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.

1. Wizard Driven AI
Anomaly Detection with
Databricks in Azure
Naomi Kaduwela
Head of Kavi Labs
Rajesh Inbasekaran
CTO

2. Agenda
Naomi
▪ Fraud Prevention Opportunity
▪ Why AI Audits
▪ Rise of Citizen Data Scientists
▪ Solution Approach
▪ Designing for Citizen Data Scientists
▪ Anomaly Lifecycle
▪ Deployment Options
▪ Success Stories
Rajesh
▪ How the Solution Works
▪ Cloud Native, Serverless Architecture
▪ Databricks Integration

3. Billions of Dollars of Opportunity
$350 B
Fraudulent Healthcare
spending*
* According to the National Health Care
Anti-Fraud Association
$25 B
Spent annually by US Banks on
anti-money laundering
compliance*
* According to Forbes
$40 B
Total annual cost of Insurance
Fraud (excluding health
insurance)*
* According to the FBI

4. Ideal
for
AI!
Why AI Audits
Data Volume &
Complex Patterns
Need to
Adapt to New Changes
High Frequency
Transactions
Transaction
Flagging
Actor-to-Actor
Flagging
AI flags the root cause of Anomalies in a Scalable way!

5. Rise of the Citizen Data Scientist
Thanks to technology abstraction
Data Scientists can now focus on solving
the business problem
Accelerating time to value
& maximizing their human potential!

6. Solution Approach
1. Different Anomaly
Signatures (possible fraud)
exist within same data
3. Despite different
methods, a holistic view of
anomaly is required for
business
2. Different methods are
efficient in detecting
different Anomaly
Signatures
4. Management of entire
anomaly lifecycle
management is critical for
effectiveness and efficiency

7. Designing for Citizen Data Scientists
Business Benefits
04
● Holistic and meaningful view
● Aggregate model into quantifiable business
opportunity
Evaluation & Visualizations
03
● Collect and report model metrics
● In built visualizations aid understanding
Portfolio of Algorithms
02
● Diverse portfolio of algorithms available
● Ability to compare parameters across methods &
combine multiple AI methods together
Wizard Driven, No code ML
01
● No programming required
● Enable Citizen Data Scientists
Anomaly Lifecycle Management
05
● Track from detection to actual recovery
● Human in the Loop for continuous improvement

8. Wizard Driven, No-Code ML
Screenshot

9. Portfolio of Algorithms
• Unsupervised • Supervised
Distribution Clustering
Association
Sequencing
Historical
Occurrence
Random
Forest
Neural
Network

10. Evaluation & Visualization

11. Business Benefits
7,761,096 $768,408,624 929,412 $21,263,307
26,452 $ 4,723,995
36,573 $ 5,263,785
119,079 $ 20,536,362
295,041 $ 262,482
21,099 $ 3,760,542
123,243 $ 4,062,246
308,025 $ 3,384,471
2019 Business Benefits Summary
Anomaly Opportunity Breakdown By Method
Billing Error
Duplicate Repair
Labor Overcharging
Material Overcharging
Over Repair
Wrong Shop
Wrong Repair
Opportunity Records Savings

12. From Statistical Anomaly to Confirmed Fraud
Raw Data
Predicted
Anomaly
Possible Fraud
Confirmed
Fraud
Actual
Recovery

13. Human in the Loop Anomaly Lifecycle
Model
Building
Update
Feedback
Anomaly
Detection
Recovery
Process
Anomaly
Validation
Citizen Data Scientist
Business SME

14. Deployment Options
Estimate
Option 1: Prevention
Real Time Scoring
at Time of Estimate
to Prevent Fraud
Money is Exchanged
Payment
Option 2: Reclaim
Batch Processing
Post Invoicing
to Reclaim Fraud
Invoice

15. Enterprise Tech Stack Integration
Digital Solutions
Layer
KPIs and Metrics, Descriptive Dashboards. AI Audits
Data Services
Layer
Integration, Transformation, Governance, Security,
Orchestration, Data Catalog
Source Systems &
Infrastructure
Ingestion of Internal Systems, Industry Systems, Customer
Systems. Storage & Compute

16. Success Stories
▪ $6.8M of potential FW&A in
prescription drug claims
▪ $7M of opportunity in Equipment
Repair Bill Invoicing Audits
• Transportation
• Pharma & Healthcare
ROI is High! Payment time is Short!

17. How the Solution Works

18. Cloud Native Serverless Architecture

19. Databricks Integration
Batch
• Jobs API
• 2.0/jobs/run-now
• Python Task
• Python Params
Interactive
• Notebook Task
• Notebook Params

20. Wizard Driven AI Anomaly Detection
Thank You!
Please share your feedback!
Feel free to reach out
https://www.linkedin.com/in/naomikaduwela/
https://www.linkedin.com/in/rajeshin/