Loan origination and interest make for a significant share of business for most banks. As such, seeing the future and forecasting the success and failure of a loan applicant to fulfill their obligation is critically important to banks.
Banks that leverage machine learning to predict and track delinquency can reduce bad debt levels and also protect a vital part of their business.
PI.EXCHANGE can assist by enabling banks to do this in a streamlined, accurate, and automated way with the AI & Analytics Engine. Read on to learn how ML can be applied to forecast the proportion of delinquent customers.
The Challenge: Not all banks have the bandwidth to predict delinquency
Accurately predicting delinquency on loans is important to banks. If the bank is holding the loan there are costs and risks associated with collection and default. Using ML, banks and Financial Institutions (FIs) can extract deep insights from their data to help assess loan risk.
Delinquency risk assessment is a vital aspect of a bank’s risk management framework. However, even large banks do not have unlimited resources. Often they may not have a sufficient number of data scientists to create, maintain, and deploy AI & ML models for all of their product lines. As a result, money is often left on the table due to insufficient “bandwidth” in AI & ML. We term this the “bandwidth problem”.
The Solution: Time-effective development of risk models
The AI & Analytics Engine is a smart AI-assisted Automated Machine Learning platform that easily integrates into existing systems and processes.
It also solves the bandwidth problem by automating away the drudgery of creating features and tuning models and thereby freeing your data scientist to focus on the business problems and help translate those to the business domain.
This technology has clear applications for banking risk management. When implemented, it can lower operational costs while providing decision-makers with more accurate scores
How does the AI & Analytics Engine help lenders understand delinquency risk?
Take a loan repayment time of 30 years. If the customer pays a fixed amount every month for the life of the loan the customer pays 360 installments. If the borrower fails to pay one of the installments, they are said to be delinquent. The borrower is assigned a delinquency status to keep track of their delinquency level.
The delinquency status starts at 0 where 0 means the account has kept payments up to date. The following delinquency status is 1, which means the account has missed one payment, and if the delinquency status is 2, then it means the account has missed two payments, and so on.
In this use case, we have combined all delinquency statuses from 4 onwards into the category 4+. This is because when a customer has missed 4 or more payments, they are considered to be in default.
Step 1: Data Preparation
We have prepared the data by summarizing the proportion of accounts that have moved from delinquency status 0 to delinquency status 1, 2, 3, 4+, for other possible combinations of from and to delinquency status.
Data was collected and uploaded as a CSV file into the platform. This could also be done by connecting to a database, server, etc.
Step 2: Model Recommendation & Training
Once the data has been assessed, proceed to the Model Recommender feature, and select models from the Engine‘s recommender to compare. These are provided prior to training and with a view to their predictive performance, training time, and prediction time. Once trained, select the best-performing model.
Step 3: One-click deployment
Deploying a trained model can be done on-premises, with IoT devices, or to the cloud. Once deployed, the model provides the predicted outcomes.
By looking at the divergence between the actual and the predicted, you have a great staring point from which to figure out what is going on with the portfolio.
Benefits of using the AI & Analytics Engine to predict delinquency
Bankers understand that it is always a better option to identify and mitigate risks before delinquencies and defaults occur. With the AI and Analytics Engine, banks can be proactive in identifying at-risk clients. and help them during this period of stress. The benefits of this approach with the Engine are many;
- Improved client loyalty through proactive measures to assist.
- Fast, affordable setup and easy ongoing management of ML models. So more product lines can be considered.
- Improvement of accuracy and relevance of ML model forecasts over time with new data via the continuous learning feature.
- Reduction in staff hours spent on the analysis of data.
- Risk mitigation through better protection and positioning during periods of economic stress.