MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641090923 A) filed by Tammineni Santhosh Kumar; Akhil Ginna; Dr. Nalabalapu Suman; Revati Anawardekar; Mahesh Shivaji Vandkar; Sagar Talluri; Dr Karthikeyan M V; Dr. Hemkant Nivrutti Gawade; Dr. Ganesh Raosaheb Patil; Ms. R. Punitha Gowri; Dr. Vivek Anil Bale; and Dinesh Sambhaji Gore on July 27, 2026, for Machine Learning-Based Predictive Analytics Framework For Business Economic Performance And Financial Stability Assessment.
Inventors include Tammineni Santhosh Kumar; Akhil Ginna; Dr. Nalabalapu Suman; Revati Anawardekar; Mahesh Shivaji Vandkar; Sagar Talluri; Dr Karthikeyan M V; Dr. Hemkant Nivrutti Gawade; Dr. Ganesh Raosaheb Patil; Ms. R. Punitha Gowri; Dr. Vivek Anil Bale; and Dinesh Sambhaji Gore.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: Newer business software developments allow firms to maintain track of day-to-day accounting transactions, manage inventory levels, or even produce electronic financial reports relatively easily. However, even today, conventional systems will assess the health of a business in terms reliant on quarterly declarations from the past and timeless balance sheet metrics. The very techniques fall short of capturing rapid, real-time movements in operational performance or volatility in markets or abrupt cash flows squeezes so lenders and business owners remain oblivious to stronger signals about emerging risk only until financial distress has set in. Embodiments of the present invention address these problems by forming a continuous, machine learning-based working model that integrates live operational and accounting data into a single landing area. The system pulls relevant data from bank feeds, sales records, and market indicators--cleaning the raw information in real time instead of waiting for late financial reports. It employs a smart feature extraction engine that computes dynamic indicators, including a Business Stress Index and a Financial Stability Index which register operational stress or cash burn as they occur. These indicators are fed to an ensemble predictive model that identifies statistically insignificant but non-linear relationships as well and predicts any financial distress months in advance. Also contained in the system is an attribution layer that explains precisely which of the many attributes being measured are leading to a higher risk score, along with a simple alerting engine that recommends actionable solutions, such as reducing client payment terms or removing unnecessary discretionary spend. The outcome is an early-warning risk radar giving enhanced financial foresight with reduced surprise insolvencies and a tool managers can use to confidently protect long-term stability. FIG.1
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