Since its founding in 1929, Lord Abbett has maintained a singular focus on the management of money. As an investment-led, investor-focused firm, Lord Abbett evaluates every decision from an investment perspective in an effort to achieve superior, long-term investment performance on behalf of its clients.

Ratings Retrieval Process

Daily process to fetch, normalize, validate and store ratings provided by many different rating agencies. This metadata driven tool allows users to easily select the connection protocol suitable for a given data provider.

Supported Protocols

Client Location
New York City
Project Team
3 - 5 Software Engineers

The lack of the standardized data format shared between data providers required metadata driven engine which can be easily tuned to the current needs. Among others, users can configure:

  • how to normalize data and how to recognize the not rated securities
  • different strategies for handling data updates (e.g. daily patch or monthly full universe update)
  • auditing strategies to comply with government regulations

Case Study: Normalization of ratings data across vendor API’s

To satisfy the need for wider rating coverage and more frequent updates our team was asked to replace an existing rating component with a stand alone tool.

After initial investigation, it turned out that each rating agency exposes data via a different protocol and in a different format. Except for that, they differ in a frequency of data releases and handling of the data updates. Some, publish full data universe on monthly basis with daily updates and others publish full data universe on each major update. To cover all corner cases we familiarized ourselves with data dictionaries and API specifications provided by each agency.

The lack of the standardized API shared between the data providers required a very flexible metadata driven tool which can be easily tuned per provider and can ensure that all the data will be stored in a consistent way.

Our tool allows users to easily configure the connection protocol, data normalization algorithm and the way of recognizing not rated securities. To address the inconsistencies in the data release frequency, we implemented various data update strategies (e.g. daily patch or monthly full universe update) that could be easily combined via the configuration file.

The single standalone tool let the client set up a robust ratings retrieval process that handles all the rating data in a consistent way and audits it in order to comply with government regulations regardless of the rating provider.

Ready to develop your vision?

Click the button and let's do it together.

Contact us