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Case study

Enabling a comprehensive risk assessment of asset-backed securities

A US-based investment management firm uses Eigen to build robust datasets that enable them to comprehensively assess the risk profile of their portfolio.

The challenge

CLO prospectuses are long, cumbersome documents. But if you’re looking to trade CLOs, or need to know how to value the ones you already hold, they’re required reading.

Compared with other asset classes, CLOs present two challenges: relatively limited amounts of data to feed into an investment strategy, and the fact that that data is time-consuming to source manually.

For CLO investors going through periods of volatility, this has historically meant making decisions with limited information about risk. Eigen changes that.

Our client, a US-based investment management firm, needed to quickly understand the risk profile of its CLO portfolio in order to make investment decisions.

The solution

Eigen’s NLP platform enabled investors to extract a complete CLO data set seven times faster than a manual process alone.

With Eigen, investors can visualize a small set of CLO prospectuses and label the fields they want to extract. These labelled fields form a machine learning model that can then be used to extract data from any prospectus.

In minutes, a prospectus becomes a series of discrete textual data points.

These data points can then be combined and manipulated to codify an investor’s unique approach to risk and return.

The results

Eigen enabled the firm to analyze CLO prospectuses seven times faster than before, yielding a data set five times larger than manual processing alone.

These robust data sets allowed the firm to comprehensively assess the risk profile of its portfolio.

Armed with this information, the firm has been able to make quicker decisions in response to changing market conditions.

7X Faster analysis of CLO prospectuses
5X More data points gathered and analyzed
25% Increase in accuracy and quality of data