The problem

An underwriting agency with a few dozen different brands had a real problem matching premium payments from agencies with the open items in their accounts receivable systems. Their problems were further compounded by an ongoing project to replace multiple underwriting systems with a single underwriting system.

The managed functions that solved the problem

EQ8R prepared two managed functions for them. The first function converts PDF and Excel remittances to a data format that can be matched with their open items. The second function uses a custom machine learning algorithm to match the remittance data with their open items.

What made these processes well-suited to managed functions?

Each managed function solves a challenging problem that would have been very expensive and time-consuming to solve using a different approach.

1. Converting PDF & Excel remittances to data

This is a non-trivial problem because the PDF remittance data can arrive in many different formats. Each broking system uses a different format and different terminology to refer to the same concepts such as policy numbers, effective dates and insured names. The Equator managed function extracts the tables from each PDF/Excel formats and converts it to data.

2. Matching remittance data with open items in the insurer's accounts receivable system

This is also a non-trivial problem because an insurer may have thousands or tens of thousands of open items in their underwriting systems. And the information on the remittances may have no common values with items in the underwriting system. The broker may  use a different format for policy numbers, may use a different name for the insured and may be part-paying the premium.

One of Equator's key strengths is its ability to match disparate data sets. In fact, the name Equator comes from its ability to equate one data element with another data element using machine learning.