Abstract
Moderation systems for local business platforms may have difficulty identifying certain types of financial fraud, such as overcharging or fake deposit scams, because of challenges in validating monetary claims found in user-generated content (UGC). A disclosed method can address this potential limitation by synthesizing two different data streams. A system can use technologies, for example natural language processing and computer vision, to extract unstructured financial claims and pricing information from UGC, which may include content such as text reviews and photos of menus or receipts. The system can then correlate these claims with structured, verified transactional signals from integrated payment systems. Such signals may include payment amounts and formal dispute data. By combining these signals, the system can generate a dynamic financial risk tier for a geo-located entity. This tier can facilitate proactive interventions, such as displaying consumer warnings or de-listing businesses, to improve platform safety.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kumar S R, Mithun, "Generating Financial Risk Tiers for Geo-Located Entities Using Monetary Claims from User-Generated Content and Transactional Signals", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/8498