AI model for Loan Eligibity checking system
About
The client, which has its headquarters in the Asia-Pacific area, caters to an underserved demographic that has a limited history of credit risk. The company has operations in more than 40 countries, provides its services to consumers in over 180 countries, and employs roughly 19,800 people across the globe. The customer also obtains additional services, such as microfinancing and business loans secured against real estate.
Project Highlights
By integrating AI technology into its Credit platform, the fintech company plans to increase its footprint in additional areas in the near future. For the purpose of managing the credit platform, our team developed an ML-powered model on alternative data. These machine learning algorithms will determine the customer's eligibility depending on their credit score. Product was designed to facilitate an easier procedure to search instantaneously, compare results, and apply for jobs. The user can browse through several different possibilities and select the one that works best for them.
The Challenges
- Unique Chatbot Calculator:The client wanted to combine calculator and chatbot capabilities in a single function.
- Track Output Performance:It was difficult for the client to keep a tab on the output performance.
- Customer Categorization:The client wanted to segregate customers due to various risk factors.
- Reduce Model Complexities:Multiple features in the model made it challenging to track process.
Tech Stack
jQuery
Python
.Net core
ReactJs
Power BI
MySQL
Result
Faster Response Rate
This made user interaction faster and seamless as end user can check the eligibility and get the response for raised queries on immediate basis.Track Output Performance:
It was difficult for the client to keep a tab on the output performance.Accurate Segmentation of Customers
Brainvire successfully categorized the customers based on transaction patterns over time intervals, past loan repayment behaviour, history, etcPattern Tracking of Customer:
This made easier tracking system as using algorithm has helped the assessment of the loan applicant and keep a regular track on the dues of the applicant or previous behaviour.
