Rajesh is the CTO of Kavi Global, a consulting services, software and solutions company. Rajesh has two decades of experience in advanced analytics and information technology, specializing in data engineering, data science and visualization, machine learning and artificial intelligence. Rajesh has led award winning enterprise data analytics initiatives at GE and fortune 500 companies across industries, and created patented next gen data and analytics tools. Before the launch of Kavi Global, Rajesh led the award-winning GE Rail Services Analytics team from GENPACT Analytics, an entity formerly owned by GE. Rajesh holds an MBA in Marketing and Systems from Bharathidasan Institute of Management in Trichy, India, and a Bachelor of Commerce degree from University of Madras in Chennai, India.
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.
June 4, 2018 05:00 PM PT
Up to 10% of the pharmacy claims submitted to health plans and insurance companies are estimated to be fraudulent. A two-step process comprising of a combination of unsupervised and supervised learning techniques can be used to effectively identify the Fraud, Waste and Abuse in the Pharmacy industry. Claims submitted by the pharmacies to insurance companies/health plans provide data rich in valuable insights that enable prediction of fraud, waste and abuse. Suspicious activity can be identified by detecting anomalies in the data using unsupervised techniques like clustering, univariate and multi-variate outlier analysis, link analysis, simulated fraud signatures, etc.
With known fraud, waste and abuse data, supervised learning techniques like Random Forest, Neural Networks, etc., can be used to identify fraudulent transactions similar to historical fraud signatures. Combining outputs of these techniques Fraud scores are generated for each player [member, pharmacy and prescriber].
In this talk, we're going to illustrate how machine learning [Spark MLLib and GraphX] was used to identify suspicious activity like co-conspiracies to commit fraud by pharmacies and prescribers[doctors] and others. We're also going to demonstrate how fraud score was determined in this pharmacy claims fraud detection application.
Session hashtag: #DS9SAIS