Formulating A Strategic Plan Based On Statistical Analyses And Applications For Financial Companies Through A Real-World Use Case
This work addresses risk mitigation and revenue enhancement for financial companies, but it is incremental as it applies existing statistical and machine learning methods to a specific real-world case without introducing new methodologies.
The study tackled the problem of improving loan risk assessment and revenue for financial companies by developing a strategic plan based on statistical analyses, applied to LendingClub, which found that loan amounts significantly impact borrower charge-offs and proposed using machine learning for better predictive models.
Business statistics play a crucial role in implementing a data-driven strategic plan at the enterprise level to employ various analytics where the outcomes of such a plan enable an enterprise to enhance the decision-making process or to mitigate risks to the organization. In this work, a strategic plan informed by the statistical analysis is introduced for a financial company called LendingClub, where the plan is comprised of exploring the possibility of onboarding a big data platform along with advanced feature selection capacities. The main objectives of such a plan are to increase the company's revenue while reducing the risks of granting loans to borrowers who cannot return their loans. In this study, different hypotheses formulated to address the company's concerns are studied, where the results reveal that the amount of loans profoundly impacts the number of borrowers charging off their loans. Also, the proposed strategic plan includes onboarding advanced analytics such as machine learning technologies that allow the company to build better generalized data-driven predictive models.