Data matching without the effort
A simple 1-2-3 no-code process drives your data quality improvements.
Flexible imports ( CSV, XLSX, SQL, API ), matching ( multi-column, tolerances, auto-config ) and working within your existing processes ( breaks, matches, adjustments ) Manage your risks - missed payments (in or out), fraudulent transactions, broken integrations or systems.
CloudSinc brings you state-of-the-art technology to simplify and accelerate your reconciliation processes. We auto-configure for many data sets and are adding machine learning to let you do more with less effort.
Basic
Basic matching example The simplest matching task is probably checking if values or rows in one ...
Rec early, rec often
“Rec early, rec often” In recent years two major shifts in thinks have occurred in the software ...
Machine Learning
The use of machine learning techniques to automate and drive efficiency in business processes is ...
Match values
Value checking example One step up from the basic transaction or existence matching example (ie ...
Two sided match
Multiple systems can have incomplete data. While the simple case for missing records involves on...
Cash
Cash reconciliations or matching, is the term we use to describe situations where no unique trans...
Structured
Explicitly structured The vast majority of data matching and reconciliation tends to focus on fla...
Versioning
When performing ad-hoc data matching, the matter of versioning typically comes up. CloudSinc has ...
Fragmented Data
Some businesses run on an enterprise platform (eg an ERP like SAP or Oracle apps), where the sof...
STP and the reconciliation quandary
Hands-off When I started working in software 25 years ago, the mantra was “Straight Through Proce...
Multi-pass
Some data matching tasks don’t succumb to a single matching condition. In these cases, it’s commo...
What are recs good for?
The business usage of data matching and reconciliations (or “recs”) are surprisingly varied. The...
