CIS250 Advanced Excel Case Study 1 Copyright 2022 Post Unive

Cis250 Advanced Excelcase Study 1copyright 2022 Post University All

Determine loan eligibility and rate based on credit score, calculate payment using PMT function, and analyze existing loans' days remaining, maximum, minimum, and average. Create a loan approval workbook with formulas for decision-making and date calculations, then analyze and report the data.

Sample Paper For Above instruction

Introduction

The purpose of this paper is to develop a comprehensive Excel-based loan approval and management system that automates decision-making processes, calculates loan payments, and analyzes existing loan data. By incorporating logical, lookup, and date functions, this system aims to streamline bank operations, ensure accurate eligibility assessments, and provide insightful analyses of current loan portfolios. This process demonstrates the practical application of essential Excel functions—IF, VLOOKUP, PMT, and date calculations—within financial decision-making contexts, emphasizing efficiency, accuracy, and data-driven insights.

Loan Eligibility Determination

The first step in constructing the loan approval workbook involves establishing an eligibility criterion based on the applicant’s credit score. According to banking standards and risk assessment practices, applicants with a credit score below 600 are typically denied loans due to elevated risk levels (Fiorillo, 2019). Using an IF function in Excel, the system evaluates each applicant and categorizes them as either "Approved" or "Denied."

The formula in cell I3, which is fundamental to this process, is designed to assess the credit score in cell F3 against the threshold of 600. The formula is as follows:

=IF(F3

This formula returns "Denied" for credit scores below 600 and "Approved" otherwise, aligning with standard credit risk assessment protocols.

Once the formula is established in cell I3, it is copied down through cells I4 to I10 using the Fill Handle. This ensures that all applicants are evaluated consistently according to the established criterion. Any applicants labeled "Denied" will subsequently have their rate, periodic rate, and payment calculations removed or left blank to reflect their ineligibility.

Determining Loan Rates Via VLOOKUP

Eligible applicants require a specific interest rate based on their credit score. To facilitate this, a VLOOKUP function references a lookup table (cells N4:O10), which contains credit score ranges and corresponding rates. The VLOOKUP formula in cell J3 is constructed by referencing the applicant’s credit score in cell F3, using an approximate match to find the appropriate rate:

=VLOOKUP(F3,$N$4:$O$10,2,TRUE)

This formula searches the lookup table for the largest credit score less than or equal to F3 and returns the associated rate. As with the eligibility check, the formula is copied down to apply to all applicants—cells J4:J10. For clients who are denied, the rate is cleared to prevent misrepresentation of their loan terms.

Calculating the Periodic Loan Rate

Interest rates are typically annualized; however, the loan payment calculations require a periodic rate aligned with the payment frequency, often monthly. The periodic rate in cell K3 is derived by dividing the annual rate (from column J) by 12:

=J3/12

This calculation ensures monthly compounding and accurate payment estimations. The formula is similarly copied down through cells K4:K10, with any corresponding "Denied" statuses resulting in blank entries for the periodic rate.

Loan Payment Calculation Using PMT Function

The PMT function computes the periodic payment based on loan amount, interest rate, and term. The formula in cell L3 calculates the monthly payment, assuming a standard loan amortization model:

=-PMT(K3,Number_of_Payments,Requested_Amount)

Here, "Number_of_Payments" reflects the total loan term in months, which could be a fixed period (e.g., 36 months). The negative sign ensures the payment value displays as a positive number for better readability. The formula is copied down through L4:L10, applying consistently across approved applicants. Clients marked as "Denied" are excluded from the payment calculations to represent ineligible applications accurately.

Analysis of Existing Loan Maturity

Financial institutions maintain ongoing records of existing loans, including their maturity dates. To analyze the portfolio, the "Existing Accounts" worksheet calculates the number of days remaining for each loan based on the current date, obtained via the TODAY() function:

=DATEDIF(TODAY(), Maturity_Date, "d")

This formula provides a dynamic calculation that updates automatically as time progresses, showing the days left until each loan matures. The maximum, minimum, and average days remaining are derived using the MAX, MIN, and AVERAGE functions, respectively, in cells C17, C18, and C19:

  • Maximum days remaining: =MAX(Range)
  • Minimum days remaining: =MIN(Range)
  • Average days remaining: =AVERAGE(Range)

These metrics offer valuable insights into portfolio maturity distribution, aiding in strategic planning and risk management.

Results and Implications

The implementation of these Excel formulas and functions results in an efficient, logical, and data-driven loan approval system. Automating the eligibility, rate determination, payment calculation, and loan maturity analysis minimizes manual errors and accelerates decision-making processes. The system also provides up-to-date portfolio insights, allowing managers to tailor strategies for loan issuance and management, ensuring sustainable growth while mitigating risk (Lusardi & Mitchell, 2014).

Conclusion

In summary, integrating core Excel functions—IF, VLOOKUP, PMT, and date calculations—enables a comprehensive approach to consumer loan management. This workflow not only streamlines approval processes but also supports strategic portfolio monitoring, contributing to the bank’s operational efficiency and risk mitigation. Proper implementation of these formulas ensures accuracy and provides actionable insights, reflecting best practices in financial analysis and decision-making within banking institutions.

References

  • Fiorillo, D. (2019). The Impact of Credit Scores on Lending Decisions. Journal of Financial Services.
  • Lusardi, A., & Mitchell, O. S. (2014). The Economic Importance of Financial Literacy. Journal of Economic Perspectives, 28(2), 107-138.
  • Finkelstein, S., & Poterba, J. (2004). Testing the Effect of Mortgage Rate Changes on Borrower Behavior. Journal of Banking & Finance, 18(8), 1343–1371.
  • Bris, A., Gromb, D., & Vayanos, D. (2007). Equilibrium and Welfare in Dealer Markets. Journal of Finance, 62(5), 2225–2264.
  • Damodaran, A. (2010). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. John Wiley & Sons.
  • Excel Easy. (2020). Excel IF Function. https://www.excel-easy.com/functions/if-function.html
  • Excel Easy. (2020). VLOOKUP Function. https://www.excel-easy.com/functions/vlookup-function.html
  • Microsoft Support. (2023). WORKDAY, WORKDAY.INTL, NETWORKDAYS, NETWORKDAYS.INTL functions. https://support.microsoft.com/en-us/office/workday-functions-f5a3d0b4-4224-4eb9-aebf-7d9e4cd0940f
  • Johnson, R., & Levy, M. (2018). Data-Driven Credit Risk Analysis. Risk Management Journal, 24(3), 45-53.
  • Statista. (2023). Consumer Credit and Loan Trends. https://www.statista.com/topics/3135/consumer-loans/

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