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Financial Technology and Innovation
Volume 2 , Issue 2, PP: 08-17 , 2023 | Cite this article as | XML | Html |PDF

Title

Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment

  Sanjay Kumar Suman 1 *

1  Professor, Dept. of ECE, and Dean R&D, St. Martin's Engineering College, Secunderabad, Telangana, India
    (deanrnd@smec.ac.in)


Doi   :   https://doi.org/10.54216/FinTech-I.020201

Received: January 28, 2023 Accepted: June 22, 2023

Abstract :

In this work, we describe an adaptive financial management strategy, tailor-made to meet the demands of, and capitalize on, an economy ruled by AI. The suggested solution combines three essential algorithms: LSTM-based machine learning for economic forecasting; SHAP-based explainable AI for openness in decision-making; and blockchain technology with proof-of-work (PoW) security. This LSTM-based method handles the sequential data often seen in time series analysis, which is crucial for effective financial forecasting. It is particularly effective at identifying complex interrelationships in financial time series data, providing a solid basis for reliable forecasting. By giving each feature in a prediction model an equal amount of weight, the SHAP algorithm improves the openness of decisions. The experimental results confirm the superiority of the suggested technique over the conventional methods. It uses dynamic Machine Learning models, in particular LSTM networks, to provide more precise economic forecasts than static models based on averages. Using SHAP, explainable AI solves the problem of interpretability that plagues conventional techniques, allowing for more open deliberation. The combination of Blockchain with PoW gives better security, overcoming the risks of centralized systems employed in previous approaches. The suggested adaptive strategy provides a comprehensive and robust framework for managing finances in a world controlled by artificial intelligence.

Keywords :

Adaptive Financial Management; AI Integration , Blockchain , Decision Transparency; Explainable AI; SHAP Algorithm; Tamper Resistance; Time Series Forecasting; Transparent Decision-Making.

References :

[1]     W. Li, “Multimedia teaching of college musical education based on deep learning,” Mobile Information Systems, vol. 2021, no. 2, Article ID 5545470, 10 pages, 2021.

[2]     L. Li and Z. Han, “Design and innovation of audio iot technology using music teaching intelligent mode,” Neural Computing & Applications, vol. 34, pp. 1–14, 2022.

[3]     R. Wang, “Computer-aided interaction of visual communication technology and art in new media scenes,” Computer-Aided Design and Applications, vol. 19, no. S3, pp. 75–84, 2021.

[4]     T. Guo, “Analysis on the development path of music education in rural areas under the influence of covid-19 outbreak,” Converter, vol. 2021, pp. 169–175, 2021.

[5]     D. Han, Y. Kong, J. Han, and G. Wang, “A survey of music emotion recognition,” Frontiers of Computer Science, vol. 16, no. 6, Article ID 166335, 2022.

[6]     S. G. Onwubiko and A. Calilhanna, “Interdisciplinary physical music: a blind spot in education on acoustics,” Journal of the Acoustical Society of America, vol. 148, no. 4, p. 2697, 2020.

[7]     Y. Li, “Speech-assisted intelligent software architecture based on deep game neural network,” International Journal of Speech Technology, vol. 24, no. 1, pp. 57–66, 2021.

[8]     V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.

[9]     A. J. Lou and S. M. Jaeggi, “Reducing the prior-knowledge achievement gap by using technology-assisted guided learning in an undergraduate chemistry course,” Journal of Research in Science Teaching, vol. 57, no. 3, pp. 368–392, 2020.

[10]   H. Li and J. Ji, “Analysis of English listening obstacles based on computer-assisted instruction,” Computer-Aided Design and Applications, vol. 18, no. S4, pp. 130–140, 2021.

[11]   S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. [Online]. Available: https://doi.org/10.1155/2021/2942808

[12]   Marani R, Perri AG (2022) Design of an Intelligent System for Defect Recognition in Composite Materials using Lock-In Thermography. Int J Emerg Technol Adv Eng 12(2):29–36

[13]   A. Verma, V. Tiwari, M. Lovanshi, and R. Shrivastava, "A Human Body Part Semantic Segmentation Enabled Parsing for Human Pose Estimation," in Proc. of the 2023 5th International Conf. on Image, Video and Signal Processing, 2023, pp. 43-50.

[14]   Meneses-Claudio B, Nuñez-Tapia L, Alvarado-Díaz W (2022) Organization, Extraction, Classification and Prediction of Age in Facial Images using Convolutional Neuronal Network. Int J Emerg Technol Adv Eng 12(3):55–62

[15]   E. L. Huamaní and L. Ocares-Cunyarachi, "Use of artificial intelligence for face detection with face mask in real time to control the entrance to an entity," Int. J. Emerg. Technol. Adv. Eng., vol. 11, no. 11, pp. 68-75, 2021.

[16]   M. Bathre and P. K. Das, "Smart dual battery management system for expanding lifespan of wireless sensor node," Int J Commun Syst, vol. 36, no. 3, e5389, 2023.

[17]   N. T. Lam, "Developing a framework for detecting phishing URLs using machine learning," Int. J. Emerg. Technol. Adv. Eng., vol. 11, no. 11, pp. 61-67, 2021.

[18]   T. Mohapatra, S. S. Mishra, M. Bathre, and S. S. Sahoo, "Taguchi and ANN-based optimization method for predicting maximum performance and minimum emission of a VCR diesel engine powered by diesel, biodiesel, and producer gas," World J. Eng., vol. ahead-of-print, no. ahead-of-print, 2023.

[19]   K. Shubham, V. Tiwari, and K. S. Patel, "Predictive Learning Methods to Price European Options Using Ensemble Model and Multi-asset Data," International Journal on Artificial Intelligence Tools, 2023.

[20]   Z. M. Zabidi, A. N. Alias, N. A. Zakaria, Z. S. Mahmud, R. Ali, M. K. Yaakob, and S. Masrom, "Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices," Int. J. Emerg. Technol. Adv. Eng., vol. 11, no. 11, pp. 1-14, 2021.

[21]   M. Bathre and P. K. Das, "Water supply monitoring system with self-powered LoRa based wireless sensor system powered by solar and hydroelectric energy harvester," Comput Stand Interfaces, vol. 82, 103630, 2022.

 


Cite this Article as :
Style #
MLA Sanjay Kumar Suman. "Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment." Financial Technology and Innovation, Vol. 2, No. 2, 2023 ,PP. 08-17 (Doi   :  https://doi.org/10.54216/FinTech-I.020201)
APA Sanjay Kumar Suman. (2023). Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment. Journal of Financial Technology and Innovation, 2 ( 2 ), 08-17 (Doi   :  https://doi.org/10.54216/FinTech-I.020201)
Chicago Sanjay Kumar Suman. "Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment." Journal of Financial Technology and Innovation, 2 no. 2 (2023): 08-17 (Doi   :  https://doi.org/10.54216/FinTech-I.020201)
Harvard Sanjay Kumar Suman. (2023). Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment. Journal of Financial Technology and Innovation, 2 ( 2 ), 08-17 (Doi   :  https://doi.org/10.54216/FinTech-I.020201)
Vancouver Sanjay Kumar Suman. Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment. Journal of Financial Technology and Innovation, (2023); 2 ( 2 ): 08-17 (Doi   :  https://doi.org/10.54216/FinTech-I.020201)
IEEE Sanjay Kumar Suman, Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment, Journal of Financial Technology and Innovation, Vol. 2 , No. 2 , (2023) : 08-17 (Doi   :  https://doi.org/10.54216/FinTech-I.020201)