Credit Risk Transformation

Modernization

The Market Data, Trade Data and Reference Data used to build these feeds and valuations are collected from various golden sources. The market data keeps changing during the course of the day – this makes capturing a snapshot of the market data in the current system difficult - this in turn impacts the ability to generate risk feeds and valuations for decision making in a timely manner.

"Application Modernization for a Credit Risk Valuation and Feed generation system."

Solution

  • Use Data Services to help reduce time to load data from Golden Sources. Dedicated services interact with Data Sources to constantly fetch data from data sources and version them.
  • Since upstream systems cannot preload market data based on the previous days feeds, snapshots of these market data are used for Risk Calculation to create an accurate Risk Valuation and Risk feed.
  • This is done based as a solution leveraging Microservices.

Technical Solution

  • Microservices are now responsible for fetching Market data from various sources. During peak time, Microservices can be automatically scaled to handle the work load and scaled back during off peak hours.
  • The system built is Cloud ready and deployed on a Private Cloud Platform (RedHat OpenShift).
  • Built an Analytic Engine for breaking a large book of trade into separate tasks – process these tasks gathering information from various sources and provide a timely Risk Calculation.

Technical Implementation

  • DevOps and CI/CD principles for zero down time for deployments.
  • The Atyeti team designed an event driven architecture for the subscription and registration of data.
  • The Risk calculation process was refactored out of the application and built as an independently deployable and scalable unit.
  • Build the application Cloud ready and Cloud Agnostic.

Microservices

  • Built the application using Microservices for Scalability, Resilience and Fault tolerance.
  • Using MongoDB ( NoSQL DB) to store and fetch data resulted in faster retrieval in msecs.
  • Microservices deployed in OpenShift containers.
  • Event Driven design using EMS.
  • Deployed CI/CD using Jenkins.
  • Deployed Industry standards and best practices in developing Cloud ready solution.

Core Technologies

  • OpenShift (Docker/Kubernetes)
  • MongoDB
  • .Net Microservices
  • Splunk
  • Prometheus
  • Grafana
  • TIBCO EMS

Result

  • The Application can now generate Risk feeds in near Real- Time and is one step closer to support T0 processing and Intra-Day Risk valuation.