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Introduction

Data analytics is defined as the science to analyse data for making relevant conclusions related to information. It assists organizations in the optimization of their performance, for performing more efficiently so that overall profit can be maximized for the formulation of strategically-guided decisions (Rikhardsson and Yigitbasioglu, 2018). The report is based on the 2 case scenarios for addressing the critical aspects of ethics and data analytics.

Task 1

a. Decisions are Taken via the IT Manager and CTO

Information and data are key concerns in the present scenario that influences the operations of the organization. It has become important for protecting the data from any sort of leaking or breaching. Chelsea has provided the organization with potential security options for addressing the requirements of the sensitive data that comprises employee salaries, customer information, and credit card details. The data provided by the consumers is the responsibility of the firm and it is protected from any sort of malicious activities. It is determined that there has been a relevant incline within the cases of security attacks, and data breaches through which information of the stakeholders is being accessed by the intruders. Open access and less secured information might enable hackers to easily get access to the information and capture the sensitive information. It becomes important for the firm to maintain and update the system of the firms so that the malicious activities of the intruders can be prevented.

The decisions made by the IT manager and CTO are wrong as they will probably lead to vulnerabilities which will further incline the risk of attacks on the organizational systems. Here, it has to be considered that Chelsea is operating various operations for distinct companies and they are aware of the sensitivity of the information and the impact that will be created through the usage of low-level security (Aydiner et al., 2019). Such kinds of systems lead to creation of the vulnerabilities that in turn will hamper the employees and consumers of the firm. The ACS must consider the interest of their customers based on the Code of Ethics. Chelsea has advised them to make use of the secured system through which they can preserve the information of the organization. The less secure systems do not prevent the firm from attacks and different vulnerabilities can be exploited by intruders for gaining any sort of benefits. The public interest is considered on priority rather than that of the individual and if any dispute occurs then it must be handled based on the public interest. Public environment, health and safety are regarded as the key concerns of public interest. The decisions that are being made by the IT manager and CTO will lead to a decline in the confidence of the customers as they can be apprehensive about the fact that personal data might be accessed by unauthorized individuals. This will lead the firm to have revenue loss and reputational harm. Further, Chelsea will be considered accountable for the data breaches that might take place within the firm because of the decisions that have been made. Under the code, priority is given to the public interest above sectional, individual, and private interest and if any dispute will occur then it will be settled based on the public interest. The reduction and recognition of the negative influences will be assisted via the ethical attitude that is taken for the completion of the job (Ashrafi et al., 2019). The decision taken is inadequate and inappropriate for the Primacy of Public Interest. Another aspect that is breached here is honesty as people have shown their trust in the firm for giving their personal and sensitive information. The wrong decisions taken by the IT manager and CTO illustrate that the information is not secured as a low-level security option is considered by the organization.

b. Implementation of the Proposed Solution

The proposed solution must be executed by Chelsea, though the solution given is low-level it will assist a firm in addressing the low-level attacks. The information that is stored on the organizational system of employees and users might be breached, hacked or leaked. The information requires a higher level of security but the solution that is being considered by the IT manager and CTO does not consider the higher level of security solution. The proposal has accepted the usage of the low-level security needs that have to be executed for having security instead of nothing. The probable risks of the hacking and breach are high which implies that the data stored with the firm is not secured. The data comprises crucial and sensitive data related to the credit cards of customers and the annual performance of the employees. The security system is examined by Chelsea and it is identified that there are different vulnerabilities present in the system that might lead to cyberattacks. The dishonesty of the firm regarding the security measures can lead to organizational downfall as they have not considered security as an essential aspect (Torres, Sidorova and Jones, 2018). Chelsea must execute the solution for the system and database from leaking or data breaching.

The suggestion can be given by Chelsea that though low-level security measures are used still they can make use of data monitoring, encryption, and firewall for prevention of the attacks. The execution of these aspects will level up the security measures of the organization and will assist in eradicating the vulnerabilities present within the system. The solution will potentially not prevent the hacking and other probable attacks that can be made on the system rather the solution will be provided for prevention of the in-office activities, Chelsea has to agree to the execution of the solution given by the CTO and IT manager so that basic security levels can be provided for reduction of the potential risks that might be made on the system and database.

c. K-anonymity for the Dataset

K-anonymity is defined as the data anonymization technique that is utilised for protection of the individual privacy within the dataset that involves PII generalization, pseudonymization or masking. It is utilized for protecting the privacy of individuals within the given dataset for ensuring that not even a single individual is determined. In this case, the k-anonymity involves a value of k equal to 5 as the table is having 5 rows for all the combinations. The value is attained through the generalization of the quasi-identifier attributes (Lepenioti et al., 2020). Here, the income table comprises sensitive information about the employee that has to be hidden along with the pair of postcodes that should also be hidden from the table illustrated on the database.

The combination will enable to furnish of the exact information regarding the zip code and identity that can be combined with the other details for evaluating the exact identity of the individual. It is the sensitive information of the individual that must be hidden for attaining the relevant security levels. Furthermore, the employee’s income is another critical concern that must be hidden under the given scenario. The short table for the anonymized data for attaining K is presented below:

Id

Age

Postcode

Sex

Income

1

20-25

*

Male

*

2

20-25

*

Male

*

3

40-45

*

Male

*

4

30-35

*

Female

*

5

20-25

*

Male

*

6

30-35

*

Female

*

7

30-35

*

Female

*

8

40-45

*

Male

*

9

40-45

*

Male

*

10

20-25

*

Male

*

11

30-35

*

Female

*

12

40-45

*

Male

*

13

20-25

*

Male

*

14

40-45

*

Male

*

15

30-35

*

Female

*

When K-anonymization will be utilized adequately then it is a potent tool for contractual safeguard and access control. Additionally, through the other methods that involve differentially private algorithms, it has a critical role within privacy enhancement technology (Müller, Fay and Vom Brocke, 2018). The public datasets can be utilized for supporting the re-identification attempts that might appear as an initial start for data security.

Task 2

a. Ethical Benefits From Banks With the Usage of the Big-data-driven System

The applications of big data analytics will assist in the analysis of consumer behaviour for identification of the financial, and personal backgrounds along with the investment pattern. Through the creation of an understanding of these factors, banks can formulate a relationship with their potential consumers depending on understanding and trust (Low and Johnson, 2014). The BI (business intelligence) tools are utilized widely for having leverage from the big-data system for identification of the probable risks within the loan sanctioning. Here, market trends will be evaluated depending on the interest rates, and regional data might incline or decline in the segment. The study is liable for optimizing the usage of the tools to analyse the performance of the bank via studying trends present within the credit risk. The improvisations within the analytical techniques have led to the development of efficient statistical tools for risk management.

In data ethics, there is easy detection of the errors and frauds that will be determined through seeking assistance from the big data within the real-time analysis (Mikalef et al., 2018). Furthermore, nearly 68% of bank employees are anxious to address regulatory compliance within the banking sector. The insight will be furnished into the data through which big data can be executed within the banking sector. The loan applications will be validated after addressing the regulatory compliance criteria through the analytical dashboard. It will enable to have data-driven proactive approach to the management of risks. It is determined through the automation of the processes and it will decline the compliance failures. The core asset that can be attained through the usage of the big data-driven system is assessing the loan applications that will enable to have accurate and rapid choices (Power et al., 2018). It will assist in the reduction of the cost related to borrowing for all.

b. Ethical Harms Due to Loan Denial

Hannah and Josh might have experienced certain damages as the impact of the loan refusal, which comprises possibilities that have been turned down for the loan that they might have afforded and the possibilities that it might have been rejected due to factors such as unrelated capacities for paying back the loan. Further, the algorithm might formulate the judgement call without considering the unique situation. They are experiencing reputational injury, emotional pain, and financial difficulties as a consequence of the loan refusal. These are regarded as morally essential damages and the financial difficulties might cause ample damages that might result in other issues, which comprises homelessness, mental health, and debt. Another critical impact that is being caused is damage to the reputation that might make it hard for them to attain loans in future (ACS, 2023). Emotional distress is relevant harm as it might lead to depression, anxiety, and other mental health issues. On a contrary note, the actual reason for the loan denial is not known which will cause a mental breakdown between them and even lead to the violation of governmental norms. The issue is that their life will be at risk because of the black web as well as involvement of the third parties, which might ruin their life completely. There is harm to transparency, and autonomy so that they can be fair for everyone, and consumers possess right so that they can be treated fairly (Conboy et al., 2020). There is a hidden bias, arbitrariness, and avoidable errors that might lead to common harm. There is a question of accessibility of confidential and personal information that is critical.

c. Harms Due to the Loan Evaluation Process

There is widespread usage of loan assessment procedures that may have a wide range of negative consequences for society comprising the possibility of denial of loans to those who cannot repay them. The possibility of the denial of loans depends on the information that is unrelated to the capacity of the person for the person for paying the loan back. Further, the machine might decide it without considering the unique situations of the user. There is inclined usage of the loan appraisal procedures that leads to the creation of a negative impact on society as a whole, which comprises denial of credit for particular groups, loss of private rights, and maintaining socioeconomic injustices. It might result in social marginalisation, & financial hardship, and denial of certain access to the groups for the credit causes that lead to enormous damage (Giebe, Hammerström and Zwerenz, 2019). The continuation of the social inequalities causes relevant harm as it will further cause exclusion and discrimination. The erosion of privacy rights leads to relevant harm as it can cause the loss of control of personal data. There is the minimization of the ability to formulation informed decisions related to its usage.

The bank lags in considering the regulatory framework and ethical norms that might cause negative intervention within the social systems and institutional sectors. It also causes economic insecurities, unfair practices, and a lack of efficient competition within the financial marketplace. Here, the ethical problems experienced by the banks need to be considered as all have the right to be treated fairly.

d. Effective Measures for Preventing Harms

It is essential to make sure that the information is used efficiently by the system and that the individual conditions are considered for preventing the impact that occurs because of the execution of the loan assessment methods. The appeals can be made through the system. For avoiding damages, the preventive measures that can be taken are mentioned below:

Autonomy, transparency, and trustworthiness: The software company and the bank authorities can amplify the relationship aspects by emphasising the factors of trustworthiness, autonomy, and transparency value. The reduction within the opt-out options and considering the option of opt-in will enable them within having clear choices for the avenues to incline the autonomy and transparency values that further incline the trust of the loan officers (Garg et al., 2021). BI tools assist in formulation of the regulatory compliance along with the software package for offering inclined consumer values and sustaining public trust.

Establishment of the ethical responsibilities and accountabilities chains: According to the ethical data, there is a need for a set of practices for the development of the ethical responsibilities chain considering the high responsibility levels. There is evolution within the plan that has to be implemented by looking into the suggestions attained through the big-data experts that will enable to have analytical data execution ideologies.

Designing privacy and security: Inadequate privacy and data security might lead to the breach of confidential information. It takes into consideration the poor data security that further leads to the exposure of confidential information that has to be avoided (Reddy et al., 2020).

Conclusion

From the above, it can be concluded that there are potential challenges within the execution of the measures, such as the bank's resistance to changing their systems due to the lack of effective regulations. They might experience compliance issues and ethical concerns while dealing with customers. It is important to treat the people fairly so that transparency and accountability of the operations can be maintained.

References

Books and Journals

ACS (2023). ACS Code of Ethics. https://www.acs.org.au/content/dam/acs/acs-documents/Code-of-Ethics.pdf

Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management , 47 , 1-15.

Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of business research , 96 , 228-237.

Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research , 281 (3), 656-672.

Garg, P., Gupta, B., Chauhan, A. K., Sivarajah, U., Gupta, S., & Modgil, S. (2021). Measuring the perceived benefits of implementing blockchain technology in the banking sector. Technological Forecasting and Social Change , 163 , 120407.

Giebe, C., Hammerström, L., & Zwerenz, D. (2019). Big data & analytics as a sustainable customer loyalty instrument in banking and finance.

Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management , 50 , 57-70.

Low, G., & Johnson, M. (2014). ACS Code of Professional Conduct Professional Standards Board Australian Computer Society. https://www.acs.org.au/content/dam/acs/rulesand-regulations/Code-of-Professional-Conduct_v2.1.pdf

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management , 16 , 547-578.

Müller, O., Fay, M., & Vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems , 35 (2), 488-509.

Power, D. J., Heavin, C., McDermott, J., & Daly, M. (2018). Defining business analytics: An empirical approach. Journal of Business Analytics , 1 (1), 40-53.

Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of dimensionality reduction techniques on big data. Ieee Access , 8 , 54776-54788.

Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems , 29 , 37-58.

Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information & Management , 55 (7), 822-839.

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