Edge Analytics - Insights #Risk ManagementEdge Analytics - Insights #Risk Managementhttp://edgeanalytics.zohosites.com/blogs/tag/risk-managementThu, 30 Apr 2026 07:36:02 -0700http://zoho.com/sites/<![CDATA[Achieving ROI through ModelOps (Part 1)]]>http://edgeanalytics.zohosites.com/blogs/post/achieving-roi-through-modelops-part-iMarket demands aided by advanced internal and external risk factors are forcing businesses to be more rapid, iterative, reliable and collaborative when executing on transformation initiatives.]]>

Market demands aided by advanced internal and external risk factors are forcing businesses to be more rapid, iterative, reliable and collaborative when executing on transformation initiatives. The client’s focus was to provide a governed framework to allow for continuous model development and deployment.  

CLIENT

Large financial services company

REGION

Africa | South Africa

SOLUTION

Automated model deployment

HIGHLIGHTS / OUTCOMES

Decrease in model development time and costs

Effective and efficient scaling of RegTech

Greater uplift in use case delivery increasing direct ROI


THE CHALLENGE


The business was experiencing a multitude of issues that included:

  • A lack of existing capability to deploy models
  • Technical constraints encountered in concluding upfront and ongoing analysis
  • A lack of structured and complete data
  • An undefined pattern library and ability to govern the model risk lifecycle 

BUSINESS OBJECTIVES

  • Faster time to deploy models through developer self-service
  • Smaller technology footprint by managing only the resources and tools required
  • Greater flexibility and capacity in supporting the deployment

SOLUTION IMPLEMENTED


The solution involved implementing a digital model lifecycle leveraging Cloud technology and a suite of applications to manage and monitor model development. This included the development of an execution environment that would serve for model CI/CD, while incorporating a best practice architecture as well as security and data services.


BUSINESS BENEFITS REALISED


The benefits to be gained were numerous, however these were the benefits immediately identified:

  • 50% decrease in model development time
  • 25% decrease in IT and Business costs
  • Easier Python code deployment given the modelled risk management use cases
  • Standardisation across the governed model deployment lifecycle 

“Deploying and operating containers at scale requires the coordination of infrastructure, 

management tooling, and operational practices.” 

DOWNLOAD A PDF OF THE CASE STUDY HERE
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