Developing AI Models for Automated Financial Reporting
Abstract
Reliability, automation, and accuracy in financial reporting have become more pertinent than ever with rising regulatory requirements, rapid expansion in business activity, and increasing complexities, as these require analysis of larger volumes of financial data than ever before. Artificial intelligence (AI) could be a savior in this world to drive efficient, automated, and compliant financial reporting. AI can surpass human capabilities in analyzing enormous data sets, identifying patterns and trends, enabling real-time insights on business performance, identifying fraudulent activities, and ensuring compliance. AI has the ability to present the results in real-time to enable efficient decision-making. Timely and effective decision-making gives a company an edge over others. AI models are trained to learn from the data provided to evolve their model, ultimately increasing their precision, improving outcomes over time, and helping in better decision-making as new data is captured and analyzed.
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References
Tamanampudi, Venkata Mohit. "NLP-Powered ChatOps: Automating DevOps Collaboration Using Natural Language Processing for Real-Time Incident Resolution." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 530-567.
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
Pal, Dheeraj Kumar Dukhiram, et al. "Implementing TOGAF for Large-Scale Healthcare Systems Integration." Internet of Things and Edge Computing Journal 2.1 (2022): 55-102.
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
J. Singh, “The Future of Autonomous Driving: Vision-Based Systems vs. LiDAR and the Benefits of Combining Both for Fully Autonomous Vehicles ”, J. of Artificial Int. Research and App., vol. 1, no. 2, pp. 333–376, Jul. 2021
Gadhiraju, Asha. "Improving Hemodialysis Quality at DaVita: Leveraging Predictive Analytics and Real-Time Monitoring to Reduce Complications and Personalize Patient Care." Journal of AI in Healthcare and Medicine 1.1 (2021): 77-116.
Gadhiraju, Asha. "Empowering Dialysis Care: AI-Driven Decision Support Systems for Personalized Treatment Plans and Improved Patient Outcomes." Journal of Machine Learning for Healthcare Decision Support 2.1 (2022): 309-350.
Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
J. Singh, “Understanding Retrieval-Augmented Generation (RAG) Models in AI: A Deep Dive into the Fusion of Neural Networks and External Databases for Enhanced AI Performance”, J. of Art. Int. Research, vol. 2, no. 2, pp. 258–275, Jul. 2022
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
Tamanampudi, Venkata Mohit. "A Data-Driven Approach to Incident Management: Enhancing DevOps Operations with Machine Learning-Based Root Cause Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 419-466.
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