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Module 4: Assignment Case Study Assignment 4.1: Visa is Enhancing the Customer Experience While Reducing Fraud With Predictive Analytics IDEX BiometricsLinks to an external site. has gathered some interesting facts on credit card fraud in 2020: 46% of the world’s credit card fraud happens in the United States, out of which 25.7% charges were made to an existing credit card 21.4% were made using an existing checking or savings account 15.9% of transactions were made using an existing debit account 0.5% of transactions were made on an existing loan or line of credit 18.5% of transactions were made using financial accounts such as PayPal 65% of the time, credit card fraud results in a direct or indirect financial loss for the user According to the Nilson ReportLinks to an external site., global card losses are expected to exceed $35 billion by 2020 This case study assignment examines the use of predictive analytics to address an ongoing dilemma of extending customer credit card services while attempting to mitigate costly fraudulent activities. Often the expansion of additional credit card services also potentially increases fraudulent claims. This case will reinforce the value of data mining techniques and their role in predictive analytics. To complete this assignment, see page 194 in Business Intelligence, Analytics, and Data Science: A Managerial Perspective. In about one paragraph each, address the three questions associated with this case. To successfully complete this assignment, view the Case Study Assignment Rubric document.Download Case Study Assignment Rubric document.

Module 4: Discussion Ethical and Social Issues and Data Mining Ethical issues with data mining abound. For example, when businesses collect personal or private customer data, questions may arise such as: What can be done with the data/information? Who owns it? How can it be used? When should it be disposed of? Where can it be securely stored? Should data collection consent be required? Can the accuracy of the data be determined? Data can be thought of as a business currency that is collected for its ever-increasing resale value. And yet, businesses must carefully balance capitalizing on the usage of this dubious currency while navigating a multitude of ethical and/or legally explosive landmines. Understanding the various ethical issues surrounding the use of data mining techniques and its applications is challenging at best.  For your initial post in the discussion forum, review the module lesson, and address the following: First, share an example from the news media (journals, newspapers, magazine articles, or current news stories, etc.), of an ethical issue posed by data mining techniques and its applications within a specific organization or industry. Be sure to link to the example, and briefly describe the specific ethical concerns and how they are being addressed. Then, reflect on how you personally define ethics, and share what you think the ethical approach is for the example you shared.  Next, assess what some of the potential ethical problems may be with predictive analytics. Provide specific examples, and share links to examples that support your arguments. Respond to at least two of your peers by reviewing their posts, and explain whether you think the example of ethical considerations they presented should be enforced and if so, explain how (and if not, explain your rationale). Then share your opinion on how far governing bodies should go in restricting the data mining activities of businesses in order to protect private citizens (for example, governance, legislation, oversight, opt-in/opt-out laws, etc.). To successfully complete this assignment, view the Discussion Rubric document. To Comment On: Response 1: A. According to PMC PubMed Central, data mining poses many ethical issues in the medical field. Digital phenotyping is a process that takes information from devices like smartphones, computer searches, and tablets and uses that information to map out behaviors. They use computer learning along with physiological and biometric data gathered from our devices and apply that information to behavior tests to help with many different aspects of the medical field. The ethical issues come from no one controls the type of data they can collect, when they collect it, and to what depths they are allowed to stoop too in order to gather information. Some of the ways that this is trying to be countered is through HIPPA and other programs for privacy but as shown by Facebook these programs are easy to get around and show no concern for the information gathered. B. I think it's fine to pull someone's shopping habits or viewing habits from the internet, though I think people need to be more careful about what they view on the internet. I don't like that all of these apps and programs and devices listen to everything we say. They should not be allowed to pull information from phone calls or text messages. Those should be considered private and not for public information. They should never be allowed to record conversations in real life having nothing to do with devices of any sort. C. Some of the problems with predictive analytics are things like incomplete information. Computers cannot always pick up on moods or sarcasm or emotional statements. They also can't understand a human's background and reasons behind some statements or thoughts. Another problem is the range in which some information is collected can be narrow and exclude many more complicated reasons for a person's behavior. I sadly do not see a realistic way to govern this type of information leaks. It would not matter what rules you applied, there would be no way to enforce it with any great strength and no way to stop it from happening again and again. Our lives now that the technology is here cannot go backwards into safety.  References:  Martinez-Martin, N., Insel, T. R., Dagum, P., Greely, H. T., & Cho, M. K. (2018). Data mining for health: staking out the ethical territory of digital phenotyping. Npj Digital Medicine, 1(1). https://doi.org/10.1038/s41746-018-0075-8Links to an external site. Loshin, D. (2021, December 7). 6 challenges of building predictive analytics models. Business Analytics. https://www.techtarget.com/searchbusinessanalytics... Response 2: The Cambridge Analytica Scandal The Cambridge Analytica scandal revealed serious ethical breaches involving the unauthorized harvesting of Facebook user data. The data was used to create detailed psychological profiles to influence voter behavior during significant political events, including the 2016 U.S. presidential election and the Brexit referendum (Confessore, 2018). The ethical concerning factors were consent, ownership, purpose and use, security and disposal. Consent: Facebook users' data was collected without their explicit consent, which raises questions about the transparency and fairness of the data collection methods (Confessore, 2018). Ownership: Users' data was exploited by Cambridge Analytica without their informed knowledge or approval, highlighting issues around data ownership and control (UNKNOWN, 2019). Purpose and Use: The data was used for targeted political manipulation, which many view as an abuse of personal information for purposes beyond what users anticipated (McCallum, 2022). Security and Disposal: The data's handling by a third party with questionable ethical practices led to concerns about data security and the adequacy of measures to prevent misuse (Confessore, 2018). In response to the scandal, Facebook undertook steps to tighten data access policies and improve transparency. The company also faced legal repercussions and public criticism, which led to a broader dialogue on data privacy and the need for stronger regulations (McCallum, 2022). However, ongoing criticisms question the effectiveness and enforcement of these measures. Ethics involves adhering to principles of right and wrong that guide behavior, focusing on fairness, transparency, respect for individual rights, and accountability. An ethical approach would have involved obtaining explicit, informed consent from users before collecting and utilizing their data. Ensuring transparency about data use and securing the data against misuse would have aligned with respecting user autonomy and privacy. Ethical Problems with Predictive Analytics could include privacy invasion, discrimination and bias, and also the misuse of data. Privacy Invasion: Predictive analytics often involves the collection and analysis of extensive personal data, which can lead to significant privacy concerns if not handled with proper consent and safeguards. Example: Target's Predictive Analytics Controversy Target’s use of predictive analytics to identify pregnant women based on purchasing behavior led to privacy issues when the company sent targeted advertisements to a teenage girl, revealing her pregnancy before she had disclosed it to her family (HILL, 2022). Discrimination and Bias: Predictive models can perpetuate existing biases if the training data is skewed, leading to discriminatory outcomes such as biased hiring or loan approval processes. Example: ProPublica's Investigation into Predictive Policing ProPublica's report uncovered that predictive policing algorithms often exhibited bias against minority communities, resulting in disproportionately higher levels of surveillance and policing in these areas (Julia Angwin, 2016). Data Misuse: Predictive analytics can lead to the misuse of data for unintended purposes, such as manipulating consumer behavior or profiling individuals without their consent. Example: Facebook and Cambridge Analytica the misuse of data by Cambridge Analytica for political manipulation underscores the risks of predictive analytics being applied unethically. The lack of adequate oversight and control over predictive models can result in significant ethical challenges (Confessore, 2018). Ethical issues in data mining and predictive analytics revolve around consent, privacy, fairness, and accountability. Addressing these concerns requires robust data governance practices, transparency, and adherence to ethical standards that safeguard individuals' rights and dignity. Technology advancement is essential for the growth we look for although it is essential to safeguard our privacy and use the data ethically.

Notes: Paliagrsim Unacceptable. You must include at least 5 references. Format your references using APA style. Each answer must not be less than 300 words The Assignment must be submitted on Blackboard (WORD format only) via allocated folder. Assignments submitted through email will not be accepted. Students are advised to make their work clear and well presented; marks may be reduced for poor presentation. This includes filling your information on the cover page. Students must mention question number clearly in their answer. Late submission will NOT be accepted. Avoid plagiarism, the work should be in your own words, copying from students or other resources without proper referencing will result in ZERO marks. No exceptions. All answered must be typed using Times New Roman (size 12, double-spaced) font. No pictures containing text will be accepted and will be considered plagiarism). Submissions without this cover page will NOT be accepted.

Scenario: A new client implementation for an ongoing placement has been live for 3 months and is 25% understaffed and 35% of the inventory is not being worked within the SLA of all claims worked within 45 days. The client’s CFO tends to review individual accounts and complain the accounts are not being worked within the SLA timeframe and is threatening to terminate the contract. Assignment: 1.Define a mitigation strategy to bring the inventory into compliance with the SLA and restore confidence with the client and the CFO. 2. Outline your plan for your first 90 days in the position of director over a new team.

Software Tool to be used: Microsoft Access KJ's Disclaimer about MS Access and it's Relevance in the workplace: Granted, few organizations use MS Access as it is more of an individual solution tool and easy to navigate BUT..... MS Access showcases data tables, table relationships and more importantly, the ability to create queries of the data selecting whatever fields and attaching criteria (AND-n-OR) to FILTER or SUMMARIZE massive amounts of data easily Remember a "Query is a Query" no matter what language it is written in including SQL (Structured Query Language) Additionally, MS Access allows you to create data entry/inquiry forms and reports too which are NOT required for this assignment. Instructions ghadah alshakkakee (name to be used in some data fields that require name)  Step 1: View the assignment Description document with step-by-step instructions: ISAN3380-HW1-BigData-Access Queries.pdf (file below)  Step 2: Download the 2 starter files to your local device/folder (Recommend the Downloads Folder so you remember WHERE you put them)  Starter Access Database: Foodmart4g.accdb (file below) YOUR Answer Sheet: ISAN3380-HW1-LnameFname.docx (file below) Follow the Assignment Instructions creating the queries, Saving Results as PDF's and Answering the questions in your answer sheet Once completed, Save/Close ALL files

its about a case about netflix and how they are enhanced by ISM. 

this homework is about E-commerce companies. in chapter 1 there is a table that describes 8 unique future of e-commerce technology So I will attach chapter 1 for you to look for the table, and I will attach you the homework. you will have to read it, understand what the questions is about, and answer each question using your own words no chat gpt. There is three companies that I have in my mind:  1st company: Namshi. 2nd: Ounass. 3rd: Jarir Bookstore  each question you will answer with one of these companies. 

Week #4-DQ2: Persuasive Messaging, When it isn't! This week we look at how to create persuasive messaging, which is important in a world that usually isn't very subtle. Ever feel like an ad was designed to hit you over the head with it's obnoxious messaging? In this discussion, consider the following questions: How do you react when you read or hear flowery, over-the-top language in promotional messages, whether it’s “the best food in town” or “the fastest smartphone on the market”? Do you dismiss it all as just marketing hype, or do you look for evidence to support the claims? If you instinctively dismiss promotional messages as mere “fluff,” why do you think so many advertisers continue to communicate in this style? Expectations: Your first response should be a minimum of 100 words, due Tuesdays/Thursdays. You should reply to a minimum of 2 other classmates' responses with 50 words minimum, due Sunday. To earn full credit you need to answer more than "I agree" or "good job." Your discussion replies may ask a clarifying question, may relate and apply to another situation, or you can probe the question deeper. But it needs to have substance in order for you to earn points.

What are some of the major Data Visualization,  Dimension Reduction, Evaluating Predictive Performance methods and how  are organizations using them to advance their business. Justify your  opinions and observations with research in  peer-reviewed academic journal articles. For a paper this short do not  include an abstract. 

Financial Analysis Financial analysis can be defined as the process of assessing the financial condition of a firm. It can be very useful in understanding the financial position of a company. There are varieties of ratios that can be used for this purpose but each has it benefits and limitations. Select two ratios you think are the most valuable when trying to understand the financial condition of a company and explain why you have selected them. Then use those ratios to assess two publicly traded US companies listed on the NASDAQ stock market. What do these ratios tell you about how investors assess the future prospects of these companies? *Make sure your selected companies are different than your classmates’. Search the SEU library or the Internet for an academic or industry-related article. Select an article that relates to these concepts and explain how it relates to doing business in Saudi Arabia. Directions: Discuss the concepts, principles, and theories from your textbook. Cite your textbooks and cite any other sources if appropriate.