MiniFinans predicts loan repayments with 80% accuracy
When MiniFinans provides fast consumer loans, they face the challenge of correctly assessing and rating potential loaners’ creditworthiness. MiniFinans wanted to improve the existing metrics to ensure that loaners would repay on time. The traditional rules of assessing potential customers are static, such as age, gender, and geography, and customers’ changing behavior is not taken into account, which creates uncertainty for MiniFinans every time they lend money. Since the MiniFinans provides the loan service online, it called for a solution that can quickly handle these assessments.
“The AI algorithm has saved us time and lowered risk because now we can assess loan seekers with up to 80% accuracy. This AI project positions us among the first in our business to do so, and it will shape the future of our business.”
MiniFinans engaged with 2021.AI to use AI and machine learning technology to give a more precise prediction of loan repayments by a potential borrower. The AI algorithm uses more than 30 variables and more than one million data points to assess potential borrowers. The algorithm provides full transparency, enabling MiniFinans to find out exactly why someone is either provided a loan or refused by the algorithm. This information also ensures the algorithm complies with legislation that says customers have a right to transparency regarding assessing their creditworthiness. 2021.AI delivered the use case, the coding of algorithms, and the production implementation of AI algorithms. MiniFinans now uses the algorithm to evaluate and rate all new customers.
2021.AI developed an algorithm and set it up on the Grace Enterprise AI Platform. We received CSV files with the required data to be stored on Grace and updated once a week. Subsequently, the incoming data was used to make real-time predictions of potential borrowers’ payback behavior and sent to the algorithm via an API.
2021.AI incorporated three algorithms and applied a majority voting method to combine the predictions. Those three-member classifiers were used to achieve better classification accuracy. Typically, the entire classification performance of an ensemble classifier tends to provide better accuracy than a single classifier.
3 facts about the MiniFinans project
→ The AI algorithm was developed on Grace and is hosted on a Linux server on AWS and implemented in MiniFinans’ infrastructure. The requests are sent to the server via Flask.
→ The three algorithms incorporated by 2021.AI are Random Forest, Support Vector Machines (SVM), and Logistic Regression.
→ 2021.AI based the data on Minifinans’ customer data storage. The data included age, income, taxes, operating systems, location, browser, and more.