Artificial Intelligence in Social Networks for Retail

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Table of Contents

Background Information on The Problem
Perspectives From Multiple Populations
Argument Supporting Proposed Solutions
Evidence From Scholarly Sources
Ethical Outcomes
Conclusion
References

The development of AI technologies in various sectors of the economy is primarily related to the level of their digitalization. The use of social medial for retail is one of the most mature sectors of the economy in terms of the use of AI. It is characterized by access to unlimited data, a significant investment by such e-commerce giants as Amazon and Alibaba, and a clear focus on increasing sales. Due to the ability to collect a large amount of data from various sources, AI poses a significant threat of data leakage and cyber fraud. Improving the cybersecurity when using social medial for retail by detecting and preventing any fraudulent activity will allow to protect confidential data when using AI.

Background Information on The Problem

Cybercrime in use of social medial for retail is a consequence of the globalization of information and communication technologies and the emergence of international computer networks. According to Hu et al. (2020), unlike other types of economic crime, cybercrime is currently the fastest-growing segment. Any information and technical innovations significantly expand the scope of cybercrime in trade and create conditions for increasing the effectiveness of hacker attacks. Therefore, cybercrime is growing at a faster rate than all other types of crime.

Perspectives From Multiple Populations

The societal problem of cybersecurity in trade affects all parts of society: from sellers to intermediaries and buyers. With the development of the Internet and the convenience of providing trading services to customers, online purchases have become widely used; they are carried out using remote banking technologies. To get hold of confidential user data, fraudsters have come up with a method called fishing, which implies creating a site that is outwardly indistinguishable from the accurate bank site.

Thus, buyers in the field of online commerce suffer from the leakage of confidential data. Sellers themselves also become victims of the problem of cybersecurity violations. For example, according to Mahmoud et al. (2020), third parties steal money and confidential data using virus software or by recording conversations using microphones. Remote access programs help fraudsters to steal from the account of trading enterprises. Intermediaries in retail also suffer from fraud; when placing ads for sale on social media, they often receive calls from scammers who present themselves as interested buyers.

Argument Supporting Proposed Solutions

Strengthening the cybersecurity of enterprises will make them less vulnerable to hacking. Therefore, buyers, clicking on the social medial link for payment, will be sure that they are transferring money to an honest seller and not to scammers (Thakor, 2019). Taking care of their reputation, e-commerce enterprises who use social medial for retail should take the security and reliability of their customers ‘ applications seriously. The use of original programs for the cyber protection of official websites will help protect customers from possible fraud.

Strengthening cybersecurity at the enterprise will also protect entrepreneurs from unauthorized debiting of funds from bank cards or accounts of legal entities. It will provide a general guarantee of the safety of payments made through non-bank payment transfer systems. The cybersecurity measures taken will be able to make electronic transfers within the trading company more secure (Donepudi, 2018). The implementation of protection mechanisms will also allow sellers to protect the process of sending, forming evidence of shipping, and receiving documents. Strengthening Internet security can also help make the conclusion of transactions safer for intermediaries. When regularly working with trading in social networks, it is necessary to equip operating equipment with media protection, and network screens. This will reduce the increased volume of fishing and data leakage caused by recording a conversation through a microphone.

Evidence From Scholarly Sources

The first scholarly source that proves the connection between the strengthening of cybersecurity and using social medial for retail is “Shopping intention at AI-powered automated retail stores.” This study examines the implementation of online purchases from the point of view of the buyer. The authors state that the self-isolation regime has led to an unprecedented boom in online trading. They advise people who often make purchases on the Internet to secure their device by installing a high-quality antivirus.

According to Pillai et al. (2020), months of quarantine have brought at least 10 million new customers to e-commerce in the United States. In addition, the article claims that 44 % of all cybercrimes account are for the theft of money from credit cards and only 16% are for the theft of classified information. Such statistics indicate the need to protect people who often make online purchases.

The article “Shopping intention at AI-powered automated retail stores” cannot be considered sufficiently reliable. In reality, the number of victims of Internet scammers is much higher since only data on known fraud cases are used in the study’s statistics. Most people who become victims prefer not to contact law enforcement agencies, and the study does not consider issues of fraud that are not made legally public.

The strength of the study is the presence of a predictive function in this comparative study; it demonstrates the trend of cybercrime to a steady increase. The article shows that in current conditions, combating crimes in the field of computer information is acute. For this reason, there is a need to apply and develop programs that will protect the average user of online stores.

Among the weaknesses of the study, the lack of completeness of statistical data can be singled out. High-quality initial statistical information is necessary for an adequate analysis of the current situation and the development of a forecast of the socio-economic development of cyberbullying. Unfortunately, it is impossible to collect accurate statistics on this issue, so the presented data can be considered as close to reality as possible.

The next scholarly source “State of the art and adoption of artificial intelligence in retailing” addresses such a buyer’s security problem as the use of search queries and information received through the phone microphone for personal purposes. The primary way to access conversations or search data is to install a program that will access the microphone and text on the screen. This data can be used not only by official systems (for example, Google) to optimize advertising but also by scammers for selfish purposes.

The article’s data provides statistics that only 0.2% of all records are transferred to Google contractors, and there is no identifying information in the audio files themselves (Weber & Schütte, 2019). However, the company admitted that its contractors could listen to recordings of users’ communication with Google Assistant. It is mentioned that there are more than a thousand phrases encrypted in TV series and news that lead to the activation of voice assistants.

Thus, the buyer’s personal information can get to third parties through the microphone and search queries. The article can be reliable, as it contains links to interviews with developers of such significant technologies as Alexa, Google Home, Microsoft Cortana, and Siri. However, the journal in which the article is published is sponsored by a Chinese competitor of the listed technologies; for this reason, the study might be a little biased.

The main advantage of the article “State of the art and adoption of artificial intelligence in retailing” is its informative content. Many technical devices belonging to different manufacturers are mentioned, and the volume and type of personal information they receive about customers. For example, data on customer data collection using smart speakers, televisions, voice assistants, microphones, and even robot vacuum cleaners are presented. The weak side of the study “State of the art and adoption of artificial intelligence in retailing” is its possible bias. Interviews with technical specialists of companies are given as statistics. However, the article does not contain official information provided by the heads of the companies mentioned above.

The third scholarly source, whose authors also mention security when using social medial for retail, is the “Design of smart unstaffed retail shop based on IoT and artificial intelligence”. The authors insist on the mandatory set of measures to protect the data of a trading enterprise from malware and hacker attacks. From the authors’ point of view, a competent approach to cybersecurity involves the multi-level protection of software, networks, databases, and PCs.

According to Xu et al. (2020), in 2019, the retail sphere was the second most popular among hackers: according to estimates, 24% of attacks were directed at it. Further, as an information statistic, the article provides a percentage list of information security risks of trading enterprises. The main cyber threats to trading companies are espionage and financial losses. So, in 2019, the motive for most attacks (84% of cases) was to obtain data, and 36% of hackers were interested in financial benefits.

The study is a valuable work since the collected statistics of information security risks in commercial enterprises is an essential contribution to the fight against cyber fraud. The data presented in the study make priceless contribution to solving the main task facing information security specialists. They help assess the feasibility of various information security risks in the company and identify possible consequences of cyber-attacks to build an effective protection system based on this knowledge.

The strength of the study “Design of smart unstaffed retail shop based on IoT and artificial intelligence” is the practicality of the data provided. Given the deep penetration of high technologies into the industrial segment, many potential attack vectors have been created. There are different ways of protection against various types of fraud. For example, the protection of the data that can be obtained through the phone microphone is different from protecting the one that can be revealed through tracking the search query. Therefore, the statistics given in the article can be used for an essential specification of the process. The disadvantage of the article is the difficulty of applying the proposed methods of combating cybercrime. The actions in the infrastructure offered by the authors are theoretically able to affect the technological process negatively. In this regard, it seems unlikely that the management will take the announced measures to strengthen security.

Ethical Outcomes

The positive ethical outcome of my decision will be the protection of confidential information from unauthorized use. Retail employees who work with personal data about clients due to their professional duties will not be able to make it public (Raghavan & Pai, 2021). The circle of people who will work with restricted access information will be clearly defined. The reverse side of such a solution to the problem may be a violation of employees’ boundaries. Monitoring employees ‘ working hours on a work computer can turn into interference in their privacy. For example, the personal life of a social media freelancer outside of working hours can be violated (Güven & Şimşir, 2020). In addition, if the manager wants to save money and contact an unreliable company, employees can become victims of voyeurs.

In case of a positive outcome, a big step is taken in solving the ethical problem of personal data protection. According to Oosthuizen et al. (2020), the confidentiality of personal information about lifestyle, health, finances, and contacts are still perceived by Internet users as a value. Therefore, by installing cybersecurity programs on retail and social media platforms, customers will be guaranteed the importance of privacy, which is a legitimate human right of a democratic society. Nevertheless, modern society and employers are becoming more and more open and tolerant to the gradual expansion of the framework of moral acceptability and legal permissibility. Under the pretext of cybersecurity, unscrupulous employers will be able to control their employees with the help of special programs and obtain data about the private life of employees.

Conclusion

With the development of Industry 4.0, information security is becoming as important a component of a digital trading. The urgency and acuteness of privacy are associated with the rapid development of information and communication digital technologies and the controversial legal regulation of this area. Accordingly, in any projects using social medial for retail, the minimum necessary list of information security systems should be used. It must be provided with the forces of specialists who can maintain and administer it and promptly detect and counteract cyber-attacks.

References

Donepudi, P. K. (2018). AI and machine learning in retail pharmacy: Systematic review of related literature. ABC Journal of Advanced Research, 7(2), 109-112.

Güven, I., & Şimşir, F. (2020). Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods. Computers & Industrial Engineering, 147(4), 180-185.

Hu, H., Zhou, N. Q., Wang, X., & Liu, W. (2020). DiffNet: A learning to compare deep network for product recognition. Access IEEE, 8(27), 19336-19344.

Mahmoud, A. B., Tehseen, T., & Fuxman, L. (2020). The dark side of artificial intelligence in retail services innovation. In E. Pantano (Ed.), Retail futures: The good, the bad and the ugly of the digital transformation (pp. 165-180). England, Bingley: Emerald Publishing Limited.

Oosthuizen, K., Botha, E., Robertson, J., & Montecchi, M. (2020). Artificial intelligence in retail: The AI-enabled value chain. Australasian Marketing Journal, 29(2), 144-154.

Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores. AIPARS Journal of Retailing and Consumer Services, 51(7), 28-34.

Raghavan, S., & Pai, R. (2021). Changing paradigm of consumer experience through Martech – A case study on Indian online retail industry. International Journal of Case Studies in Business, IT and Education, 5(1), 186-199.

Thakor, S. D. (2019). Effect of artificial intelligence (AI) on retail business. International Journal of Research in all Subjects in Multi Languages, 7(2), 72-74.

Weber, F. D., & Schütte, R. (2019). State of the art and adoption of artificial intelligence in retailing. Digital Policy, Regulation and Governance, 21(3), 23-28.

Xu, J., Hu, Z., Zou, Z. J., Hu, X., Liu, L., & Zheng, L. (2020). Design of smart unstaffed retail shop based on IoT and artificial intelligence. Access IEEE, 8(13), 147728-147737.

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