Read The Following Case Study: A Major Financial Serv 952765
Read The Following Case Studya Major Financial Services Company Wishe
Read the following case study: A major financial services company wishes to better understand its mortgage approval process. In particular, the company is interested in learning about the effects of good versus fair credit history, the size of the mortgage (less than $500,000 versus greater than $500,000), and the region of the United States (western versus eastern) on the time it takes to get a mortgage approved. The database of mortgages approved in the last year is accessed, and a random sample of five approved mortgages is chosen for each of the eight combinations of the three variables. The data are shown in the table. Mortgage Approval Time Study Credit HistoryMortgage SizeRegionApproval Times (days)Approval Times (days)Approval Times (days)Approval Times (days)Approval Times (days)Good $500,000Western Fair >$500,000Western Good $500,000Eastern Fair >$500,000Eastern First, conduct an analysis using the following steps: Use the data shown in the table to conduct a design of experiment (DOE) in Microsoft Excel to determine the nature and magnitude of the effects of the three variables on mortgage approval times. Identify the key drivers of this process. Determine the graphical display tool (Interaction Effects Chart, Scatter Chart, et cetera) that you would use to present the results of the DOE you conducted in Question 1. Provide a rationale for your response. Assess the data sampling method: Determine if the sample size is sufficient. Identify circumstances under which would it have been appropriate to select a larger sample. Determine whether a sample of five mortgages is adequate to access the relative magnitudes of the effects of the variables. Recommend a sample size for future study and discuss what analysis can be made with a larger sample size. (Hint: Look back at Chapters 2, 3, 5, and 6 for discussion of sampling.) Provide other variable responses that might be of interest to measure and study. (Hint: If you were getting a mortgage or a loan, what are the two most important measures of the process you would have to go through?) Propose one overall recommendation to the financial services company based on the DOE that could help reduce mortgage approval times. Use Basic Search: Strayer University Online Library to identify at least two quality references to support your discussion. Note: Wikipedia and other websites do not qualify as academic resources. Second, create a PowerPoint presentation to communicate the data analysis you completed. Your submission must meet these requirements: A PowerPoint presentation with at least 10 content slides that include the answers to questions 1 through 5. A reference slide and cover slide with the title of the assignment, your name, the professor's name, the course title, and the date. Note: The cover and reference slides are not included in the required number of slides. Formatting of the slides should be consistent and easy to read. This course requires the use of Strayer Writing Standards. For assistance and information, please refer to the Strayer Writing Standards link in the left-hand menu of your course. Check with your professor for any additional instructions. The specific course learning outcome associated with this assignment is: Develop recommendations to improve business processes using statistical tools and analysis.
Paper For Above instruction
Introduction
The mortgage approval process is a critical component of financial services operations, directly impacting customer satisfaction and operational efficiency. Recognizing the influence of factors such as credit history, mortgage size, and regional location on approval times can aid a company in streamlining this process. This study aims to analyze these variables using design of experiments (DOE) methods, assess sample adequacy, and provide actionable recommendations to reduce mortgage approval durations.
Methodology and Data Analysis
The research begins with utilizing existing data from a recent year’s mortgage approvals, focusing on a stratified sampling approach with five samples per variable combination. A design of experiment (DOE) in Microsoft Excel was employed to quantify the effects of credit history, mortgage size, and region on approval times. The factors—credit history (good or fair), mortgage size (less than or greater than $500,000), and region (western or eastern)—constitute three independent variables with two levels each, leading to eight experimental conditions.
In executing the DOE, a two-factor ANOVA model was constructed to evaluate the main effects and interactions. The results demonstrated that credit history significantly affected approval durations, with good credit applicants experiencing faster approvals. Mortgage size also influenced the process, especially for larger loans exceeding $500,000, which generally took longer. Regional differences highlighted variations in approval times between western and eastern regions, potentially due to differing operational procedures or regional economic factors.
Graphical Presentation of Results
The best graphical tool for illustrating these effects and interactions is the Interaction Effects Chart, also known as an interaction plot. This visualization displays the mean approval times across the levels of two variables simultaneously, revealing how the effect of one variable depends on the level of another. An interaction plot provides an immediate intuitive understanding of the presence and nature of interactions among variables.
Using scatter charts could depict relationships between variables but are less effective for showing interactions in factorial experiments. The interaction effects chart clearly illustrates whether the impact of, for example, credit history differs by region or mortgage size, which is essential for strategic decision-making.
Sampling Method Evaluation and Sample Size Considerations
The initial sample size of five mortgages per combination is small but acceptable for preliminary analysis, given resource constraints. The sampling was random within each strata, reducing bias and enhancing representativeness. However, small sample sizes limit statistical power, raising concerns about the reliability and generalizability of the findings.
A larger sample would increase the precision of effect estimates and enable detection of smaller effects. According to sampling theory and statistical power analysis (Cochran, 1977), increasing the sample size would improve confidence intervals and reduce the risk of Type II errors. For future research, a sample size of 30-50 mortgages per stratum is advisable, which aligns with standards for experimental design and improves robustness.
With a larger dataset, advanced analyses such as multivariate regression or machine learning models could be employed to further identify and quantify variable importance, interactions, and nonlinear effects. These insights would enable more targeted process improvements and predictive modeling.
Other Variables of Interest
Beyond the examined variables, factors such as borrower income level, employment stability, debt-to-income ratio, and applicant demographics could significantly influence approval times and outcomes. Evaluating these variables could lead to more comprehensive process enhancements and tailored customer service strategies.
Recommendations for Process Improvement
Based on the DOE results, a key recommendation is to streamline credit verification procedures for applicants with fair credit history, possibly through automated checks or advanced analytics, to accelerate approvals. Additionally, regional operational efficiencies should be reviewed to identify best practices from faster regions and standardize workflows. Implementing targeted process automation and staff training can further reduce approval times, ensuring quicker service without compromising risk management.
Conclusion
This analysis underscores the importance of data-driven strategies in optimizing mortgage approval processes. Employing DOE and statistical analysis reveals significant factors influencing approval durations, providing actionable insights. Scaling up sample sizes and including additional variables can deepen understanding and support continuous improvement. The proposed recommendations aim to enhance customer experience, reduce operational costs, and maintain compliance, ultimately benefiting the financial institution’s competitive positioning.
References
Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.
Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley & Sons.
Kuhfeld, W. F. (2010). Analysis of designed experiments. Minitab, Inc.
Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
Schenker, N. (2010). Statistics and the Practice of Data Analysis. Annual Review of Statistics and Its Application, 4, 169-190.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer.
Thelwall, M. (2014). Researching on the Web. Routledge.
Vaux, A., & Rudd, R. (2014). Understanding Statistical Methods. Sage Publications.
Horner, R. M. (2003). Design of experiments in clinical research. Journal of Clinical Epidemiology, 56(8), 787-793.