Genpact has been granted a patent for a method and system that uses machine learning algorithms to analyze financial data, determine compliance with applied algorithms, and identify anomalies. The method includes receiving data, applying algorithms, and using a classifier to classify outcomes as compliant, potentially non-compliant, or non-compliant, enabling real-time anomaly detection. GlobalData’s report on Genpact gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Smarter leaders trust GlobalData
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
According to GlobalData’s company profile on Genpact, was a key innovation area identified from patents. Genpact's grant share as of May 2024 was 86%. Grant share is based on the ratio of number of grants to total number of patents.
Anomaly detection method using machine learning algorithms for financial data
A recently granted patent (Publication Number: US11954738B2) outlines a method and system for anomaly detection in financial data using a combination of dynamic algorithms and machine learning (ML) classifiers. The method involves receiving financial data, applying a set of dynamic algorithms to generate outcomes, and using an ML classifier to classify these outcomes as algorithm compliant, potentially algorithm non-compliant, or algorithm non-compliant. The classifier is trained to identify latent anomalies in the financial data by analyzing a variety of outcomes simultaneously. The system includes a processor and memory with instructions to carry out the anomaly detection process, with the classifier capable of mimicking user actions for customized classification.
Furthermore, the patent details the use of ML to derive dynamic algorithms, hierarchical classification for hierarchical financial data, and algorithms based on various factors like differences between actual and budgeted values, temporal properties, ratios, and causal relationships within the financial data. The system is designed to display classifications in a hierarchy, providing a comprehensive overview of potential anomalies. By utilizing persona-driven actionable triggers, the classifier can adapt its classification process to mimic specific user behaviors, enhancing the accuracy and relevance of anomaly detection in financial data. Overall, the patented method and system offer a sophisticated approach to anomaly detection in financial data, leveraging dynamic algorithms and ML classifiers to identify and classify anomalies effectively.
To know more about GlobalData’s detailed insights on Genpact, buy the report here.
Data Insights
From
The gold standard of business intelligence.
Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
