Reconciliation process occurs in every organization, and it can be of different types- bank reconciliation, customer reconciliation, vendor reconciliation, mutual fund reconciliation, and so on. It is the process of comparing all the necessary line items of a data source against multiple data systems or sources. This process assists in identifying the inconsistency or where the breaks occur in data sources so that it can be rectified by taking investigative action. Executing a reconciliation for a large set of data can be cumbersome, time-consuming, and error-prone if the process in place is not efficient.
One of the worst practices for reconciliation deployed in many organizations is to get it done through MS Excel. There are high possibilities of clerical error while entering data in Excel and a high risk of review error. One of the instances where an employee enters one of the line items in excel in white text for balance reconciliation, but it was invisible to a busy reviewer who was under pressure to meet deadlines. Such activity can have an adverse impact on the business and can lead to hefty fines and penalties.
We, at Algonox, came across many firms that are still utilising excel sheets or inefficient processes for reconciliation. To overcome the challenges associated with ineffective recon process, we have developed a reconciliation module- “REX Reconciliation” on our automation platform Algonox Cognitive Engine (ACE) based on Big Data Technologies.
It aids in the computation of large data sets within minutes, unlike excel sheets which take a fair amount of time for large data set processing. In case of an exception in the reconciliation process, our automation platform embedded with AI and Advance Analytics assists the business users in assessing the reconciliation issues and guides them to the root cause. With our Smart Workflows, business users can easily track each recon process via timestamps and audit trails providing robust control.
‘We excel in end-to-end automation and ensure that any process automation implemented is adoptable by the business users and complement them in increasing process efficiency to many-folds.’