Suhas Sreehari

Title: Suhas Sreehari: Innovator in Financial Crime Detection

Introduction

Suhas Sreehari is an accomplished inventor based in Berkeley, CA. He has made significant contributions to the field of financial crime detection through his innovative patents. With a total of 2 patents, Sreehari is recognized for his expertise in applying machine learning to predict financial fraud.

Latest Patents

Sreehari's latest patent focuses on "Network based features for financial crime detection." This approach utilizes both network and non-network features to train a predictive machine learning model aimed at identifying financial crimes and fraud. The model generates graphical network features by applying financial entity risk vectors to a network model that represents various types of networks. These networks may include transactional, non-social, and social networks, with edges that are weighted based on characteristics such as frequency, amount, type, and recency. The predictive model uses these graphical features to assess the likelihood of financial crime, generating alerts on computing devices when a potential crime is detected. The alerts provide insights into the financial entities involved and present graphical representations of the relevant networks and linkages.

Career Highlights

Suhas Sreehari is currently employed at Wells Fargo Bank, N.A., where he continues to develop innovative solutions for financial crime detection. His work is instrumental in enhancing the bank's capabilities in identifying and preventing fraudulent activities.

Collaborations

Sreehari collaborates with notable colleagues such as Agus Sudjianto and Wayne B Shoumaker, contributing to a dynamic team focused on advancing financial technology.

Conclusion

Suhas Sreehari's contributions to financial crime detection through his innovative patents highlight his role as a leading inventor in this critical field. His work not only enhances the capabilities of financial institutions but also plays a vital role in safeguarding against fraud.