Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

User behavior that raises suspicion: Machine learning can spot fraud or compromised applications as they happen. According to Google's fraud czar Shuman Ghosemajumder, AI will eventually fully automate most cyber security tasks. 16. Education: AI tutors enable one-on-one instruction for students. They can help students who receive human or robotic tutors feel less anxious and stressed. AI can lead to a dysfunctional environment with retaliatory repercussions, such as using technology to make it difficult for kids to focus. In another example, AI could assist teachers in making early predictions about their students in a virtual learning environment (VLE) like Moodle. Learning activities must be conducted online, especially during the COVID-19 pandemic, to prevent the virus from spreading through in-person contact. 17. Finance: Financial institutions have used artificial neural network algorithms for a long time to identify charges or claims that are unusual and flag them for further human inquiry. When Security Pacific National Bank established a fraud prevention taskforce in 1987 to combat debit card fraud, AI's application in banking was officially underway. Kasisto and Moneystream use AI. Banks use AI to streamline operations, handle books, invest in stocks, and manage real estate. Even when there is no business, AI can respond to changes. By watching for unusual changes or anomalies in behavioral patterns, AI fights fraud and financial crimes. Major economic theories have been altered using AI in decision-making and online trading applications. For instance, AI-based platforms for buying and selling estimate unique demand and supply curves, enabling special pricing. Markets have become more efficient due to the reduction of information asymmetry brought about by AI machines. 18. Trading and Investment: Algorithmic trading is the process of making millions of trades a day without the help of a human by using AI systems to make trading choices at speeds orders of magnitude faster than any human can. Such high-frequency trading is a rapidly expanding industry. Nowadays, many banks, funds, and proprietary trading companies handle their whole portfolios using AI. Large institutional investors frequently utilize automated trading systems, but smaller businesses can also deploy AI trading systems. Large financial firms use AI to support their investment procedures. Aladdin, BlackRock's AI engine, is utilized by businesses and clients to assist in making investment decisions. Among its features is the use of natural language processing for text analysis, including that found in news articles, broker reports, and social media feeds. Then it determines how people feel about the companies mentioned and gives them a score. SQREEM (Sequential Quantum Reduction and Extraction Model) is a data mining d by banks like UBS and Deutsche Use Bank to create consumer profiles and match them with wealth management products. 19. Audit: AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing assurance, and reducing audit duration. 20. Anti-Money Laundering: For anti-money laundering (AML), artificial intelligence (AI) software like Laundro Graph, which employs modern suboptimal datasets, could be deployed. It is possible to "develop the AML pipeline into a robust, scalable solution with a reduced false positive rate and high adaptability" using AI. A study on deep learning for AML identified "key challenges for researchers" as "access to recent real transaction data and scarcity of labeled training data, and data being highly imbalanced" and recommends that future research focus on "explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labeled data; and joint research programs between the research community and industry to benefit both parties." 21. Government: AI facial recognition systems are utilized, particularly in China, for widespread monitoring. 2019 saw the deployment of AI-managed traffic signals in Bengaluru, India. The signal timing in this system is adjusted based on the amount of time required to clear traffic using cameras to monitor traffic density. 22. Military: Many nations are using AI for military purposes. The primary applications improve integration, interoperability, communications, sensors, command, control, etc. Research focuses on semi-autonomous and autonomous vehicles, logistics, cyber operations, information operations, and intelligence gathering and analysis. AI technologies can coordinate sensors and effectors, identify threats, designate enemy positions, mark targets, acquire targets, and coordinate and deconflict distributed joint fires between networked combat vehicles involving manned and unmanned teams. In Syria and Iraq, AI was used in military operations. Robotics expenditures for the military increased from US$5.1 billion in 2010 to US$7.5 billion in 2015. The employment of unnerved military drones is widespread, and many scientists avoid military applications. 23. Workplace Health and Safety : AI-enabled chatbots eliminate the need for people to handle routine call center duties. To avoid overworking, machine learning in sentiment analysis can detect weariness. Like how they help avert natural catastrophes, decision support systems can improve the effectiveness of disaster response. Predictive analytics may be applied to material handling manual employees to lower the risk of musculoskeletal injury. Wearable sensor data can help with risk analysis, research, and occupational health surveillance. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 24 ( ) Year 2023 D Journey of Artificial Intelligence Frontier: A Comprehensive Overview