial stability and customer satisfaction. This research seeks to assess the efficiency insurance premium calculations through different regression models in ML, including polynomial, decision tree, random forest, and gradient boosting. The study employs rigorous analysis techniques using a comprehensive dataset from a Moroccan vehicle insurance company, including claim history and insurance categories. The dataset is split into training and testing sets to assess the accuracy of the ML models using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R-squared). Initial findings suggest that ML models greatly surpass traditional actuarial methods, indicating the potential for machine learning to transform premium pricing strategies. That could result in more customized and financially sustainable outcomes within the insurance industry. The findings of this paper are likely to contribute to national insurance premium policy and expand the existing literature on this subject in Morocco.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approaches for Insurance Pricing: A Case Study of Public Liability Coverage in Morocco

2024

IJFMR

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“…However, it is also necessary to investigate unsupervised learning techniques, such as clustering, and text-mining methods to convert financial texts into feature vectors. Lakhchini and Hassan [6] used a scoping review in conjunction with an embedded review to assess advancing AI and ML in financial research. Their study outlines important fields where AI and ML are heavily used, such as portfolio management, risk management, financial fraud detection, sentiment analyses, stock market prediction, data protection, and big data analytics.…”

Section: Approachmentioning

confidence: 99%

“…If a machine can perform these activities, it can be considered to possess AI. AI's achievements rely on algorithms, statistical models, and programs that process and analyze extensive data [4][5][6][7][8]. Some common AI applications include expert systems, natural language processing, speech recognition, and machine vision.…”

Section: Introductionmentioning

confidence: 99%

The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework

Al-Baity

2023

Sustainability

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Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study’s findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry’s needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI’s development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI’s potential in the Saudi Arabian financial sector.

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Forecasting Volatility of Bank Deposits of Individuals Using Hybrid Arcing -ARIMA Approach: Forecasting Volatility of Bank Deposits

Boyoukliev,

Kulina,

Gocheva-Ilieva

2023

Proceedings of the 2023 6th International Conference on Mathematics and Statistics

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Artificial Intelligence & Machine Learning in Finance: A literature review

Cited by 4 publications

References 0 publications

Machine Learning Approaches for Insurance Pricing: A Case Study of Public Liability Coverage in Morocco

Machine Learning Approaches for Insurance Pricing: A Case Study of Public Liability Coverage in Morocco

The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework

Forecasting Volatility of Bank Deposits of Individuals Using Hybrid Arcing -ARIMA Approach: Forecasting Volatility of Bank Deposits

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