Ashwin Pingali

Title: Ashwin Pingali: Advancing Knowledge Graphs for Identity Fraud Detection.

Introduction

Ashwin Pingali is an inventor based in Parker, Colorado, whose patent work focuses on applying knowledge graphs to identity verification and fraud detection. His inventions describe ways to analyze information from identification images and transactions to identify patterns that may indicate fraud. Pingali is associated with Jumio Corporation. His listed portfolio includes 2 patents.

Latest Patents

**System and Method of Knowledge Graph Based ID Anomaly Detection in Order to Determine if the Anomaly Is Associated with ID Fraud.** This invention describes a system for detecting anomalies in images of identification documents. It creates image fragments and evaluates them through a workflow, using a knowledge graph schema to define nodes and relationships. The results can be compared with a gold standard image, while human-curated knowledge can help identify patterns that may signal potential identity fraud.

**Knowledge Graph Generation for Contextual Decision Making.** This invention describes generating knowledge graphs from data fragments associated with transactions. The fragments are represented as nodes, connected according to their relationships, and clustered to identify patterns. Contextual information captured in those patterns can support decisions, including reversing decisions when the analysis indicates possible fraud.

Career Highlights

Pingali’s listed patents address two related challenges: detecting suspicious features in identification documents and using transaction context to support fraud-related decisions. Together, they illustrate how knowledge graph structures can organize fragmented data and reveal relationships that may be difficult to identify through isolated checks.

Collaborations

Pingali is associated with Jumio Corporation. His listed coworkers include Arman Mohseni-Kabir and Marzieh Mehdizadeh.

Conclusion

Ashwin Pingali’s patent work applies knowledge graphs to identity and transaction analysis, with the goal of identifying patterns that can help detect potential fraud.