Making Small/Medium Business Banking Easier
The SME Banking accelerator demonstrates a model that analyzes existing known financial factors of a small business, and automatically pre-approves a loan. Further, upon request for a larger amount loan amount, the model evaluates the risk and recommends further actions needed to acquire the loan.
Evaluating a loan risk is challenging and costly for SME banking institutions. The SME Banking accelerator uses machine learning and explainable AI solutions to analyze existing financial information and known factors of the business. The model evaluates the risk of a requested loan, and automatically approves or rejects it for a customer. Further, upon request for an approved loan, the model recommends a loan amount to the customer. Lending institutions and banks can use our accelerator to reduce costs and risks associated to loans and make better decisions faster.
The SME Banking Prediction accelerator includes a set of sample data science assets. The Data assets provides the information that you need to predict loan status, such as acceptance of loan, life of loan, and amount of loan. Your data scientists can use the sample notebooks, predictive models, and UI provided to accelerate data preparation, machine learning modeling, and data reporting.
Financial institutions face a number of challenges throughout the Small / Medium Enterprise lending value chain that are preventing them from addressing the current gap in lending. These challenges include:
- Minimal or poor integration of SME data with credit bureaus and other financial tools.
- Lack of proper process auditability because of applications’ and approvals’ manual processes.
- Data Collection, payment, and collections is a manual process, thus difficult to track.
Machine learning and explainable AI solutions to evaluate the risks and to facilitate decision-making.
- Acceptance classifier predicts the most likely outcomes of an applied loan (Accept/Reject).
- Loan life classifier predicts the life of a loan (longer/shorter than one year).
- Credit limit regressor suggests an optimal amount of that yields the maximum profit for the lending agency.
- Interest rate regressor informs the customer about an estimation of the loan interest rate.
Accelerator use case
The accelerator will provide AI solutions to help lending agencies process loan data more efficiently. This process can be improved by equipping SME customer with relevant AI predictions.
The accelerator will provide a sample application that the SME customer can use. The sample application will have the following:
- Prediction of a pre-approved loan amount based on the customer historical data.
- Additional information such as estimated interest rate and monthly payment become available as soon as the customer inputs data for a new loan.
- Prediction of the most likely outcome of the loan status (Accept/Reject) if the customer applies for a new loan.
- Predicted amount of the new loan that is recommended for a customer based on the updated information.
An amount will be recommended only if the loan is accepted and the life of loan is predicted as longer than one year. The model considers a wide range of values based on the customer's applied amount and recommends the amount that yields the maximum profit.
User groups for CP4D services
- The analytics team will have access to a sample data preparation notebook. The scripts give an idea of how they can prepare their organization data into a feature set for training models.
- The analytics team will have access to machine learning models built by AutoAI, which they can run by clicking a few buttons. There is also a Jupyter notebook on predicting loan status, life, and amount, for advanced data scientists in the team. Also, key factors that influenced the prediction of the most likely outcome, are visible from AutoAI and notebooks.
- The development team will see how they can easily deploy machine learning models with Watson Machine Learning.
- The SME customer will be able to use a custom user interface to key in details and get the AI predictions.
This is Natalie Smith, the owner of Watson Café who would like to apply for a loan. A sample loan application is provided for Natalie to interact with the deployed models.
This is how Natalie, the loan applicant, would interact with the application.
1. Natalie is the SME customer who wants to apply for a loan. She first looks at her checking and saving accounts information. If she is eligible for a loan, she can view a pre-approved amount for the loan that is recommended based on her past information. She can apply for a new amount and click on 'Start Application' to start the loan application process.
2. Natalie keys in details of her business including the term and the amount of the loan and views the estimated interest rate and monthly payment which are calculated based on her input. She clicks on the 'Continue with application' button.
3. Natalie might need to upload additional documents if she needs to update her information and convince the lending agency for a higher loan amount.
4. Natalie sees a new screen with the decision on the loan application. This page includes the following information.
- Top panel: Information about the loan status and whether the amount of loan requested is accepted.
- Bottom left panel: If the loan is accepted, a new loan amount is suggested immediately instead of sending her through a long process. Here, additional information such as the term of the loan is provided. Natalie can accept the loan here.
- Bottom right panel: Natalie can choose to call an SME advisor.
Installing and running the accelerator
Required services: This accelerator requires IBM Cloud Pak for Data base component installed.
Importing the accelerator
To use this accelerator on Cloud Pak for Data v3.0.1, contact the Data Science Elite team.
This accelerator has been verified on:
- Cloud Pak for Data v3.0.1
About the developer:
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