Keeping business continuity in mind, organizations are looking for more efficient ways to manage the massive amounts of data from multiple, disparate sources, rapidly and effectively.
Fremont, CA: The year 2020 has been riddled with changes for all industries. Before the pandemic shut down life as we know it, there was a wave of digitization. Technological breakthroughs were driving new and innovative methods in every industry. 2020 was earmarked to be the start of the decade of data. This can be seen in the number of data banks, and other financial institutions are surfing through each day from various sources. The quantity and complexity of the data is increasing at a rapid pace. It is no longer adequate to rely on manual processes to analyze and interpret the data collected. Without a doubt, only those institutions that are equipped with the technology to handle the wave of data coming in will be able to survive this testing period and the competitive future of the industry that lies ahead.
The outbreak of the pandemic has only amplified the need for a resilient, connected system, and a more robust process. Keeping business continuity in mind, organizations are looking for more efficient ways to manage the massive amounts of data from multiple, disparate sources, rapidly and effectively. Ensuring ongoing data integrity is of prime importance. The bigger question is if it is possible to automate the most critical processes? If yes, what is the potential for machine learning technology to revolutionize how the operations are carried out?
Reconciliation, a control function that can protect firms from regulatory fines, financial loss, and in the worst case, failure of the entire business, is one of those mission-critical functions. However, the automation of reconciliation has always been a complicated process for most organizations. The Reconciliation Maturity Model (RMM) has shown some promise in recent years and is being leveraged by many companies.
The RMM draws on working on reconciliation best practices across a broad range of financial organizations from tier-one banks and global asset managers to smaller hedge funds and corporates. The model provides a practical and actionable roadmap in five steps for firms who would like to make the journey towards automation, consolidation and optimization of their reconciliation processes, eventually arriving at a self-optimizing model made possible by machine learning.
See Also: Top Reconciliation Platform Companies
See also: Top Machine Learning Companies
