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Apply machine learning to financial risk management


Machine learning is transforming all areas of business, including the way in which financial institutions and other industries are approaching tighter compliance requirements and risk management. This developer journey shows you how machine learning on IBM z/OS is deployed for a financial risk model to determine customer credit worthiness. You’ll learn how the results are displayed using an API, enabling you to incorporate the data into business applications.


Financial institutions need to continually weigh the risks of their transactions, and they determine their risk level through credit scoring. Leading up to the 2008-09 financial crisis, almost all large banks used credit scoring models based on statistical theories; that crisis, largely brought about by underestimating risk, proved the need for better accuracy in their scoring. The combination of increased requirements and the development of advanced new technologies has given rise to a new era: credit scoring using machine learning.

Machine Learning for IBM z/OS gives organizations the ability to quickly ingest and transform data. They can now create, deploy, and manage high quality self-learning behavioral models, using large corporate data sets residing on IBM Z. This risk assessment and management takes place securely in place and in real time, and helps financial institutions more accurately determine credit worthiness and other business needs.

In this developer journey, you will use a financial risk management model that’s been designed and deployed in a large banking system to approve or deny a loan according to input parameters. You will discover and test a financial risk management API and then create and extend a financial risk management application based on Machine Learning on z/OS. By completing the journey, you’ll discover how machine learning can be used in applications to deliver accurate financial risk management.


machine learning risk management flow

  1. The user calls the financial risk management API through the financial application. This API is published in a secure API Connect server, hosted in the public cloud (IBM Cloud).
  2. The financial risk management API calls the financial risk management system through the IBM Secure Gateway service, meaning that a Secure Gateway server has been set up in the public cloud, and a Secure Gateway client has been set up in the provider cloud, in front of the mainframe in a virtual IBM DataPower® Gateway. According to a configured access control list (ACL) file, the DataPower Gateway authorizes or denies the incoming request.
  3. If the request has been authorized by the virtual IBM DataPower Gateway, the financial risk management system (that is, the machine learning scoring service) is called through a REST/JSON interface, wherein a predictive model has been deployed. This model returns a score representing a loan approval (probability of the capacity of loan refund for a banking customer).


Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.

  1. Start with the API Developer Portal.
  2. Subscribe to the Financial Risk Management API.
  3. Work with the Financial Risk Management API.
  4. Download and review the financial application code.
  5. Run the financial application.
  6. Start with Node.js on Cloud.
Alexis Chretienne
Yann Kindelberger
Nora Kissari