Accelerating Business Growth with Big Data and Artificial Intelligence

Artificial Intelligence (AI) is considered to be the fourth industrial revolution. Artificial Intelligence with the help of big data has transformed all industries around the world Artificial intelligence refers to the simulation of human or animal intelligence in computational systems so that they are programmed to think like Intelligent beings and mimic the actions of intelligent entities. Computational systems which have programmed intelligence can solve different real-world problems far more accurately and efficiently than computational systems that are deterministic and hardcoded. Since many problems in business and business analytics cannot be solved by deterministic systems, AI plays a major role in tackling problems in the business world Machine learning and deep learning which are subsets of the field of AI is widely used to solve and optimize many problems in business such as marketing, credit card fraud detection, algorithmic trading, customer service, portfolio management, product recommendation according to the needs of customers, insurance underwriting. AI and big data have revolutionized the business world and this paper discusses some AI and big data technologies that are currently being used to accelerate business growth.

[1]  Changjun Jiang,et al.  Random forest for credit card fraud detection , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[2]  Ashwani Kumar,et al.  Cognizant Technology Solutions: Growth and Transformation of Its Data Warehousing and Business Intelligence Division , 2008 .

[3]  Harshal Patel,et al.  Predicting Stock Prices Using LSTM , 2017 .

[4]  Chee Peng Lim,et al.  Credit Card Fraud Detection Using AdaBoost and Majority Voting , 2019, IEEE Access.

[5]  Surendrabikram Thapa,et al.  Oversampling based Classifiers for Categorization of Radar Returns from the Ionosphere , 2020, 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC).

[6]  Omprakash Kaiwartya,et al.  Characteristic of enterprise collaboration system and its implementation issues in business management , 2019, Int. J. Bus. Intell. Data Min..

[7]  Surendrabikram Thapa,et al.  Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[8]  Howon Kim,et al.  Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection , 2016, 2016 International Conference on Platform Technology and Service (PlatCon).

[9]  Zhengyao Jiang,et al.  Cryptocurrency portfolio management with deep reinforcement learning , 2016, 2017 Intelligent Systems Conference (IntelliSys).

[10]  Alex Pentland,et al.  Big Data-Driven Marketing: How Machine Learning Outperforms Marketers' Gut-Feeling , 2014, SBP.

[11]  Nathaniel D. Bastian,et al.  A hybrid recommender system using artificial neural networks , 2017, Expert Syst. Appl..

[12]  Stuart G. Colianni,et al.  Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis , 2015 .

[13]  Surendrabikram Thapa,et al.  Evaluation of Factors Affecting Compressive Strength of Concrete using Machine Learning , 2020, 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA).

[14]  Wim Schoutens,et al.  Machine learning for quantitative finance: fast derivative pricing, hedging and fitting , 2018, Quantitative Finance.

[15]  Scott A. Wright,et al.  The rising tide of artificial intelligence and business automation: Developing an ethical framework , 2018, Business Horizons.

[16]  Chin-Teng Lin,et al.  A review of clustering techniques and developments , 2017, Neurocomputing.

[17]  Deepak Puthal,et al.  A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[18]  Samuel A. Oluwadare,et al.  Credit card fraud detection using machine learning techniques: A comparative analysis , 2017, 2017 International Conference on Computing Networking and Informatics (ICCNI).

[19]  Sean Sands,et al.  From data to action: How marketers can leverage AI , 2020, Business Horizons.

[20]  Ming Zhou,et al.  SuperAgent: A Customer Service Chatbot for E-commerce Websites , 2017, ACL.

[21]  Vijay Laxmi,et al.  Colluding browser extension attack on user privacy and its implication for web browsers , 2016, Comput. Secur..

[22]  Andrew Nguyen,et al.  ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD , 2023, International Research Journal of Modernization in Engineering Technology and Science.

[23]  Giuseppe Nuti,et al.  Algorithmic Trading , 2011, Computer.

[24]  Abhishek Singhal,et al.  Phishing URL detection by using artificial neural network with PSO , 2017, 2017 2nd International Conference on Telecommunication and Networks (TEL-NET).

[25]  B. Sarumathi,et al.  Impact of Artificial Intelligence in Business , 2013 .

[26]  Qingshan Liu,et al.  A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization , 2012, Neural Networks.

[27]  Sunil Erevelles,et al.  Big Data consumer analytics and the transformation of marketing , 2016 .

[28]  Mukesh Prasad,et al.  Detecting Alzheimer's Disease by Exploiting Linguistic Information from Nepali Transcript , 2020, ICONIP.

[29]  Michele Colajanni,et al.  On the effectiveness of machine and deep learning for cyber security , 2018, 2018 10th International Conference on Cyber Conflict (CyCon).