Artificial Intelligence Techniques in the Financial Services Industry

Dear Colleagues,

In the modern era of technology, artificial intelligence (AI) has come to have a significant effect on the financial services sector, and with the advent of innovations involving AI, data have become the most vital resources in a financial services organization. AI encompasses a range of methods that serve to increase the financial industry’s efficiency overall, such as machine learning (ML), natural language processing (NLP), and image recognition (IR). In the financial sector, artificial intelligence and machine learning serve various purposes, from workforce automation and financial crime detection to customer service via chatbot assistants. Corporations are embracing AI at a faster pace, redefining processes such as performance assessment, budgeting, data analysis, and attending to clients, given that AI is capable of assisting financial companies in the decision-making process by processing and interpreting data in real time. Insurance providers are utilizing artificial intelligence at an increasing rate to streamline operations across fields such as intelligent underwriting, price optimization, or claims processing, due to the fact that AI algorithms can identify patterns within insurance claim data and estimate when certain individuals may require further medical care in a future period. Moreover, as banking privacy is critical, AI is an invaluable asset in the struggle against fraudulent activity. In this regard, massive data sets are processed by AI algorithms, which subsequently utilize the outcomes to instantly identify odd trends or unethical behaviors. AI algorithms are also implemented by investment professionals to boost trading velocity and effectiveness, which has revolutionized the way in which stocks are traded. Therefore, this Special Issue will focus on an array of topics that include, but are not limited to, the following: 

• Algorithmic trading techniques powered by artificial intelligence;
• Option pricing with neural network models;
• Investor sentiment analysis based on artificial intelligence;
• Artificial neural networks for business valuation;
• Financial statement fraud using artificial intelligence techniques;
• Machine learning techniques for detecting and preventing credit card payment fraud;
• Detecting malicious clients with machine learning algorithms;
• Machine learning algorithms for predicting customer bank deposits;
• Machine learning credit scoring systems;
• Insurance fraud prediction via machine learning methods.

Prof. Dr. Ştefan Cristian Gherghina
Guest Editor

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• artificial intelligence (AI)
• machine learning (ML)
• swarm learning (SL)
• artificial neural networks (ANNs)
• supervised learning
• unsupervised learning
• reinforcement learning
• deep learning
• natural language processing (NLP)
• computer vision