Using enhanced data analytics to deliver
data that can be trusted
The global markets division of a major European investment bank engaged us to deliver a comprehensive account certification system. They faced increased regulatory scrutiny, a tight deadline and multiple, inconsistent data sources.
Industry: Investment Banking
Designing a set of tests
To mitigate the risks brought on by multiple data sources and to compensate for missing or inconsistent data, we devised a comprehensive set of repeatable tests to validate data sources.
We began by identifying and sourcing all relevant data to support the account certification tests, and then used an analytics engine to load and normalise the data.
The tests consisted of primary and secondary categories of checks.
- Primary checks were conducted on client account and entity records.
- Secondary checks leveraged additional internal sources of data, such as credit and traded risk hierarchies to re-validate the primary checks.
Exact & partial matching
- Exact name matches were awarded maximum scores.
- Partial matches scoring 70% and above were awarded a higher score than those with 50% and above.
- Anything below 50% were awarded zero or negative scores.
- The credit and risk checks determined the legitimacy of the entity and client account relationship.
- These internal checks matched entity and account information to the credit and risk hierarchy – if the match was valid then scores were awarded as normal.
- If the internal checks revealed inconsistencies, all other tests would be invalidated and the entity would not be account certified.
Confidence in the scores
- Scores from all tests were totalled and graded to ensure the required level of confidence in a match.
- Anything below the required value would be sent to the review team for investigation, and a request for additional data from clients to allow certification.