APPROACHES FOR MACHINE LEARNING IN FINANCE
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| Formato: | Recurso digital |
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2020
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| _version_ | 1866902203310014464 |
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| author | Shubham Shukla |
| author_facet | Shubham Shukla |
| contents | <p>Financial institutions have undergone fundamental transformation through machine learning technology because they<br>deploy this system for analytical data processing, decision support systems, and risk management processes.<br>Organizations apply their powerful algorithms in machine learning to both accurately detect patterns and automate<br>processes while forecasting market trends for large amounts of data. Machine learning brings fundamental sector<br>modification to financial institutions, enabling them to identify fraudulent activity and create automated trading<br>procedures while taking control of credit resources. The processing of soft data obtained from news and social media<br>sentiments enhances the operational efficiency of forecasting systems alongside decision-making capabilities.<br>Financial institutions obtain new opportunities with ML technologies while these technologies develop innovative<br>solutions and operational improvements that lead to market success.<br>Financial departments implementing machine learning technologies create specific, powerful effects on their<br>regulatory compliance while simultaneously enhancing their risk-based operations. The assessment approaches for<br>risk use historical information analysis with static pattern recognition models, which prove insufficient when dealing<br>with present market fluctuations. Machine learning differs from traditional systems because it uses time-sensitive data<br>analysis to detect ailments and project threats accurately. ML technology analyzes fake activities through abnormal<br>behaviors that differ from conventional patterns. Financial institutions perform ML-based systematic regulatory<br>assessments to uncover abnormal transactions, strengthening their AML and KYC regulatory operations. Such<br>systems decrease operational spending and stabilize financial stability to facilitate better security control.<br>Machine learning implements deliver multiple benefits to financial services, but such benefits generate technical<br>challenges for these institutions. Implementing machine learning in finance encounters numerous challenges caused<br>by privacy-related problems, while unknown operational mechanics promote discriminatory machine behavior. The<br>identified situations produce ethical issues, which create risks for legal complications. Financial organizations need to<br>show total transparency and fairness in their ML systems while they meet all current financial regulations and those<br>that emerge in the future. Financial organizations need reliable data protection systems to maintain their confidential<br>records since they manage large amounts of information. A complete success of machine learning systems requires<br>collaboration between technologists, financial experts, and regulators to address operational challenges that will<br>maximize system benefits. Correct implementation alongside continuous developmental efforts will drive ML-based<br>finance innovation toward its complete effective utilization.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14874581 |
| institution | Zenodo |
| language | |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | APPROACHES FOR MACHINE LEARNING IN FINANCE Shubham Shukla Machine Learning/supply & distribution Artificial Intelligence data analysis Machine learning <p>Financial institutions have undergone fundamental transformation through machine learning technology because they<br>deploy this system for analytical data processing, decision support systems, and risk management processes.<br>Organizations apply their powerful algorithms in machine learning to both accurately detect patterns and automate<br>processes while forecasting market trends for large amounts of data. Machine learning brings fundamental sector<br>modification to financial institutions, enabling them to identify fraudulent activity and create automated trading<br>procedures while taking control of credit resources. The processing of soft data obtained from news and social media<br>sentiments enhances the operational efficiency of forecasting systems alongside decision-making capabilities.<br>Financial institutions obtain new opportunities with ML technologies while these technologies develop innovative<br>solutions and operational improvements that lead to market success.<br>Financial departments implementing machine learning technologies create specific, powerful effects on their<br>regulatory compliance while simultaneously enhancing their risk-based operations. The assessment approaches for<br>risk use historical information analysis with static pattern recognition models, which prove insufficient when dealing<br>with present market fluctuations. Machine learning differs from traditional systems because it uses time-sensitive data<br>analysis to detect ailments and project threats accurately. ML technology analyzes fake activities through abnormal<br>behaviors that differ from conventional patterns. Financial institutions perform ML-based systematic regulatory<br>assessments to uncover abnormal transactions, strengthening their AML and KYC regulatory operations. Such<br>systems decrease operational spending and stabilize financial stability to facilitate better security control.<br>Machine learning implements deliver multiple benefits to financial services, but such benefits generate technical<br>challenges for these institutions. Implementing machine learning in finance encounters numerous challenges caused<br>by privacy-related problems, while unknown operational mechanics promote discriminatory machine behavior. The<br>identified situations produce ethical issues, which create risks for legal complications. Financial organizations need to<br>show total transparency and fairness in their ML systems while they meet all current financial regulations and those<br>that emerge in the future. Financial organizations need reliable data protection systems to maintain their confidential<br>records since they manage large amounts of information. A complete success of machine learning systems requires<br>collaboration between technologists, financial experts, and regulators to address operational challenges that will<br>maximize system benefits. Correct implementation alongside continuous developmental efforts will drive ML-based<br>finance innovation toward its complete effective utilization.</p> |
| title | APPROACHES FOR MACHINE LEARNING IN FINANCE |
| topic | Machine Learning/supply & distribution Artificial Intelligence data analysis Machine learning |
| url | https://doi.org/10.5281/zenodo.14874581 |