Learn how you can take advantage of machine learning to improve your automated business processes with a demo scenario for evaluating bank loan applications.
Use Case: Evaluating Bank Loan Applications
Problem Statement
Evaluation of a loan application entails routine and tedious processes of analyzing an applicant’s financial history, which is time-consuming.
Realization Approach
With the help of workflow orchestration and integration with machine learning models, the evaluation of loan applications can be expedited via a series of automated steps based on a decision model.
Solution Space
Integrations with external machine learning APIs and services can enhance the prediction capabilities and intelligence of workflows by customizing the decision models for specific business processes and workflows. The result is a manifold improvement in process efficiency and reduced human effort for routine processes.
Featured Workflow Orchestration Solution
Camunda is a workflow orchestration platform offering a host of features and integration to automate business workflows across people, systems, and devices to continuously overcome complexity and increase efficiency. Their technology is powered by an advanced workflow engine that supports design, simulation and testing of business process and decision models.
This article explores the use of various integrations supported by Camunda, such as Amazon SageMaker along with other Connectors, like the SQL Connector and Slack Connector, to build a demo scenario for evaluating bank loan applications.
Evaluating Bank Loan Applications With Machine Learning
For a bank manager, a thorough evaluation of applicants for loan eligibility is crucial to making sound financial decisions. Leveraging machine learning algorithms can significantly enhance this evaluation process. Classification algorithms, such as Logistic Regression, are particularly useful for predicting whether an applicant is likely to qualify for a loan.
By applying these algorithms, it is possible to analyze various features of an applicant’s financial history, such as credit score, income level, debt-to-income ratio, and previous loan repayment behavior. Logistic Regression helps in modeling the probability of an applicant meeting the eligibility criteria based on these features.
Additionally, other classification techniques like Decision Trees, Random Forests, and Support Vector Machines can further refine predictions. These models can be trained on historical data to identify patterns, making more accurate predictions about future applicants.
Integrating these Machine Learning models within the bank loan assessment process provides a more objective and data-driven approach, potentially reducing the risk of defaults and improving overall loan approval efficiency.
For this use case, Amazon SageMaker is utilized to train a sophisticated machine learning model using historical data. SageMaker’s robust platform enables development and fine-tuning of the model effectively. Once trained, the model is deployed as a real-time endpoint, allowing for seamless integration into the demo loan assessment workflow.
Modelling and Implementation
The BPM (Business Process Management) modeling for this use case is straightforward and does not require complex modeling techniques. Once a loan application is submitted, a process instance is triggered. This instance then calls the inference endpoint created in Amazon SageMaker. The endpoint evaluates the applicant’s data and returns a classification indicating their loan eligibility.
Classification and Inference
The model classifies each applicant into one of four categories:
0: Rejection due to Low Credit Score
1: Rejection due to High Debt
2: Rejection due to Missing Information
3: Approval
The inference response, which could be 0, 1, 2, or 3, is then processed by the DMN (Decision Model and Notation). The DMN interprets the numeric response and outputs the corresponding classification type.
The DMN is represented as below:
Once the output from DMN is obtained, a conditional gateway would decide the route to Slack notification.
Based on the output from the DMN, applicants are notified of their application status. In this demo scenario, notifications are sent via Slack.
Use of Camunda for This Case
Camunda’s platform provides an efficient way to orchestrate and automate the loan application process without requiring extensive coding. By leveraging Camunda’s Connectors and BPMN (Business Process Model and Notation) features, machine learning predictions and decision-making workflows can be integrated easily.
Key Benefits:
- No Extensive Coding Required: With Camunda, most of the process orchestration can be configured using graphical BPMN models and built-in Connectors, minimizing the need for custom code.
- Integration with Amazon SageMaker: Using Camunda’s SageMaker connector, engineers can seamlessly call machine learning inference endpoints to classify loan applicants based on their data.
- Decision Automation: The DMN (Decision Model and Notation) can be used to automatically interpret the machine learning model’s output and determine the applicant’s eligibility status.
- Notification Integration: Camunda’s connectors, like the Slack/Whatsapp connector, allow for easy setup of notifications to communicate the decision to applicants.
Process Overview:
- Application Submission: A process instance is triggered upon loan application submission.
- Inference Call: The process instance makes a call to the Amazon SageMaker inference endpoint.
- DMN Evaluation: The response from SageMaker is processed by the DMN to determine the classification type.
- Notification: Based on the DMN output, a conditional gateway routes the process to send a notification via Slack.
This streamlined process leverages Camunda’s powerful workflow automation capabilities, enabling quick and accurate loan application processing with minimal manual intervention.
For more details on connecting Amazon SageMaker to Camunda, read the original article here.
