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Abanca Data Analytics Project Report
  • 2

  • Course Code: DATA4000
  • University: Kaplan Business School
  • Country: Australia

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) 

•    Part B (Application for data access presentation) 
 

Assessment Description

https://www.bankingsupervision.europa.eu/press/pr/date/2022/html/ssm.pr221216_1~4742bce1b3.en.html

ECB sanctions ABANCA for failing to report cyber incident within deadline

The European Central Bank (ECB) has imposed an administrative penalty of €3,145,000 on ABANCA Corporación Bancaria, S.A. (ABANCA) after it knowingly failed to report a significant cyber incident to the ECB within the prescribed two-hour deadline outlined in the cyber-incident reporting framework implemented in 2017.

In February 2019 ABANCA became the target of a cyber-attack when its IT systems were infected with malicious software. ABANCA responded by temporarily suspending internet and mobile banking services, ATM services and SWIFT payment services, among other measures.

Despite being aware of its reporting obligation and the significance of the cyber incident as early as 26 February 2019, the bank submitted the required report on the incident 46 hours after the prescribed deadline.

The bank’s omission hindered the ECB’s ability to properly assess ABANCA’s prudential situation and to react in a timely manner to potential threats to other banks, what could have had potential consequences on the reputation and the stability of the banking sector as a whole.

The entity promptly addressed the effects of the cyber-incident at the time it occurred. The ECB notes that the penalty relates solely to the breach of a reporting obligation in February 2019 and does not entail any assessment of the soundness of the bank’s existing IT systems.

Brief

As an analyst within ABANCA, you have been tasked with considering ways in which customer data can be used to further assist ABANCA with its marketing campaigns.

As a further task, you have been asked to consider how ABANCA could potentially assist other vendors interested in the credit card history of its customers. 

Assessment Instructions

Part A: Industry Report  - Individual

Based on your own independent research, you are required to evaluate the implications of the European legislation such as GDPR on ABANCA’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/

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Executive Summary

Important findings from ABANCA's data analytics project will help the bank use customer data for marketing campaigns. Data usability is key to customer profiling, targeted marketing, and personalised recommendations. However, data collection, storage, and analysis tools cost money.

Data privacy and security are paramount. Data security for ABANCA requires encryption, access control, and GDPR compliance. Maintaining customer trust requires ethical considerations like consent, fairness, and transparency.

AI improves data security and privacy with real-time threat detection, data anonymization, and compliance support. But it raises ethical questions about transparency, bias mitigation, and fairness. ABANCA must balance customer data benefits with data security, privacy, and ethics to achieve data-driven excellence. This balance is essential for trust, regulatory compliance, and project success.

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1. Introduction

ABANCA, a major financial institution, is exploring ways to use customer data for marketing and help external vendors access credit card history data. European laws, particularly the GDPR, shape ABANCA's business environment.

This legislation restricts personal data collection, storage, and use, threatening the data analytics project and ABANCA's business model.

This industry report examines GDPR's complex effects on ABANCA's analytics venture. The feasibility and benefits of using customer data, data security and privacy, ethical issues surrounding data usage consent, and the role of artificial intelligence are examined.

ABANCA must understand the regulatory landscape to balance data-driven innovation and European data protection laws.

2. Data Usability

2.1 Benefits and Costs

ABANCA benefits from using customer data for marketing campaigns but pays a price.

Benefits:

  • Better Customer Profiling: Customer data reveals preferences, behaviours, and demographics (Anshari et al., 2019). ABANCA can create more accurate customer profiles for targeted marketing that resonates with specific segments, improving campaign effectiveness.

  • Targeted Marketing: Predictive analytics determine market opportunities and threats using customer data (Bharadiya, 2023). ABANCA can optimise marketing for specific customer segments to increase conversion rates and reduce costs.

  • Personalised Recommendations: Prescriptive analytics uses past behaviour and preferences to make recommendations (Frazzetto et al., 2019). This improves customer satisfaction, loyalty, and cross-selling/upselling.

Costs:

  • Data collection: Maintaining customer data requires resources. GDPR compliance may require investments in data acquisition, integration, and cleansing tools (Brandy, 2023).

  • Data Storage and Maintenance: Large data volumes require expensive infrastructure and operations. Maintaining data accuracy requires cleansing and deduplication.

  • Data Analysis Tools: Advanced software and skilled data analysts are needed for data analytics (Azeroual et al., 2023). Costs include tools, licencing, maintenance, and staff.

  • Compliance: To avoid penalties, GDPR compliance requires additional data security, privacy, and legal compliance investments (Brandy, 2023).

  • Risk Management: Cybersecurity investments and insurance policies reduce financial and reputational risks from data breaches.

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2.2 Descriptive, Predictive, and Prescriptive Applications

Customer data allows ABANCA to use data analytics to transform its marketing strategies.

Descriptive Analytics for Customer Profiling:

Descriptive analytics finds patterns and trends in historical data (Hirt et al., 2019). ABANCA can create detailed customer profiles using this method:

  • Segmenting customers by demographics, behaviours, and preferences.

  •  Mapping customer journeys to analyse bank product and service interactions.

  •  Refine future marketing campaigns by analysing past performance.

Predictive Analytics for Targeted Marketing:

Predictive analytics predicts trends using historical data and statistical algorithms (Surendro, 2019). For ABANCA, this means:

  • Prioritising high-value segments by predicting Customer Lifetime Value (CLV).

  • Identifying and implementing retention strategies for customers at risk of churn.

  • Suggesting cross-selling and upselling based on customer behaviour and needs.

Prescriptive Analytics for Personalized Recommendations:

Prescriptive analytics provides actionable advice beyond prediction (De Leoni et al., 2020). This allows ABANCA to offer customers:

  • Improved customer satisfaction by providing customised financial advice related to their goals.

  • Boosted revenue by suggesting relevant products and services.

  • Enhanced customer retention by personalising experiences.

These analytics methods allow ABANCA to use customer data to make informed marketing decisions, improve customer satisfaction, and meet business goals.

2.3 Data Analytics Software Tools

An essential suite of data analytics software tools helps ABANCA use customer data for marketing and analytics:

  • Data Visualisation Tools: Tableau and Power BI make complex data easier to understand by creating visually appealing charts, graphs, and dashboards (Dartmann et al., 2019).

  • Data Integration Platforms: These platforms streamline data integration and ensure data consistency and accuracy, enabling more meaningful analysis.

  • Statistical Analysis Software: Data-driven decision-making requires statistical analysis software like R or Python libraries like Pandas and NumPy.

  • Machine Learning Frameworks: TensorFlow and scikit-learn enable predictive model development, improving marketing strategies.

  • Customer Relationship Management (CRM) Software: CRM software like Salesforce manages customer interactions and tracks behaviours, enabling personalised marketing campaigns (Ranjan and Foropon, 2021).

  • Big Data Processing Tools: Hadoop and Spark are essential for processing large amounts of data.

3. Data Security and Privacy 

3.1 Data Security Measures

  • Encryption: Data in transit and at rest must be protected by strong encryption protocols (Attaallah et al., 2022). This keeps data unreadable and inaccessible even if unauthorised.

  • Access Control: Only authorised personnel should access sensitive customer data. To restrict data access, use RBAC and strong authentication.

  • Data Redundancy and Backup: Regular backups of customer data protect against hardware failures and other unforeseen events (Patil et al., 2020).

  • Intrusion Detection and Prevention Systems (IDPS): IDPS can detect and prevent security breaches in real time, reducing data breaches (Ali et al., 2021).

  • Regular Security Audits: To identify and fix data infrastructure vulnerabilities, security audits and vulnerability assessments are necessary.

3.2 Data Privacy Measures

  • Consent Mechanisms: Customers must give informed consent for data use. ABANCA must disclose data use purposes and obtain customer consent.

  •  Data Minimization: GDPR's data minimization principle requires collecting only the data needed for the purpose. ABANCA should only collect and store data as needed.

  • Data Subject Rights: GDPR gives data subjects the right to access, rectify, and erase their data. ABANCA needs easy ways for customers to exercise these rights.

  •  Privacy by Design: Data processing must start with privacy measures (Venkatraman and Venkatraman, 2019). Privacy should be built into ABANCA's systems and processes.

  •  Data Protection Impact Assessments (DPIAs): DPIAs for high-risk processing activities identify and mitigate privacy risks

  • Data Transfer Safeguards: Standard Contractual Clauses (SCCs) or Binding Corporate Rules (BCRs) must be used to protect customer data transferred outside the European Economic Area (EEA).

3.3 Challenges and Compliance

ABANCA faces several data security and privacy issues:

  • Complexity of Compliance: GDPR compliance is complicated by legal and technical requirements. ABANCA must constantly monitor and adjust its practises to regulatory changes.

  • Data Accuracy: Data Analytics and GDPR compliance require accurate and up-to-date customer data (Kumar and Bhatia, 2020).

  • Third-Party Risks: ABANCA is responsible for the security and privacy of customer data shared with third-party vendors for analytics or other purposes. Selecting trustworthy partners requires diligence.

  •  Data Breach Response: GDPR requires reporting data breaches within 72 hours, so a well-defined incident response plan is essential.

  •  Ethical Considerations: ABANCA should consider data usage ethics beyond legal compliance (Aljazaery et al., 2020). Ethical data practises require customer autonomy and transparency.

4. Ethical Considerations

4.1 Opt-In and Opt-Out

Ethical data usage includes giving customers the option to opt in or out. It respects customer privacy and autonomy. This choice has major consequences:

•    Respect for Autonomy: The option to opt in or out acknowledges customers' control over their personal data. This ethical approach builds trust and gives customers control over their data.

•    Transparency and Informed Consent: Ethical data practises require customers to be informed of how their data will be used through transparency and informed consent (Frazzetto et al., 2019). ABANCA must explain data collection and processing purposes clearly. Informed consent ensures customers understand the consequences before consenting.

•    Privacy Protection: Ethical opt-in and opt-out mechanisms protect customer privacy by preventing data collection and usage without consent (Attaallah et al., 2022). This protects customers from unwanted data collection.

•    Mitigating Unintended Consequences: Allowing customers to opt out can reduce the risk of unintended consequences, such as receiving irrelevant or unwanted marketing communications. Ethics prioritise customers' needs.

4.2 Other Ethical Issues

Data source transparency and fairness:

Ethics require data source transparency. ABANCA should disclose its analytics data sources and ensure they are ethical:

•    Ethical Data Sourcing: ABANCA must use legal and ethical methods to obtain data. This includes using reliable data sources and avoiding privacy and rights violations (Venkatraman and Venkatraman, 201).

•    Fairness in Data Usage: Data must be used fairly and non-discriminatorily (Ali et al., 2021). Data analytics should promote fairness and equality by not reinforcing biases or discriminating against groups.

Avoiding Data Use Discrimination:

Data ethics require avoiding discrimination and bias in all data use:

•    Algorithmic Fairness: ABANCA should identify and mitigate biases in data analytics algorithms. This prevents discrimination based on race, gender, or socioeconomic status.
•    Non-Discriminatory Marketing: Ethical marketing practises promote inclusive messages and offers without discrimination (Labrecque et al., 2021). Marketing should target diverse audiences without stereotyping or prejudice.
•    Regular Audits and Monitoring: Implement regular audits and monitoring to identify and correct discriminatory or biased data analytics practises (Hirt et al., 2019).

Data usage is fair and just under ethical oversight.

5. Artificial Intelligence

5.1 AI in Data Security

Data security is revolutionised by AI. The real-time threat detection and rapid response capabilities enhance traditional security:
•    AI-powered security systems can detect and analyse cyber threats in large datasets. This includes detecting user behaviour, network traffic, and system log anomalies that humans may miss (Anshari et al., 2019).
•    AI can predict potential threats based on historical data, enabling proactive measures to prevent security breaches (Patil et al., 2020).
•    AI-driven security solutions automate incident response, enabling immediate threat mitigation, damage reduction, and time reduction (Brandy, 2023).
•    AI continuously learns and improves to detect and respond to new attack vectors.
•    AI can improve user authentication by using behavioural biometrics, making it harder for attackers to impersonate legitimate users.

5.2 AI in Data Privacy

Data privacy can be protected by AI technologies:
•    Data Anonymization: AI can help anonymize data by removing or obfuscating personally identifiable information (PII) while retaining its utility for analytics (Azeroual et al., 2023). This protects customer privacy and enables meaningful analysis.
•    Pseudonymization: AI facilitates data analysis without identifying individuals by replacing PII with pseudonyms. Anonymization reduces data breaches and improves GDPR compliance.

Ensuring GDPR Compliance with AI Solutions:

Companies handling customer data worry about GDPR compliance. AI helps with compliance in several ways:
•    AI can help manage and track customer consents for data usage, ensuring that data is used as agreed upon by customers (Venkatraman and Venkatraman, 201).
•    AI can simplify data subject rights processes, making it easier for organisations to comply with GDPR requirements, including access, correction, and deletion of personal data (Aljazaery et al., 2020).
•    AI can aid in conducting Data Protection Impact Assessments (DPIAs) to assess the impact of data processing on individual privacy and determine risk mitigation measures.
•    AI enables organisations to demonstrate GDPR compliance by automating report generation.

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5.3 AI and Ethical Concerns

As AI becomes more embedded in data analytics, ethical issues arise regarding AI algorithms:

  • Transparency: Insisting on transparency in AI algorithms is crucial. AI decisions that affect people's lives should be explained by organisations (Labrecque et al., 2021).

  • Bias Mitigation: AI algorithms may unintentionally reinforce historical biases. In order to treat everyone equally, ethical AI practises monitor and mitigate bias (Hirt et al., 2019).

  • Algorithmic Accountability: Organisations should take responsibility for AI algorithm decisions. Clear accountability and oversight are essential (Kumar and Bhatia, 2020).

  • Fairness: This is an ethical requirement for AI-powered analytics. Algorithmic biases should be addressed and diverse groups must be given equal opportunities and treatment.

6. Conclusion

In an era of rapid data growth and digitalization, ABANCA's data analytics project is both exciting and challenging. The financial institution must balance data usability, security, privacy, and ethics when using customer data for marketing.

Effective customer profiling, targeted marketing, and personalised recommendations are the benefits of using customer data for marketing. These benefits come with data collection, storage, and compliance costs.

GDPR requires strict data security and privacy. For trust, ABANCA must protect customer data with encryption, access control, and ethical data practises. Customer consent, fairness, transparency, and bias mitigation are ethical issues.

Responsible and customer-centric data analytics is based on these principles. AI improves data security and privacy but raises ethical issues that must be managed.

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References

Ali, B., Gregory, M.A. and Li, S., 2021. Multi-access edge computing architecture, data security and privacy: A review. IEEE Access, 9, pp.18706-18721.
Aljazaery, I., Alrikabi, H. and Aziz, M., 2020. Combination of hiding and encryption for data security.
Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A., 2019. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), pp.94-101.
Attaallah, A., Alsuhabi, H., Shukla, S., Kumar, R., Gupta, B.K. and Khan, R.A., 2022. Analyzing the Big Data Security Through a Unified Decision-Making Approach. Intelligent Automation & Soft Computing, 32(2).
Azeroual, O., Nikiforova, A. and Sha, K., 2023, June. Overlooked Aspects of Data Governance: Workflow Framework For Enterprise Data Deduplication. In 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) (pp. 65-73). IEEE.
Bharadiya, J.P., 2023. Machine Learning and AI in Business Intelligence: Trends and Opportunities. International Journal of Computer (IJC), 48(1), pp.123-134.
Brandy, S., 2023. Overcoming Challenges and Unlocking the Potential: Empowering Small and Medium Enterprises (SMEs) with Data Analytics Solutions. International Journal of Information Technology and Computer Science Applications, 1(3), pp.150-160.
Dartmann, G., Song, H. and Schmeink, A. eds., 2019. Big data analytics for cyber-physical systems: machine learning for the internet of things. Elsevier.
De Leoni, M., Dees, M. and Reulink, L., 2020, October. Design and evaluation of a process-aware recommender system based on prescriptive analytics. In 2020 2nd International Conference on Process Mining (ICPM) (pp. 9-16). IEEE.
Frazzetto, D., Nielsen, T.D., Pedersen, T.B. and Šikšnys, L., 2019. Prescriptive analytics: a survey of emerging trends and technologies. The VLDB Journal, 28, pp.575-595.
Hirt, R., Kühl, N. and Satzger, G., 2019. Cognitive computing for customer profiling: meta classification for gender prediction. Electronic Markets, 29(1), pp.93-106.
Kumar, R. and Bhatia, M.P.S., 2020, October. A systematic review of the security in cloud computing: data integrity, confidentiality and availability. In 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 334-337). IEEE.
Labrecque, L.I., Markos, E., Swani, K. and Peña, P., 2021. When data security goes wrong: Examining the impact of stress, social contract violation, and data type on consumer coping responses following a data breach. Journal of Business Research, 135, pp.559-571.
Patil, B.P., Kharade, K.G. and Kamat, R.K., 2020. Investigation on data security threats & solutions. International Journal of Innovative Science and Research Technology, 5(1), pp.79-83.
Ranjan, J. and Foropon, C., 2021. Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, p.102231.
Surendro, K., 2019, March. Predictive analytics for predicting customer behavior. In 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT) (pp. 230-233). IEEE.
Venkatraman, S. and Venkatraman, R., 2019. Big data security challenges and strategies. AIMS Mathematics, 4(3), pp.860-879.

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