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Machine Learning for Finance Risk Assessment

Dr. M. Dhanalakshmi working as an Associate professor in LEAD college of Management Autonomous, palakkad Kerala . Dr. M. Dhanalakshmi’s dedication to education, research, and professional development is evident through her extensive experience and numerous accolades. Her contributions have significantly impacted the academic and professional communities in which she has been involved.She has participated in various workshops and faculty development programs focusing on research techniques, statistical analysis, and emerging trends in commerce and management. she has numerous publications and has presented papers at various international and national seminars and conferences. Her research interests include organizational climate, job satisfaction, and innovative HR practices. has presented numerous papers at national and international seminars and conferences. She has published extensively in various journals on topics such as employee job satisfaction, green management, corporate social responsibility, and the impact of economic modernization on the steel industry. Patent Holder: Various patents including one for “Analysis of Team’s Interaction Within Organization Towards the Development of Performance” International patent, UK Design Data processing device for share market analytics International Design Classification.

Dr. Brajesh Kumar Singh, Received the B.Tech in Electronics and Communication Engineering from Delhi Technological University, New Delhi (Formerly DCE, Delhi University) and, M.Tech and P.hD.both completed from Guru Gobind Singh Indraprastha University, New Delhi. He is working as Associate Professor inGalgotia College of Engineering and Technology (GCET), Greater Noida, Utter Pradesh, India.He has more than 13 years of teaching experience He has published more than 12 research papers in international Journals, 7 International Conferences, 3 Patents in the field of Image processing, Biometrics, Machine Learning, Communication Technology and IoT.

María Teresa Espinosa Jaramillo, is an outstanding Ecuadorian professional in accounting and auditing with a broad and solid academic background and vast experience in free professional practice, research and teaching. She is a PhD candidate in Economic Sciences from the University of Zulia, Venezuela, has obtained a master’s degree in Business Administration with a specialty in Project Management from the Arturo Prat University of the State of Chile and another in Integral Auditing from the Universidad Técnica Particular de Loja, Ecuador, in addition to a Higher Diploma in Taxation from the same university. Diploma in Tax Management, Diploma in Integral Audit, Diploma in Forensic Audit. María Teresa has made significant contributions to the field of auditing and business management, being the author of important publications such as the book “Forensic Audit, Case Studies in Ecuador”, and articles in prestigious international journals on cybersecurity and financial auditing, such as “AI in Cybersecurity: Threat Detection and Response with Machine Learning” and “Financial Audit as a Key Tool in Business Planning and Development”. He has also collaborated in relevant academic publications, contributing to the advancement of knowledge in his area of expertise.

Dr. Haewon Byeon received the Dr. degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AI-medicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on a 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books.

Description

Machine learning has become an integral tool in financial risk assessment, offering advanced methods for analyzing vast datasets and predicting potential risks with greater accuracy. Traditional risk assessment models often rely on historical data and linear assumptions, which may not capture the complexities of financial markets. In contrast, machine learning algorithms can process large volumes of data from diverse sources, identify patterns, and adapt to changing conditions, providing a more nuanced and dynamic approach to risk management. One significant advantage of machine learning in financial risk assessment is its ability to handle unstructured data. Financial markets generate enormous amounts of data, including news articles, social media posts, and transaction records. Machine learning algorithms, particularly those employing natural language processing (NLP), can analyze this unstructured data to extract relevant information and sentiment, which can be crucial for predicting market movements and identifying potential risks. By incorporating these insights, financial institutions can enhance their risk models and make more informed decisions. Another critical aspect is the use of machine learning for anomaly detection. Financial fraud and irregularities can have severe consequences, and early detection is essential for mitigating these risks. Machine learning techniques, such as clustering and outlier detection, can identify unusual patterns in transaction data that may indicate fraudulent activity. These algorithms continuously learn and improve from new data, increasing their accuracy over time. This proactive approach helps financial institutions stay ahead of potential threats and safeguard their assets. Furthermore, machine learning enhances credit risk assessment by improving the accuracy of credit scoring models. Traditional credit scoring methods often rely on limited data points, such as credit history and income. Machine learning algorithms can incorporate a broader range of variables, including social and behavioral data, to create more comprehensive and accurate credit profiles. This leads to better risk stratification and more personalized lending decisions, ultimately reducing default rates and improving overall financial stability.

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