Jayashree S Kolhatkar

20130018795, Jan 17, 2013

Jul 15, 2012

13/549491

Jayashree S. Kolhatkar - Emerald Hills CA, US
Sangita S. Fatnani - Cupertino CA, US
Yitao Yao - Saratoga CA, US
Kazuo Matsumoto - Santa Clara CA, US

G06Q 20/40

705 44

Exemplary embodiments for detecting electronic payment card fraud include receiving real-time payment card transaction data from ingress channels and an egress channels of at least one payment card system through a first application programming interface (API); generating transactional profiles for each of at least payment cards, the ingress channel, the egress channels, and funding sources of the payment cards; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compare the transaction data to the transactional profiles to calculate a probabilistic fraud score for the current transaction; evaluating the probabilistic fraud score and the current transaction data based on a set of rules to generate a recommendation to approve, decline or review the current transaction; and transmitting the recommendation back to the payment card system via a second API.

20130018796, Jan 17, 2013

Jul 15, 2012

13/549492

Jayashree S. Kolhatkar - Emerald Hills CA, US
Sangita S. Fatnani - Cupertino CA, US
Yitao Yao - Saratoga CA, US
Kazuo Matsumoto - Santa Clara CA, US

G06Q 20/40

705 44

Electronic payment card money laundering detection includes receiving real-time payment card transaction data from ingress channels and an egress channels of at least one payment card system through a first API; generating transactional profiles for each of at least payment cards, the ingress channel, the egress channels, and funding sources of the payment cards; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compares the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; evaluating the probabilistic money laundering score and current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and transmitting the suspicious activity report back to the payment card system and transmitting the suspicious activity report to an identified regulatory body.

20130151453, Jun 13, 2013

Dec 7, 2012

13/708898

Jayashree S. Kolhatkar - Palo Alto CA, US
Mark Paul Palaima - Saratoga CA, US
Vijay Raghavendra - Fremont CA, US
Yitao Yao - Saratoga CA, US

INKIRU, INC. - Palo Alto CA

G06N 5/02

706 46

A real-time predictive intelligence platform comprises: receiving from a user through a meta API definitions for predictive intelligence (PI) artifacts that describe a domain of an online transaction system for least one business entity, each of the PI artifacts including types, component modules and behavior bundles; exposing an entity API based on the PI artifacts for receiving entity events from the online transaction system comprising records of interactions and transactions between customers and the online transaction system; responsive to receiving an entity event through the entity API, executing the component modules and behavior bundles to analyze relationships found between past entity events and metrics associated with the past entity events, and computing a probabilistic prediction and/or a score, which is then returned to the online transaction system in real-time; and processing entity event replicas using modified versions of the PI artifacts for experimentation.