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https://github.com/madhanmohanreddy2301/lending_club_case_study


https://github.com/madhanmohanreddy2301/lending_club_case_study

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README 

        
# LENDING CLUB CASE STUDY

> As a consumer finance company specializing in lending various types of loans to urban customers,

our primary objective is to optimize our loan approval process to maximize profitability and

minimize financial risks. The decision to approve or reject a loan application must be made based

on the applicant's profile, considering two critical types of risks.



## Table of Contents

* [General Info](#general-information)

* [Conclusions](#conclusions)

* [Technologies Used](#technologies-used)



## General Information

- Our project aims to optimize loan approval for urban customers, focusing on minimizing credit and operational risks. Leveraging advanced data analytics, we'll assess creditworthiness by analyzing diverse data points. Streamlining processes and implementing robust risk management protocols will ensure regulatory compliance and mitigate fraud, ultimately enhancing customer experience while safeguarding financial interests.

- The background of the project lies in the evolving landscape of consumer finance, where traditional methods of loan approval are increasingly being augmented by data-driven approaches. With urban customers constituting a significant market segment, there's a growing need for tailored lending solutions that balance profitability with risk mitigation. This project emerges from the intersection of technological advancements in data analytics and machine learning, coupled with the imperative for financial institutions to optimize their operations and enhance customer satisfaction.

- As a consumer finance company specializing in lending various types of loans to urban customers,

our primary objective is to optimize our loan approval process to maximize profitability and

minimize financial risks. The decision to approve or reject a loan application must be made based

on the applicant's profile, considering two critical types of risks.

- loan data set

  



## Conclusions

-  Lending Club should reduce the interest rates for 60-month loans, as they are more prone to default.

-  Grades are a good metric for detecting defaulters. Lending Club should examine more information from

borrowers before issuing loans to lower grades (G to A).

- Lending Club should limit the number of loans issued to borrowers from California, Florida, and New York

to increase profitability.

- Small business loans have a higher default rate. Lending Club should reduce or stop issuing loans for small

businesses.

- Borrowers with mortgage homeownership are taking higher loans and defaulting more frequently. Lending

Club should stop approving loans for this category when the requested loan amount exceeds $12,000.

- . People with a higher number of public derogatory records are more likely to file for bankruptcy. Lending

Club should ensure borrowers have no public derogatory records before issuing loans.



## Technologies Used

- numpy - version 1.23.5

- pandas - version 1.5.3

- matplotlib - version 3.7.0

- seaborn  - version 0.12.2



## Contact

Created by [@madhanmohan2301] - feel free to contact me!