Data Science
Automate the business loan approval system for a central bank using historical data of borrowers
NPAs is a four-letter word in Indian banking industry. Can you design a system for loan approval that minimises chances of bad debt due to human error?
Role
Machine Learning Engineer
Industry
Technology
Menterns at work
95
Level
Intermediate
Time Commitment
Submit First Draft in 30 days
Duration
60 days
Tools you’ll learn
Here’s What You Work On
About the Company
Accion Labs has been building innovations for businesses across industries like healthcare, fintech and e-commerce for over a decade. They believe digital transformation and innovation can happen at any level of organisations. Accathons are an internal event to encourage innovation and out-of-the-box problem solving at the company. The current menternship calls on menterns to develop a fin-tech innovation for merchant banks.
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SQL (PostgresQL)
Exploratory Data Analysis
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Bridging the gap
Reserve Bank of India predicts that non-performing assets for Indian banks are likely to cross Rs 10 lakh crores in the financial year 2022-2023. The non-performing assets for a bank are primarily bad debts - these are loans which have not been repaid, and are likely to not be repaid. It is the task of a loan manager to assess the credit rating of loan applicants to ensure that there are no bad debts and most loans are repaid on time. Data science can create machine learning models to optimise this process and save the banking industry several crore rupees by making smarter loan decisions.
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Data Analysis
Data preparation
Hypothesis testing
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Expected output
In this menternship, you will be challenged to use PostgresQL to create an automation system that generates a recommendation to approve or deny loan requests based on historical data of loan application and credit profiles of applicants.
Create
Data preparation of the given dataset
SQL-based Automation System to recommend loan approved or denied
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What you’ll need before starting
SQL, Postgres-QL
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Automate the business loan approval system for a central bank using historical data of borrowers
NPAs is a four-letter word in Indian banking industry. Can you design a system for loan approval that minimises chances of bad debt due to human error?
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