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Wizard Driven AI Anomaly Detection with Databricks in Azure

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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.

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Wizard Driven AI Anomaly Detection with Databricks in Azure

  1. 1. Wizard Driven AI Anomaly Detection with Databricks in Azure Naomi Kaduwela Head of Kavi Labs Rajesh Inbasekaran CTO
  2. 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. 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. 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. 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. 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. 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. 8. Wizard Driven, No-Code ML Screenshot
  9. 9. Portfolio of Algorithms • Unsupervised • Supervised Distribution Clustering Association Sequencing Historical Occurrence Random Forest Neural Network
  10. 10. Evaluation & Visualization
  11. 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. 12. From Statistical Anomaly to Confirmed Fraud Raw Data Predicted Anomaly Possible Fraud Confirmed Fraud Actual Recovery
  13. 13. Human in the Loop Anomaly Lifecycle Model Building Update Feedback Anomaly Detection Recovery Process Anomaly Validation Citizen Data Scientist Business SME
  14. 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. 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. 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. 17. How the Solution Works
  18. 18. Cloud Native Serverless Architecture
  19. 19. Databricks Integration Batch • Jobs API • 2.0/jobs/run-now • Python Task • Python Params Interactive • Notebook Task • Notebook Params
  20. 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/

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.

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