Case study: Text analytics - fuzzy matching


Pain Points

Difficult to reconcile information from bank statements to the accounts receivables due to volume and inexact names


Automation and text analytics

Result/ Benefits

Improved accuracy and time-saving

Pain points

Our client has numerous customers that corresponded to a long list of accounts receivables (“AR”) entries in the accounting module. Upon presenting the invoices, the customers will make payments which will be reflected in our client’s bank statements.

It was challenging and time-consuming to match the payments from the bank statements with the specific AR entry as the names presented in the bank statement were not standardised, and often did not completely match the ones in the accounting system.


We developed a programme to automate the matching of the bank statements with the AR module. 

Additionally, we applied fuzzy string matching techniques that performed an approximate match of the descriptions stated in the bank statements with the official name listed in the accounting system. 


With the deployment of this system, staff no longer need to manually match the bank records and match it with the accounting entry before updating it. The solution can reconcile information more accurately and quickly, leaving the staff to concentrate on higher-value work.



Our Data Science team, comprising a group of specialists with multi-disciplinary skills, helps our clients make better strategic and operational decisions by turning data into actionable insights.

For more information, contact:

Adrian Tan
Partner and Industry Lead for Technology, Media and Telecommunications
T +65 6594 7876
[email protected]