Introduction
When a financial institution client enters into a banking service agreement or takes out a loan, they provide a significant amount of personal data for processing – from name and passport details to information about real estate and marital status. Not always do they sign a consent to their processing. The degree of success of the banking business depends to a large extent on the ability of the institution to maintain confidentiality. The personal information of customers has a different confidentiality regime and different methods of legal protection, but the organizational and technical measures to protect both sets of data may be identical. Strict requirements encourage NAB to take a systematic approach to protect personal data, especially since its leakage poses risks to the business’s reputation and legal action related to compensation for damages.
Data Usability
Creating a system that meets the information needs of NAB requires an approach distinct from developing conventional file systems. To successfully implement a database-driven system, one must think first of the information and only then of the applications. The foremost benefit of using a database for stakeholders includes eliminating data redundancy by integrating files to avoid storing multiple copies of the same piece of information. Eliminating or controlling data redundancy reduces the risk of inconsistent states.
Moreover, the integrity of the database means the correctness of the information stored. Furthermore, the security system can be expressed in credentials and passwords to identify users. Access to the data on the part of the registered user may be limited to only some operations. For example, a database administrator may be granted access to all data in the database, a manager to information relevant to his department, and an ordinary employee to the data necessary for his job duties. In some cases, the cost of databases and additional software may not be high compared to converting existing applications to work with the latest system (Isaak and Hanna, 2018, p. 57). These costs can include the expense of training staff to operate the new applications and the price of the specialists who will assist with the conversion and startup. Another risk is that centralizing resources increases the vulnerability of the system. Since all users and applications depend on being up and running, a failure of one component can bring the entire organization to a halt.
Descriptive analysis is the first stage of the analytical part of the study, containing a numerical and graphical description of the data. The descriptive analysis includes the construction of frequency tables, which can be used to calculate the distribution of frequencies in the categories of the variable and determine the number of objects in each class. It can be operated as the calculation of various indicators and coefficients that characterize the distribution of frequencies of the values of the variable (Isaak and Hanna, 2018, p. 57). Methods of descriptive analysis allow for investigating data and choosing the way of their further in-depth analysis (methods of analytical statistics), for example, methods for testing statistical hypotheses and modeling relationships.
Thus, it will be possible to establish the cause of the incident and summarize the raw data to find a more individual approach to further analysis and systematization of information about each client. The predictive application of data is usually considered a forecasting method that explains what can happen in the future. The bank planned to introduce new control mechanisms to ensure the reliable safety of its client’s data. The predictive use of data will create a clear strategy for managing private information following ethical norms, respecting security, accuracy, and confidentiality.
Prescriptive data applications are most helpful in optimizing and modeling algorithms to advise them on every possible outcome. This prescriptive analysis will mainly focus on quantifying different software tools with the effect of future solutions to report all possible results with the data analysis tools used. Data analysis refers to the entire spectrum of organizing the collection, processing, and interpretation of information that will help clarify the marketing strategy, reinforce the weaknesses, and multiply the strengths (Phillips, 2018, p. 578). It will aid in creating knowledgeable guesses instead of depending on guesswork.
For this purpose, the most suitable tool is Excel because it is a program with enormous possibilities for analytics. Analysts will confirm that it is a versatile tool that can cope with various tasks: from small to big-data processing, using a plug-in. In particular, MS Excel Power Query is a universal instrument that allows importing (search, send) external data into Excel from sources available online or through corporate networks and then processing them (Phillips, 2018, 578). It can load data of diverse designs, structures, and types from various sources, which is specifically helpful in this case.
Data Security and Privacy
Banking has always been associated with processing and storing large amounts of confidential data. Therefore, risks associated with leaks of personal information come to the fore. First of all, to protect against personal data leaks, DLP systems can be used to monitor and analyze data sent outside the organization via corporate and webmail, the Internet, and instant messaging designs. These systems also authorize security officers to control sending files to printers and copying information to removable media (Qiu et al., 2019, 1298). The Bank’s processing of personal data must be lawful and fair and must be limited to specific, predetermined, and legitimate purposes. The Bank ensures that necessary measures are taken to delete or clarify incomplete or inaccurate personal data.
The reputation of customers should be the primary value for the institution. Thus, by valuing and guaranteeing the anonymity of the identity of its customers, the question of marketing becomes more complex. Therefore, the interest of marketers should shift towards direct communication with customers. It is necessary to figure out what the user is doing within the company’s app as quickly as before. In addition, marketers can work with the data that a person left voluntarily: when registering on the site, filling out service, or taking a survey. Customers can be offered to exchange data for bonuses from the company, such as answering a few questions about preferences in exchange for a promo code. They can likewise get a profitable card by attaching it to the phone number and SMS-mailing.
The procedure for collecting, storing, using, sharing, transferring, protecting, and destroying personal data is determined by data privacy laws. Any institution must comply with these laws wherever it does business. This information may not be shared with other persons or parties who do not have access rights and may only be used for business purposes as required by law (Qiu et al., 2019, p. 1298). Thus, data cannot be transmitted to providers interested in credit card history without customer consent. At the same time, this rule is not absolute. First of all, the Bank has to proceed in the direction of public interest and decide whether or not the disclosure of the credit card history is reasonable and corresponds to the principle of public safety. Therefore, if the information contains valuable facts, it can be disclosed in order to achieve the public good.
Ethical Considerations
In the era of complete access to large volumes of data, including personal data, the issue of ethical access to certain types of data and their use in specific fields of knowledge and technology has become relevant. The European and Australian concept of usage regulation is based on the idea that there should be no excessive control over an individual’s private life, either on the part of government institutions or corporations (Wachter and Mittelstadt, 2019, 494). If the introduction of new technologies or procedures for handling data has the potential to violate human rights, there is widespread public resistance.
The subject has the right to decide whether to turn over their data to an operator, but they give consent to processing when it is necessary. In cloud storage, however, neither the provider, the equipment manufacturer, nor the organization through whose cables the bits and bytes of information run nor any other third party have access to the data. No one can force the client to give up his login and password, so this method is safe. Often it is impossible to balance law and ethics, and one has only to apply the law (Wachter and Mittelstadt, 2019, p. 494). In any case, the operator must consider ethics as long as it is possible when approaching data protection and privacy. It is imperative to acquire an ethical consciousness because it can manage any activity properly.
Violations of ethical principles in artificial intelligence, such as data privacy or algorithm bias, have long been a global problem. Workers require relevant functional instruments to help them develop algorithms. In particular, AI templates provide sophisticated capabilities to people who are not engineers or computer scientists (Wachter and Mittelstadt, 2019, p. 494). The benefit of templates is that they expand the range and extent of application in diverse areas and permit administrators without a technical background to utilize AI and robotic process mechanization in federal agencies. At the same time, templates must be designed to promote ethics and fight bias so that algorithms make decisions impartially.
Artificial Intelligence
Two areas of ethical concerns for artificial intelligence can be identified. The first sphere includes moral norms, standards, and principles that regulate social relations connected with the development and application of artificial intelligence systems. The second area comprises more fundamental ideas about how such systems should be developed and used, taking into account their potential impact on the well-being and values of society (Wan et al., 2022, p. 14). At first glance, the ethical issues of personal data are of a purely applied nature. Still, a deeper look reveals that systems for collecting and analyzing a person’s explicit and implicit digital footprint can change many social practices, values, and even perceptions of the individual and society.
Conclusion
Despite the seriousness of the ethical problems associated with the processing of personal data, it is possible to overcome the crisis. With national ethics commissions and associations of developers lobbying for their economic or geopolitical decisions, the establishment of traditional international associations may become an incentive to develop unified approaches to understanding the regulation of artificial intelligence systems. There is no doubt that attempts to remove artificial intelligence technologies from strict legal codes will contribute to the violation of human rights. It is necessary to strengthen legal responsibility and accountability that produce innovations in this sphere.
Reference List
Isaak, J., and Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), pp. 56-59.
Phillips, M. (2018). International data-sharing norms: from the OECD to the General Data Protection Regulation (GDPR). Human genetics, 137(8), pp. 575-582.
Qiu, H. et al. (2019). A user-centric data protection method for cloud storage based on invertible DWT. IEEE Transactions on Cloud Computing, 9(4), pp. 1293-1304.
Wachter, S., and Mittelstadt, B. (2019). A right to reasonable inferences: re-thinking data protection law in the age of big data and AI. Colum. Bus. L. Rev., 45(2), p. 494.
Wan, Z. et al. (2022). Sociotechnical safeguards for genomic data privacy. Nature Reviews Genetics, 23(1), pp. 1-17.