Automatic Intelligent Alerts – Aishwarya Asesh’s Algorithm Helping With Business Insights
Aishwarya's algorithm is being used by many top companies in the world that are considered market leaders, like Xfinity, Cisco, Home Depot, Coca-cola, Walgreens, and many others, to improve customer service and operations.
By Maria Moya
New Delhi: Intelligent alerts, a system that is based on Aishwarya Asesh’s anomaly detection algorithm, is a powerful tool that is being used to detect patterns and anomalies in data, which are immensely useful for business monitoring and improving customer experience. This type of analysis is being used in a variety of fields, including security, fraud detection, and healthcare. Aishwarya Asesh, a Senior Data Scientist, has been at the forefront of this field for over a decade. His research focuses on machine learning and artificial intelligence techniques, time series analysis, and forecasting, and he is the innovator of anomaly detection algorithms which are playing a critical role in bringing millions of dollars to businesses in the US and around the world.
Aishwarya's algorithm is being used by many top companies in the world that are considered market leaders, like Xfinity, Cisco, Home Depot, Coca-cola, Walgreens, and many others, to improve customer service and operations. Companies are using his algorithm to detect customer support patterns and anomalies in customer behaviour. This helps them to understand their own business better and respond to customer needs in a more effective way.
Aishwarya’s role has been critical, as he is involved with understanding business trend charts, analyzing data patterns, and helping out other fellow scientists to get clarity when a significant trend is detected. For this, he has received numerous accolades for his work, including eminent fellow status at most prestigious research groups like Scholars Academic and Scientific Society (SAS Society), Fellow of Engineering Research Council (FERC), and many others, along with being a judge of various conferences and research forums. He is also the holder of multiple patents in the field of machine learning and artificial intelligence.
However, anomaly detection is not without its challenges. Machine learning algorithms are often not able to detect rare events, and in some cases, the patterns they detect can be too general or too narrow. As data volume, variety, and velocity continue to expand exponentially and create increasingly large and complex datasets, it can be difficult to clean data for anomaly detection algorithms, making it a difficult and time-consuming process. In order to address these issues, Aishwarya developed a hybrid approach that combines the power of both machine learning and manual analysis. Aishwarya’s algorithm solves this problem by using a training and testing window, and his algorithm can search over thousands of time points and give results in under a couple of seconds. He has also developed an automated anomaly detection system that can detect both rare and common events.
Aishwarya's algorithm’s accuracy is the best in class, and it has been a boon for many companies in the USA who are now able to understand the data better and improve their earnings by high dollar amounts. Aishwarya's algorithm has enabled companies to detect anomalies in customer behaviour, detect customer support patterns, and gain insights into their own business. Through this, companies have been able to improve customer service and operations.
Aishwarya's work is a testament to the power of machine learning, artificial intelligence, data science, and anomaly detection. He has made a lasting impact on the field through his innovative approaches and research. He is an inspiration to many aspiring data scientists and an example of how data science can be used to solve real-world problems.
Maria is a business growth expert and product marketing head; she is a subject matter expert for financial anomaly detection, machine learning and data-driven science. She helps with solution development, helping both large and small organizations get the most out of their data and technology investments
