Shreedhar Narhari
Automated Reconciliation
Manual reconciliation of data—a tedious and laborious process used to ensure that accounts are accurate and up to date—has been a necessary part of the accounting process for a long time. As data sources become more complex and data sets continue to grow, a manual reconciliation process is becoming increasingly more difficult, time consuming, and costly. Automating this process with the help of machine learning can provide organizations with efficient and accurate data reconciliation.
Utilizing machine learning in reconciliation automation improves accuracy and operational efficiency. Machine learning systems learn over time how to accurately identify patterns and anomalies within large datasets and can make both simple and complex determinations. For example, ML algorithms can identify false positives in data reconciliation, allowing an organization to quickly catch discrepancies that would otherwise be missed by manual reconciliation.
Data-driven Solutions
Data-driven organizations can use the speed and accuracy of machine learning for reconciliation automation. It takes time for manual data-reconciliation processes to sort and verify information from different sources. This can be compounded when dealing with larger datasets in fields such as healthcare, retail, banking, and government. Machine learning automation saves time and prevents errors by automatically deducing accurate results and providing detailed reports on discrepancies.
Furthermore, data-driven organizations can use machine learning to analyze unstructured data. A machine learning algorithm can ingest large amounts of unstructured data and extract important insights quickly. This allows for data-driven insights to inform decision-making across a wide variety of industries. Machine learning models are fast, accurate and easy to implement, which helps organizations stay ahead of the competition.
Automation Technologies
Organizations have been utilizing automation technologies such as robotic process automation (RPA) for many years to streamline back-office processes. RPA is a process that utilizes software robots to automate manual tasks, eliminating the need for manual processes in data-reconciliation as well. The software robots replicate the way a human would operate a system, allowing for rapid data-reconciliation processes and improved accuracy.
More recently, organizations have started to explore machine learning technologies to automate reconciliation. Machine learning robots can ingest large amounts of data, curate that data, and detect patterns in it. This can augment the existing RPA systems, making them even more efficient. Machine learning algorithms are able to detect discrepancies faster and more accurately than manual processes, drastically reducing the cost and time of reconciliation.
Automatic reconciliation using both RPA and machine learning technologies is transforming the way organizations handle their financials. Automating data-reconciliation eliminates the need for manual processes and allows organizations to quickly identify discrepancies, errors, or patterns in their data. The accuracy and speed of automated reconciliation processes, coupled with the detailed report generation, provide decision makers with the insights they need to make better decisions with their data.
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Shreedhar Narhari
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A single source of truth is a central and controlled source of data from which the entire organization can draw.
It is the master data.
When you lack such data and employee can pull down apparently the same metrics from disperse systems, unavoidably those systems will produce different numbers.
You get into a he-said-she-said situation, each player drawing and defending their position with their version of the ‘truth’.
Or even worse, some teams may naively leverage stale, low-quality, or otherwise incorrect data/metrics and make bad decisions, when they could have leveraged a better source.
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