Sample DATA4000 Introduction to Business Analytics Report 3A

DATA4000 Introduction to Business Analytics Report 3 Sample

Your Task

Consider below information regarding the National Australia Bank data breach. Read the case study carefully and using the resources listed, together with your own research, complete: Part A (Industry Report).

Assessment Description

Bank of Ireland
https://www.rte.ie/news/business/2022/0405/1290503-bank-of-ireland-fined-by-dpc/

Background

Bank of Ireland has been fined 463,000 by the Data Protection Commission for data breaches affecting more than 50,000 customers. It follows an inquiry into 22 personal data breach notifications that Bank of Ireland made to th Commission between 9 November 2018 and 27 June 2019. One of the data breach notifications affected 47,000 customers.

The breaches related to the corruption of information in the bank's data feed to the Central Credit Register (CCR), a centralised system that collects and securely stores information about loans. The incidents included unauthorised disclosures of customer personal data to the CCR and accidental alterations of customer personal data on the CCR”.

Brief

As an analyst within Bank of Ireland, you have been tasked with considering ways in which customer data can be used to further assist Bank of Ireland with its marketing campaigns. As a further task, you have been asked to consider how Bank of Ireland could potentially assist other vendors interested in the credit card history of its customers.

Assessment Instructions

Part A: Industry Report (1800 words, 25 marks) - Individual

Based on your own independent research, you are required to evaluate the implications of the European legislation such as GDPR on Bank of Ireland’s proposed analytics project and overall business model. Your report can be structured using the following headings:

Data Usability

- Benefits and costs of the database to its stakeholders.
- Descriptive, predictive and prescriptive applications of the data available and the data analytics software tools this would require.

Data Security and privacy

- Data security, privacy and accuracy issues associated with the use of the database in the way proposed in the brief.

Ethical Considerations

- The ethical considerations behind whether the customer has the option to opt in or opt out of having their data used and stored in the way proposed by the analytics brief

- Other ethical issues of gathering, maintaining and using the data in the way proposed above.

Artificial Intelligence

- How developments in AI intersects with data security, privacy and ethics, especially in light of your proposed analytics project.

It is a requirement to support each of the key points you make with references (both academic and “grey” material) Use the resources provided as well as your own research to assist with data collection and data privacy discussions.
https://gdpr-info.eu/

Solution

Part A: Industry Report

Introduction

The risk connected with the mortgages that the Bank of Ireland and other commercial organisations issue is managed via the application of data. For Assignment Help, Analysing the information they get about specific clients is how they accomplish things like client credit rating, payment card usage, balances owing on various payment cards, and balances owed on various kinds of credit (net loans capacity) can all be included in the dataset, although they are not the only ones. To determine a lender's creditworthiness or determine the hazard associated with loan issuing, credit security assessment is the study of past data (Shema 2019, p. 2). The research findings assist financial organisations and the Bank of Ireland in assessing both their own and their client's risks.

Data Usability

A person or group that might influence or be impacted by the information administration procedure is referred to as a participant in whatever data management program. The stakeholder database is used as more than just a device for public connections; it also acts as documentation for compliance and verification, a trustworthy source of data for future computer evaluations or studies, and fosters lengthy effectiveness. Stakeholder databases are essential, yet they are frequently underfunded, and numerous businesses continue to keep their data on unprotected worksheets (Campello, Gao, Qiu, & Zhang 2018, p 2). The average expense to design a database managing application is 24,000 dollars. Yet, the whole price ranges from 11,499 to 59,999 dollars. Any database administration application with fewer capabilities, or perhaps a Minimum viable product, would be less expensive than one that involves all of the anticipated functions.

Figure: Data usability
Source: (Hotz, et al, 2022)

An institution's daily activities regularly make utilization of descriptive data. Professional analyses that offer a historical overview of an institution's activities, such as stock, circulation, revenue, as well as income, all seem to be instances of descriptive data. Such reporting' material may be readily combined and utilized to provide operational glimpses of a company. Numerous phases in the descriptive analytical method may be made simpler by the use of corporate insight technologies including Power BI, Tableau, as well as Qlik.

Likelihoods are the foundation of predictive data analysis. Predictive modelling makes an effort to anticipate potential prospective results as well as the possibility of such occurrences utilizing a range of techniques, including data analysis, numerical modelling (arithmetical connections among factors to anticipate results), as well as optimization techniques for computer learning (categorization, stagnation, and grouping methods) (Lantz 2019, p 20). Among the best, most trustworthy, and most popular predictive analytic tools are IBM SPSS Statistical. It has existed for a while and provides a wide range of features, such as the SPSS modeller from the Statistics Framework for Behavioral Research.

Prescriptive data builds on the findings discovered via descriptive as well as predictive research by outlining the optimal potential plans of operation for a company. Because it is among the most difficult to complete and requires a high level of expertise in insights, this step of the corporate analytics method is hardly employed in regular corporate processes. Automating email is a clear example of prescriptive data in action. Marketers may send email content to each category of prospects separately by classifying prospects based on their goals, attitudes, and motivations. Email automation is the procedure in question.

Data Security and privacy

To safeguard database management systems from malicious intrusions and illegal usage, a broad variety of solutions are used in database security. Information security solutions are designed to defend from the abuse, loss, and intrusion of not just the data stored within the network but also the foundation for data management in general and any users (Asante et al. 2021, p 6). The term "database security" refers to a variety of techniques, methods, and technologies that provide confidentiality inside a database structure. Database security refers to a set of guidelines, strategies, and procedures that develop and maintain the database's security, confidentiality, and dependability. Because it is the area where breaches occur most frequently, openness is the most important component of data security.
Infringements might be caused by a variety of programming flaws, incorrect setups, or habits of abuse or negligence. Nearly half of documented data thefts still include poor credentials, credential exchange, unintentional data deletion or distortion, as well as other unwelcome human activities as their root reason. Database governance software ensures the confidentiality and security of data by ensuring that only permitted individuals get access to it and by executing permission tests when the entrance to private data is sought. One of the data breach reports involving Bank of Ireland involved 47,000 clients. The data flow from the bank to the National Credits Record, a unified platform that gathers and safely maintains data on mortgages, was compromised in the incidents. Unauthorized client private information exposures to the CCR and unintentional changes to client private information upon that CCR were among the instances.

Figure: Data Security and privacy
Source: (Yang, Xiong, & Ren, 2020)

According to Shaik, Shaik, Mohammad, & Alomari (2018), the safeguarding of content that is kept in databases is referred to as database integrity. Businesses often maintain a variety of data within the system. They must employ safety methods like encrypted networks, antivirus software, safety encrypting, etcetera, to protect that crucial data. The safety of the system itself as well as the moral and regulatory ramifications of whatever information must be put upon that database in the first position were the two key concerns concerning database confidentiality. Additionally, the ethical obligation imposed on database protection experts to protect a database management structure must be taken into account.

Data consistency, which acts as the primary yardstick for information quality, is defined as data consistency with reality. The proper information must match the data that is required since more conformity converts into higher dependability. It suggests that the information is accurate, without mistakes, and from a reliable and consistent source. Since inaccurate data leads to inaccurate projections, data integrity is essential. If the anticipated outcomes are inaccurate, time, money, and assets are wasted. Accurate information enhances decision-making confidence, increases productivity and advertising, and reduces costs.

Ethical Considerations

According to Tsang (2019), conversations regarding how firms manage consumer data typically revolve around regulatory issues, such as risks and constraints. With good reason: the massive private data collections made by businesses and government agencies entail severe consequences and the potential for harm. In a way, more current security regulations, including the General Data Protection Regulations (GDPR) of the European Union and the Consumers Privacy Act of California (CCPA), prohibit usage attempts to regain the user's power.

The best way for a business to convince consumers to give their consent for the collection and use of their private details is to use that data to the customer's benefit. Letting users understand what data companies gather about them and the ways it's used in company services or offerings. Every business with clients or users is providing a valued offering or service. The worth is sometimes rather clear-cut. Users of location tracking, for example, are likely aware that these apps must track user locations to show the participant's true location, alter turn-by-turn directions, or provide actual-time traffic data. Most users agree that utilizing up-to-date mapping information offers benefits over employing monitoring programs that can keep track of their locations (Trivedi, & Vasisht, 2020, p 77). In similar circumstances, businesses would have to convince clients of the benefit of their information consumption to win their support. Users are conscious of the barter as well as, in some cases, are willing to permit the utilization of personal data if it is used by a company to improve the value of its services, promote research and development, improve stock control, or for any other legitimate purpose. When businesses give clients a compelling cause to express their support, everyone wins. This requires gaining the client's trust through both behaviour and information.

Companies have an ethical responsibility to their customers to only collect the necessary material, to secure that information effectively, limit its dissemination, and also to correct any errors in relevant data. Employees have a moral duty to hold off on glancing at customer records or files until it is essential, to hold off on giving customer data to competitors, and to hold off on giving user details to friends or relatives. Customers who share data with companies they do business with also have an ethical responsibility in this respect (Kim, Yin, & Lee 2020, p 2). Such compliance might comprise providing accurate and complete data as needed as well as abiding by the prohibition on disclosing or using company data that individuals may have access to.

Artificial Intelligence

With the advent of technical advancement, multiple new and updated machines are used in several sectors across the globe. Financial sectors are one of the most growing and continuously changing sectors which requires an in-depth analysis of its internal changing faculties that takes place rapidly. According to Kaur, Sahdev, Sharma, & Siddiqui, (2020), the role of Artificial intelligence is enormous in securing the growth and development of the financial sectors. The Bank of Ireland has been providing satisfactory customer services for years. However, in recent times, some difficulties are generated in banking services due to questions regarding protecting the data of the customers and restricting the bank authority from any kind of malpractice of the data. In this regard, the role of artificial intelligence is crucial to bring a massive transformation in the data safety and security process and win the hearts of customers. Artificial intelligence works for enhancing cybersecurity and protecting the bank from money laundering (Manser Payne, Dahl, & Peltier 2021, p. 15(2). In recent times, a large number of banks are now focusing on the implementation of Artificial intelligence to ensure the safety and security of their data of customers. However, now the areas which require more emphasis are understanding how artificial intelligence works for protecting data and what steps can be implemented to harness the safety of data.

Artificial intelligence generally helps in future predictions based on previous activities of the customers and is significantly able to differentiate between the more important and least important data. With the help of cognitive process automation, multiple features can be enabled most appropriately. According to Manser Payne, Peltier, & Barger (2021), scecuring ROI reduces the cost and ensures the quick processes of services at each step of bank services. In the finance sector, it is important to have a quick review of the financial activities of the customers. For human labour, it is quite a tough task. To make the procedure easy and harness the financial activities of banks takes help from the inbuilt automation process and robot automation process which denotes a high level of accuracy, lesser human-made errors, use of the cognitive system for making decisions and deviating valuable time to the optimum success of the financial sectors (Flavián, Pérez-Rueda, Belanche, & Casaló 2022, p. 7).

Figure: Use of AI in banks
Source: (Shambira, 2020)

The Bank of Ireland uses cloud storage to keep the data of the customers safe and protected. The prime goal of using AI in banks is to make the financial activities of the bank more efficient and customer driven. Address the issues more efficiently and adopt new methods to attract more customers. The Bank of Ireland is one of the most prominent banks in the country and they have to handle a wide range of data. Using optimum levels of AI technologies will help to bring more efficiency to the banking system.

Conclusion

To conclude, it can be stated that the Bank of Ireland has been providing services for many years and since the inception of the bank its prime duty is to provide safe and secure services to its customers. With the increasing pressure on customers and raising questions about data protection, the banking sectors are now focusing on utilising Artificial intelligence in banks which can provide maximum safety to the data of the customers and increase the number of customers.

Reference