Eigen's NLP platform enables you to extract data points from bonds that are relevant for your specific framework including maturity dates, call option and redemption provisions.
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Eigen’s highly tailored NLP platform lets you quickly cut through the complexity of matching adjustments so you can better manage your bond portfolio.
APPLICATIONS
Run through every bond in your current portfolio to confirm eligibility and compliance
Quickly identify from the universe of available bonds those that are eligible for inclusion in your portfolio
Maturity date
Currency
Call option
Question Topics That Can Be Answered
Redemption provisions
Customizable fields
Eigen's platform helps you speed up the process of identifying bonds that meet your specific matching adjustments criteria.
By using machine learning to process more prospectuses, you can determine eligibility for all available bonds - rather than just those you are familiar with - to find the best returns.
Eigen also helps ensure compliance with your home market regulatory framework by customizing the analysis to find bonds that match the cashflow of your products.
Get up and running immediately with Eigen Cloud and the support of our Client Success team.
FAST FACTS
5-10x
faster than manual processing alone
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EIGEN FOR SOLVENCY II
Quickly scale up the identification of bonds that meet your criteria for matching adjustments
Other use cases
Eigen enables clients to focus their time on making faster, more informed decisions, instead of analyzing pages of documents.
Below are just a few of the use cases Eigen can help your business tackle.
Quickly identify key facts about CLO tranches that enable you to build risk and return profiles without reading 400+ pages yourself.
Identify linked instruments and their fall-back and transition processes across a myriad of diverse contracts to mitigate your prudential risks.
Automatically identify governing law, cross default rights, and restrictions on the transfer of credit to ensure compliance.
Automatically verify terms to speed up negotiations and ensure finalized drafts reflect the individual terms prior to signing.
Reconcile data of back book across multiple systems and create a single source of truth for the entire loan life-cycle.
How Eigen works
Users upload a handful of prospectuses and label the relevant data fields for extraction. Eigen uses this information to build a machine learning model.
The model then analyzes all new prospectuses to retrieve the correct data points. The extracted data is exported or sent to other systems via APIs.
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