Credit Agreement Digitization Solution

In less than 15 minutes, you can go from unstructured data to making informed decisions.

A global investment management firm has cut loan onboarding time by two-thirds with Eigen

Learn how a leading global investment firm uses Eigen to automate and accelerate loan onboarding and eliminate a number of data re-keying, cross-checking and approval processes.

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Using Eigen you can digitize your credit agreements and extract the data you require for loan onboarding, loan servicing and loan monitoring purposes including compliance and regulatory reporting. Eigen's Credit Agreement Digitization Pack includes pre-trained models with +100 data fields covering everything from covenant ratios to transfer provisions. And you can also easily build custom models to extract data from non-LMA or LSTA bilateral loan documents.

With Eigen, you can:

Automate the processing of information and insights from credit agreements with +98% data accuracy
Perform data remediation with human exception handling workflows based on your business needs
Easily extract, validate and download the information you need from LSTA or LMA-style agreements
Send the data to your downstream IT systems and databases such as LoanIQ or Salesforce via our REST API or integration partners
Reduce RWA and improve CCAR/stress testing compliance by collecting complete and accurate data

Download pre-trained fields

+90% OOTB accuracy. Benefit from superior accuracy with custom confidence targets and human exception handling workflows to further improve data quality.

+100 pre-built questions. Unlock immediate value with our pre-trained models. Easily add your own questions as required based on your business needs.

Enterprise-grade model risk management (MRM). Eigen's OOTB MRM framework allows for quick deployment and compliance with even the strictest processes.

Data export and integration options. Integrate Eigen with systems such as LoanIQ and SharePoint with our REST API or talk to us about your custom workflow automation needs.

How Eigen extracts data from credit agreements

Capture credit agreements

Ingest agreements from any source

Integrate Eigen with your email systems or document repositories to automatically detect and ingest new credit agreements as they arrive, or drop files straight into the user interface. Either way, you can easily and immediately extract over 100 commonly required terms from credit agreements.

Integrate Eigen with your email systems or document repositories to automatically detect and ingest new credit agreements as they arrive, or drop files straight into the user interface. Either way, you can easily and immediately extract over 100 commonly required terms from credit agreements.

Classify credit documents

Organize them by document type

Using machine learning, Eigen automatically classifies documents into their respective document types (LMA, LSTA, Amendment, Notice, Collateral Doc etc). This includes identifying and separating sub-documents contained within a single file (also known as deblobbing) in order to extract the appropriate data elements from each.

Using machine learning, Eigen automatically classifies documents into their respective document types (LMA, LSTA, Amendment, Notice, Collateral Doc etc). This includes identifying and separating sub-documents contained within a single file (also known as deblobbing) in order to extract the appropriate data elements from each.

Data extraction

Eigen understands context

Eigen understands the context around an answer, as well as the content of the answer itself, allowing it to extract the contextually correct instance of a particular term/phrase/text string from subsequent agreements. By taking both into consideration, Eigen develops a conceptual understanding of the information and leverages that to extract similar information from agreements it has not seen before, even if the same concept is worded differently.

The pre-trained models are specifically trained on LMAs and LSTAs to ensure data accuracy and quality.

Eigen understands the context around an answer, as well as the content of the answer itself, allowing it to extract the contextually correct instance of a particular term/phrase/text string from subsequent agreements. By taking both into consideration, Eigen develops a conceptual understanding of the information and leverages that to extract similar information from agreements it has not seen before, even if the same concept is worded differently.

The pre-trained models are specifically trained on LMAs and LSTAs to ensure data accuracy and quality.

Associate credit agreements

With parent-child mapping

Eigen extracts all tables within a document in combination with terms across amendments and related documents to give you a consolidated picture of provisions from umbrella agreements.

Eigen extracts all tables within a document in combination with terms across amendments and related documents to give you a consolidated picture of provisions from umbrella agreements.

Data post-processing

Act on your credit data

Customer-defined normalization and extraction rules can be applied post-extraction to standardize, clean, cross-reference or to compare data, among other things.

Customer-defined normalization and extraction rules can be applied post-extraction to standardize, clean, cross-reference or to compare data, among other things.

Credit data validation

Eigen flags high and low confidence answers

Eigen provides human-in-the-loop capabilities with the ability to review the original document and remediate the extracted data. Additionally:

Rules can be applied to any given extraction to validate data accuracy
Each extraction is flagged as high or low confidence based on configurable numerical thresholds
Users can leverage a manual remediation workflow to correct machine predictions, add comments or flag extractions for further review.

All changes are captured in audit logs with UI and API export functionality, and changes can also be used to improve your model.

Eigen provides human-in-the-loop capabilities with the ability to review the original document and remediate the extracted data. Additionally:

Rules can be applied to any given extraction to validate data accuracy
Each extraction is flagged as high or low confidence based on configurable numerical thresholds
Users can leverage a manual remediation workflow to correct machine predictions, add comments or flag extractions for further review.

All changes are captured in audit logs with UI and API export functionality, and changes can also be used to improve your model.

Data export and integrations

Data can be exported from Eigen in csv or xls format, or you can integrate Eigen directly with a variety of systems (like LoanIQ, SharePoint, Unqork, Xceptor, etc.) using Eigen's REST API.

Or, work with Eigen's Professional Services team to create an end-to-end workflow to extract and feed data from credit agreements to your upstream and downstream business applications.

Data can be exported from Eigen in csv or xls format, or you can integrate Eigen directly with a variety of systems (like LoanIQ, SharePoint, Unqork, Xceptor, etc.) using Eigen's REST API.

Or, work with Eigen's Professional Services team to create an end-to-end workflow to extract and feed data from credit agreements to your upstream and downstream business applications.

Data accuracy and model risk management

We take data accuracy and model risk management (MRM) seriously. As one of our core principles, we've developed our MRM framework to meet the needs of our enterprise clients. Eigen's three lines of defense ensure the data you receive and act upon is correct