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Pal, A.; Gopi, S.; Lee, K.M. Financial Technologies. Encyclopedia. Available online: https://encyclopedia.pub/entry/51483 (accessed on 15 November 2024).
Pal A, Gopi S, Lee KM. Financial Technologies. Encyclopedia. Available at: https://encyclopedia.pub/entry/51483. Accessed November 15, 2024.
Pal, Anagh, Shreya Gopi, Kwan Min Lee. "Financial Technologies" Encyclopedia, https://encyclopedia.pub/entry/51483 (accessed November 15, 2024).
Pal, A., Gopi, S., & Lee, K.M. (2023, November 13). Financial Technologies. In Encyclopedia. https://encyclopedia.pub/entry/51483
Pal, Anagh, et al. "Financial Technologies." Encyclopedia. Web. 13 November, 2023.
Financial Technologies
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This entry is adapted from the peer-reviewed paper 10.3390/electronics12153301

Financial technology (fintech) is an emerging field where novel technologies are used to improve the business operations or services offered by financial institutions and enterprises. Artificial intelligence (AI), blockchain, and cloud services have caused process disruption, while big data enables greater customer acquisition and retention. Together, these technologies have enhanced the use of interactive fintech agents in finance.  

financial technology fintech technology artificial intelligence computer blockchain big data machine learning cloud computing

1. Artificial Intelligence and Machine Learning

According to computer scientist and AI pioneer John McCarthy, “(AI) is the science and engineering of making intelligent machines, especially intelligent computer programs” [1] (p. 2). The concept of ML emerged from the idea that “programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort” [2] (p. 535). Hence, ML, a subcategory of AI [3], is used to make sense of data based on experience. Deep learning is a further subset of ML where computers learn using algorithms modeled on the human brain’s biological structure and functioning [4].
In recent times, Artificial Intelligence (AI) has become widespread across various technological domains, primarily because of its capability to carry out computations in distributed systems or the cloud [5]. These technologies are being put to a wide range of uses in various fields.
The advancement of AI and ML enables real-time analysis of multimedia streaming data, facilitating informed decision-making. Diverse sources generate vast amounts of valuable data, which AI and ML techniques efficiently process to extract meaningful insights. This empowers organizations to identify trends, anomalies, and critical events, optimizing processes and services [6].
Another application of AI is with electromyogram (EMG) signals, which are generated by the electrical activity of muscles and are widely used in applications such as prosthetics, rehabilitation, and human-computer interaction. Traditionally, hardware processing techniques have been employed to analyze and interpret EMG signals. However, with the advancements in AI and edge computing, intelligent embedded processing has emerged as a superior approach [7].
AI applications in marketing have revolutionized the ability to customize services and content on websites and apps, serving as a crucial initial step in driving personalized marketing campaigns and fostering meaningful consumer engagement. ML-powered AI chatbots play a vital role in this process by continuously improving and becoming smarter over time. These chatbots are vast, adaptable, and intelligent, enhancing user experiences with a more lifelike interaction [8].
Similarly, AI and ML have far-reaching implications for fintech. AI systems can process and analyze large amounts of financial data in a consistent and accurate manner that is not possible for humans [9]. AI-powered financial apps provide a greater range of tailor-made services and products at a lower cost by leveraging personal customer data [10]. AI and ML can be applied to data such as the client’s income, saving and spending habits, assets, and liabilities, and can give investment recommendations that match their needs [11] as well as more customized advice than traditional advisors offer [12]. AI and ML also power conversational interfaces, automatically providing relevant and increasingly more accurate information over time [13][14].
For example, Bank of America’s AI-driven virtual assistant, Erica, is used by millions of customers to answer basic banking questions. The chatbot is fed with customer data, including past financial history and location information. Applying ML and deep learning, Erica can provide tailor-made services [15]. The BlackRock Robo-Advisor 4.0 also uses AI and ML and can outperform human stock-pickers in the task of buying stocks whose estimated intrinsic value is higher than the market value [16].
While AI has a diverse range of applications, including fintech, AI technology faces the challenge of needing to be human-centered and placing human well-being at its core. This approach entails designing AI systems responsibly, respecting privacy, adhering to human-centered design principles, implementing appropriate governance and oversight, and ensuring that an AI system’s interactions with individuals consider and respect users’ cognitive capacities. By adopting such an approach, stakeholders can navigate the complexities of AI while prioritizing ethical considerations and harnessing the full potential of these technologies to benefit humanity [17]. Other challenges regarding the development of AI include security, privacy, energy consumption, morality, and ethics [18].

2. Big Data

Big data refers to massive data sets that are complex, varied, and fast-moving, requiring advanced management and analysis techniques. Big data analytics refers to a set of technologies and techniques used to find patterns and information from data sets that are substantially larger and more complex than usual data sets [19].
Big data helps banks provide improved services to their customers, boost their security systems, and gauge customer sentiments from social media data. For example, Q.ai, a robo-advisory app, uses AI and big data to provide customized portfolio recommendations and maximize returns on investments [20]. Banks also use big data analytics to study consumption patterns and customer behavior [21][22]. Similarly, health insurance companies use data from wearable technologies to provide superior customer service and product innovations [23], and some insurance companies track driving data to reward safe driving [24]. It is also possible to build a system that recommends buying, selling, or holding a stock at specific times of the day [25].

3. Cloud Computing

The U.S. National Institute of Standards and Technology (NIST) defines cloud computing as “ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [26] (p. 2). Cloud computing lets clients access their personal financial files through the internet from anywhere. It is used extensively in finance, especially by banks, to reduce hardware, software, and human resource costs.
Cloud computing improves cash flows for banks, allowing them to rapidly provide and scale up services [27] and adopt newer technologies effectively [28]. Coupled with big data analytics, cloud computing enables banks to provide customized services and sound financial advice services [29]. The service Temenos Banking Cloud, for example, allows banks to launch and scale banking services quickly and at a low cost [30].

4. Blockchain

A blockchain comprises data sets, each composed of data packages or blocks. A block constitutes multiple transactions. With each additional block, the blockchain is extended, and together denotes a full ledger of the transaction history. These blocks can be validated by the network using cryptography [31]. Thus, a blockchain is a decentralized, open ledger where anyone can transact or validate transactions.
The impact of blockchain on the financial industry is far-reaching, promising lower costs and improved security [32]. When one block is added to another, it is through a verified transaction; hence, attackers cannot tamper with it once registered [33]. Also, blockchain allows transactions to be automated based on mathematical rules that are self-enforced; hence, transactions are largely secure, free of errors and illegal practices, and do not require verification from a reliable third party [34].
One of the most prominent uses of blockchain in finance is cryptocurrency [35], one example of which is Bitcoin, launched in 2008. It established a peer-to-peer system of payments based on electronic transactions, enabling different entities to send payments to one another without a central authority [36]. There are several other cryptocurrencies like Ethereum, Litecoin, Dash, and Ripple, and the industry is worth hundreds of billions of dollars [37].

References

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  2. Samuel, A.L. Some Studies in Machine Learning Using the Game of Checkers. IBM J. Res. Dev. 1959, 3, 210–229.
  3. Schuld, M.; Sinayskiy, I.; Petruccione, F. An Introduction to Quantum Machine Learning. Contemp. Phys. 2015, 56, 172–185.
  4. Deng, L.; Yu, D. Deep Learning: Methods and Applications. Found. Trends® Signal Process. 2014, 7, 197–387.
  5. Zheng, Y.; Wen, X. The Application of Artificial Intelligence Technology in Cloud Computing Environment Resources. J. Web Eng. 2021, 20.
  6. Mansour, R.F.; Soto, C.; Soto-Díaz, R.; Escorcia Gutierrez, J.; Gupta, D.; Khanna, A. Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks. Int. J. Interact. Multimed. Artif. Intell. 2022, 7, 14.
  7. Proaño-Guevara, D.; Blanco Valencia, X.P.; Rosero-Montalvo, P.D.; Peluffo-Ordóñez, D.H. Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach. Int. J. Interact. Multimed. Artif. Intell. 2022, 7, 40.
  8. Haleem, A.; Javaid, M.; Asim Qadri, M.; Pratap Singh, R.; Suman, R. Artificial Intelligence (AI) Applications for Marketing: A Literature-Based Study. Int. J. Intell. Netw. 2022, 3, 119–132.
  9. Noreen, U.; Shafique, A.; Ahmed, Z.; Ashfaq, M. Banking 4.0: Artificial Intelligence (AI) in Banking Industry & Consumer’s Perspective. Sustainability 2023, 15, 3682.
  10. Kapsis, I. Artificial Intelligence in Financial Services: Systemic Implications and Regulatory Responses. Bank. Financ. Serv. Policy Rep. 2020, 39, 1–21.
  11. Phoon, K.; Koh, F. Robo-Advisors and Wealth Management. J. Altern. Investig. 2017, 20, 79–94.
  12. Koksal, I. How AI Is Expanding the Applications of Robo Advisory. Forbes. 18 April 2020. Available online: https://www.forbes.com/sites/ilkerkoksal/2020/04/18/how-ai-is-expanding-the-applications-of-robo-advisory/ (accessed on 20 May 2021).
  13. Schuetzler, R.M.; Grimes, G.M.; Scott Giboney, J. The Impact of Chatbot Conversational Skill on Engagement and Perceived Humanness. J. Manag. Inf. Syst. 2020, 37, 875–900.
  14. Mhlanga, D. Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. Int. J. Financ. Stud. 2020, 8, 45.
  15. Hwang, S.; Kim, J. Toward a Chatbot for Financial Sustainability. Sustainability 2021, 13, 3173.
  16. Tokic, D. BlackRock Robo-Advisor 4.0: When Artificial Intelligence Replaces Human Discretion. Strateg. Chang. 2018, 27, 285–290.
  17. Ozmen Garibay, O.; Winslow, B.; Andolina, S.; Antona, M.; Bodenschatz, A.; Coursaris, C.; Falco, G.; Fiore, S.M.; Garibay, I.; Grieman, K.; et al. Six Human-Centered Artificial Intelligence Grand Challenges. Int. J. Hum. Comput. Interact. 2023, 39, 391–437.
  18. Luan, H.; Geczy, P.; Lai, H.; Gobert, J.; Yang, S.J.H.; Ogata, H.; Baltes, J.; Guerra, R.; Li, P.; Tsai, C.-C. Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Front. Psychol. 2020, 11, 580820.
  19. Verma, J.P.; Agrawal, S. Big Data Analytics: Challenges and Applications for Text, Audio, Video, and Social Media Data, Video. IJSCAI 2016, 5, 41–51.
  20. Tenn, J. Robo Investing App. Yahoo Finance. Q.ai Launches Beta Version of Its AI-Powered. Available online: https://finance.yahoo.com/news/q-ai-launches-beta-version-142748487.html (accessed on 17 February 2021).
  21. Srivastava, U.; Gopalkrishnan, S. Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks. Procedia Comput. Sci. 2015, 50, 643–652.
  22. Aziz, N.A.; Long, F.; Wan Hussain, W.M.H. Examining the Effects of Big Data Analytics Capabilities on Firm Performance in the Malaysian Banking Sector. Int. J. Financ. Stud. 2023, 11, 23.
  23. Nayak, B.; Bhattacharyya, S.S.; Krishnamoorthy, B. Integrating Wearable Technology Products and Big Data Analytics in Business Strategy. J. Syst. Inf. Technol. 2019, 21, 255–275.
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  33. Rawat, D.B.; Njilla, L.; Kwiat, K.; Kamhoua, C. IShare: Blockchain-Based Privacy-Aware Multi-agent Information Sharing Games for Cybersecurity. In Proceedings of the International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA, 5–8 March 2018; Volume 2018.
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Subjects: Mathematics, Applied; Communication
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Anagh Pal
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Shreya Gopi
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Kwan Min Lee
View Times: 324
Revisions: 2 times (View History)
Update Date: 14 Nov 2023
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