How RPA reduces Banking Reconciliation Processes time by 50%
A typical reconciliation process time is significantly reduced to a few hours through RPA reconciliation solutions, which also minimizes risk by reducing manual intervention in matching, validation, and exception management processes. Learn more here.
Karthik Kamalakannan / 18 March, 2020
The most important tasks today happen online with technological access to every nook and corner of the world. Reconciliation landscape is not left behind, which is evolving with Robotic Process Automation (RPA) and the like, aimed at cost reduction and maximum optimization.
Reconciliations were introduced as key controls in operational processes, yet seemed to have spawned beyond this. Increased regulatory scrutiny, larger amounts of data, and increasingly complex financial products have led to operational departments having to operate hundreds of reconciliations daily, extracted from Journal Automation paper series in Financial services by Henley Business School and CAPCO Institute.
Reconciliation is done to check and ensure that two or more data sources match, without any discrepancy. In financial institutions, there are multiple types of reconciliation including front office to back office (FOBO) reconciliation for books and records platform, exchange reconciliation for trade, position, and cash records, Nostro reconciliation for Nostro bank accounts, General ledger for reconciliation between general ledger and sub-ledger, back office to back office (BOBO) reconciliation, intersystem reconciliation for data integrity and completeness, and Trading: Total Equity reconciliation between central banking clearing and a broker.
Typical bank reconciliation involves accessing bank records and software, updating uncleared cheques and deposits in transits, entering new expenses and bank balances, reviewing reconciliations, investigating undocumented variances, and adjusting immaterial items.
Here are the advantages of RPA-powered Bank reconciliation processes, which also eliminates many challenges in accounts reconciliation process including duplicate entrie and time and date discrepancies.
With the use of Natural Language Processing (NLP) with intuitive User Interface (UI) and rule-based automatic configurations, it's like shooting fish in a barrel even for non-technical users, as they no longer have to rely on IT support for reconciliation.
Not only is the complex intersystem reconciliation eliminated, but errors are also captured and corrected before they impact any calculations or downstream processes.
Blockchain has all the bells and whistles for technological transformation. Each link in the blockchain validates and ensures the data has not been altered, and the accuracy is verified through consensus validation, leading to a single immutable representation of data.
DLT increases fault tolerance and avoids a single point of failure, centralized operating risk, and risk and accusation of the central data store owner of manipulating the data.
The application of Artificial Intelligence Techniques helps in predicting the appropriate action through heuristic techniques via rules, classification techniques by taking user actions as a training set of data, and clustering techniques by identifying patterns.
It takes an average of 64 days to onboard a new reconciliation and more than three-quarters of this time is typically spent on analysis and building, according to Aite Group – Reconciliation Centers of Excellence: An Assessment of Quality. This time is significantly reduced to a few hours through RPA reconciliation solutions, which also minimizes risk by reducing manual intervention in matching, validation, and exception management processes.
Last updated: November 21st, 2023 at 6:56:16 PM GMT+0