Naomi is the Head of Kavi Labs at Kavi Global, a consulting services, software and solutions company. Naomi is the Head of Kavi Labs, the innovation and incubation arm of Kavi Global. She is an extremely passionate data scientist and product management consultant with over seven years of experience using data analytics and design thinking to deliver business value. Prior to this role, she was a founding member of the Global Analytics Team at GE Healthcare and has created breakthrough innovative AI solutions using AI and IoT. Naomi graduated from GE’s Digital Leadership Program. Naomi has a Master of Science in Analytics from Northwestern University and a certificate in Business Management from Indiana University. She received a Bachelor of Science degree in Computer Science & Applied Psychology from Ithaca College. Naomi is also a published AI Ethics author, published research in scientific journals, and holds several AI related patents.
May 26, 2021 05:00 PM PT
Fraud is prevalent in every industry, and growing at an increasing rate, as the volume of transactions increases with automation. The National Healthcare Anti-Fraud Association estimates $350B of fraudulent spending. Forbes estimates $25B spending by US banks on anti-money laundering compliance. At the same time as fraud and anomaly detection use cases are booming, the skills gap of expert data scientists available to perform fraud detection is widening.
The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
An innovative aggregation framework converts probabilistic fraud scores and their probabilities into a meaningful and actionable prioritized list of suspicious (a statistical outlier) and potentially fraudulent transaction to be investigated from a business point of view. The AI Anomaly Detection models improve over time using Human-in-the-Loop feedback methods to label data for supervised modeling.
Finally, The Kavi team overviews the Anomaly Lifecycle: from statistical outlier to validated business fraud for reclaim and business process changes to long term prevention strategies using proactive audits upstream at the time of estimate to prevent revenue leakage. Two client success stories will be presented acros Pharmaceutical Rx and Transportation industries.