Lasiandra Finance Inc. (LFI) New York, USA is a leading private financing company which caters for the funding needs of Small and Medium enterprises (SMEs). LFI clearly understood that some business dreams need that extra push to see them accelerated. Hence it allows its loaning process to be tailor-made and customer-centric. In the past few years, it has tremendously expanded its wings and to speed up the process, it needs to automate the loan eligibility process based on customer portfolio entered online.
Problem:
The main problem faced by the LFI is the approval process of the loans. Because it is a complicated verification and validation procedure, there is still no guarantee whether the chosen applicant is the deserving one out of all applicants. Hence, it needs a model which can predict the loan approval. Therefore, analysing the data set obtained from past customers, and building the most accurate model to predict the approval process as approved or rejected is pivotal.
Documents:
Datasets: TRAINING_DS & TESTING_DS CSV files, both of which represent the training and testing datasets respectively
SAS Code: SOURCE-CODE-MAR-2024
Full report: FULL-REPORT.pdf