Breaking Free from Loan System Constraints in Notice Processing

This is the second in a series of posts on how Alkymi is transforming loan data workflows. You can see the first post here:  The Data Bottleneck in Private Credit is an AI Problem.

Loan Agent Notice Processing Shouldn't Depend on Your Loan System

For credit operations teams, loan agent notices are where the data lifecycle begins. Every rate reset, principal paydown, and interest payment arrives as a notice, and that data needs to flow quickly and accurately into the systems and tools your team actually uses: Aladdin, AllVue, Geneva, WSO, PAM, Charles River IMS, Snowflake, DataBricks, SimCorp, Clearwater, or whatever combination your firm runs.

The problem is that most firms are relying on their loan system to handle the data lifecycle, yet most of these platforms only handle it partially. If you’re running WSO, you have solid automation for WSO-generated notices. But your portfolio, though, isn’t composed solely of agents using WSO. The rest - Alter Domus, GLAS, LoanIQ, various white-label solutions used by major US & EMEA banks – still land in your team’s inbox and require manual processing.

Notice formats in practice

These aren’t just minor differences in layout, but materially different formats that require tailored approaches to identify the same underlying data. Here are some sample formats that highlights the data complexity facing credit ops teams:

• A large European bank sends notices with a structured remittance section with clean banking details. Then, a block of all-caps free text at the bottom overrides the remit instructions entirely, PIK interest has been added to the loan balance, and no wire is forthcoming. The structured data is incomplete, with the real instructions in the prose. Additionally, the amounts follow European number conventions, with periods as thousand separators and a comma as the decimal.

• Several major US banks send notices as plain email text. No PDF, at best rough tables using tabs and spaces. Yet the text is formulaic, with clear label/value pairs, and a standard header indicating the notice type.

• Alter Domus notices are clean and well-formatted for both the human and machine eye. A summary block at the top introduces the loan’s global and lender balances, followed by a structured interest payments table below.

• WSO takes a similar approach, but with different layout choices: two columns at top with deal metadata, and a lighter contracts table for the financial detail.

• GLAS is the most structurally dense. Nested facility-level subtables for both Global and Lender share, eleven columns per row, one row per principal balance/period. Everything is present in explicit technical detail, but in a nested structure that breaks systems that only handle flat layouts.

From initial processing to downstream integration

Format coverage matters because the data has to go somewhere. ‘Somewhere’ may mean portfolio systems like Aladdin, Allvue, PAM, or Geneva, a datalake/warehouse like Snowflake or Databricks, or a combination of these platforms and in-house solutions. Regardless of the destination, getting notice data into those systems requires clean, structured handling first.

This is where a lot of WSO-centered workflows hit a wall. WSO works well for WSO-format notices. For everything else, especially in diversified portfolios, that’s a significant share of volume, the data ingestion is manual, which means the downstream posting is often still manual, too.

Alkymi is different. We identify 100+ client-tailored fields regardless of format: borrower, facilities, rates, amounts, dates, banking details, and more. That includes cases that come up more often than you’d expect, like non-US number formatting and free-text overrides that contradict the structured data. The output is standardized and ready to push downstream to whatever system your firm uses today.

Connect with us to see how this works against your specific notice formats and loan portfolio workflow.

See Alkymi in Action