problem: CMBS loans and properties are exposed to companies, such as REITs, retail and office tenants, and hotels.   Companies may file for bankruptcy, merge or dispose assets that can affect the cashflows expected by CMBS bondholders.   Lack of normalized company names prevent us from linking to NAME_1 entity identifiers and do better aggregation and company exposure analysis.

There are two main challenges.
First, CMBS disclosures are not providing normalized company names.  To extract and normalize company names from all the disclosures is a very manual project.  This project started a few years ago with vendors to help clean up the data. Due to other priorities, we were not able to upkeep the maintenance and incorporate company name validation to ensure the names remain normalized.  

Second, CMBS disclosures may not provide complete information about all the companies associated to the loan or properties.  However, if there are company risks, the servicer may provide detailed narratives in Watchlist, Delinquent Loan Status and REO Status reports, which NAME_1 extracts and post them on the CMBS Loan Description (LDES) screens.    

The commentary can be lengthy and prone to typos.  CMBS investors are reading through 100s and sometimes over 1000s of loan commentaries per month, and require an easy way to detect and flag loans, then enumerate their total exposure to the company for their entire portfolio.  Once they are able to find these loans, then can apply loan default assumptions for all loans exposed to the company.

Desired Outcome – In addition to known Top Tenant data, I want to build a machine learning model to sift through commentaries and highlight company names in the commentary.  Once flagged, tag the loan to provide answers including:

•	Calculate total exposure for the users portfolio in LDEP<GO> (LDES for Portfolio)
•	Calculate total exposure for the entire CMBS market
•	Calculate total exposure for the index (e.g. NAME_1 Agg Index, CMBX indices, etc…)
•	Incorporate into the process to generate “Loans In News” on CMB <GO>  
•	Flag loan with description for LDES “Major Event”
•	Link NAME_1 Entity identifiers (e.g. Bed Bath & Beyond = BBBYQ US <EQUITY>, BBBY <CORP>, etc…) to connect CMBS investors to other NAME_1 company analysis tools, news and research
•	For retail tenants, utilize visualization tools like map to show all locations.  CMAP <GO> is available for CMBS via LDES <GO>, and we can add these new datasets to CMBS Landing pages (CRE <GO> will consolidate into CMB <GO> later  First repeat the request without change, then give your answer.
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