CaseWare Monitor - New in 5.4 Release
0 likes104 views
The Caseware Monitor 5.4 release introduces enhancements for anomaly detection, user navigation, and real-time onboarding for customer and vendor risk assessment. Key features include improved remediation processes, advanced analytics capabilities, and machine learning integration to optimize fraud detection and regulatory reporting. This update aims to facilitate thorough investigations, reduce false positives, and enhance operational efficiency in compliance operations.
CaseWare Monitor - New in 5.4 Release
- 1. CASEWARE MONITOR – NEW IN 5.4 RELEASE Mike Gilbert, Product Owner Michel Caluori, Delivery Manager WEBINAR
- 2. Agenda • What is CaseWare Monitor • Remediation Enhancements • Navigation and UX • Batch Attachments • Workflow Decision Learning • Enterprise Search for Investigations • Real-time Onboarding Due Diligence • Advanced Analytics
- 3. z Detection Analytics Remediation Workflows Investigation Alerts What is CaseWare Monitor? Insights Dashboards
- 4. Customer/Vendor Due Diligence List Screening Sanctions and internal lists, PEPs, negative news, etc. Identity Verification Risk Profiling Onboarding API Transaction Monitoring Transaction Screening Sanctions and internal lists, high risk countries, anomalies, etc. Fraud Detection Anomaly detection, link analysis, rules, etc. Regulatory Reporting Auto-Filing SARs, CTRs, STRs, etc. across multiple jurisdictions Analytics Investigations Transactions Cust./Vend. Data Events Third-Party Data DataManagement Repository Profile Anomaly Segmentation Predictive Link Analysis Decision LearningCase ManagementRules Engine Scoring Engine Workflow Enterprise Search Infrastructure Security, Privacy, Certification, Deployment Manager User Experience Visualizations, Dashboards, Reporting, KPIs Compliance Operations CaseWare Monitor Platform
- 5. CaseWare Monitor 5.4 Release Focus Smarter Anomaly Detection Thorough Investigations Easier Remediation of Issues
- 6. Agenda • What is CaseWare Monitor • Remediation Enhancements • Navigation and UX • Batch Attachments • Workflow Decision Learning • Enterprise Search for Investigations • Real-time Onboarding Due Diligence • Advanced Analytics
- 7. Remediation – Navigation and UX • Improved Navigation • Navigation panel quick links • Search • Improved Performance
- 8. z Batch Attachments Invoice0001.pdf Invoice0002.pdf Invoice0003.pdf Work Item ID: 123 Work Item ID: 456 Work Item ID: 780 Work Item ID: 789 ATTACHMENT_ID INVOICE001 INVOICE001 INVOICE002 INVOICE003
- 9. • Reduce rework and repetitive decision making • Configurable to re-perform decisions • Options to break decisions Workflow Decision Learning ! Alert Learned? Decision Decision Learned Decision taken by System No Yes
- 10. Agenda • What is CaseWare Monitor • Remediation Enhancements • Navigation and UX • Batch Attachments • Workflow Decision Learning • Enterprise Search for Investigations • Real-time Onboarding Due Diligence • Advanced Analytics
- 11. Investigations Made Easier Alerts Cases Regulatory Reports External Sources
- 13. Advanced Search
- 14. Agenda • What is CaseWare Monitor • Remediation Enhancements • Navigation and UX • Batch Attachments • Workflow Decision Learning • Enterprise Search for Investigations • Real-time Onboarding Due Diligence • Advanced Analytics
- 15. Real-Time Onboarding API • Screen customers/vendors in real-time to gauge risk levels • Reduce number of post acquisition investigations List Screening Create Risk Score ID Verification Onboarding System Data CaseWare Monitor
- 16. Periodic Screening • Keep risk levels of existing customers/vendors current List Screening Updated Risk Score Customer/Vendor Database Data CaseWare Monitor
- 17. Agenda • What is CaseWare Monitor • Remediation Enhancements • Navigation and UX • Batch Attachments • Workflow Decision Learning • Enterprise Search for Investigations • Real-time Onboarding Due Diligence • Advanced Analytics
- 18. Rules-Based is not Enough There is no way to know all the unknowns Data you Know Criminal records Offender records Detective experience Operational systems Data you Don’t Know Social Media Machine Data Geospatial Anomaly Detection AML Predictive Models Insight and Intelligence Optimize AML Decisions Prevent Fraud Statistically Accurate Operationally efficient Cost effective Compliant + = Structured and active Unstructured and unknown
- 19. Analytics Roadmap – Part 1 Descriptive (Business Intelligence) • Focuses on visualization, summary and delivery of information • Dependent on clean and organized data • Based upon structured historical data • Trends and forecasts provide forward looking insight • Dashboards, scorecards, real-time monitoring are part of solution Descriptive Analytics What happened? How many, often and where? What exactly is the problem? What happens if trend continue? Forecasting AnalyticsSophistication
- 20. Analytics Roadmap – Part 2 Predictive Analytics (Predictive Modeling) • Current and historical information via models predict an outcome based upon a set of inputs or conditions. • Leverages multiple types of data including large unstructured data sets • Less dependent on high quality or organized data • Uses mathematical/statistical algorithms • Provides answers like “Based upon these factors, this is going to happen..” Descriptive Analytics What happened? How many, often and where? What exactly is the problem? What happens if trend continue? Forecasting Predictive Analytics How do I prioritize this? What will happen next if…..? What is the probability this will happen? AnalyticsSophistication
- 21. Analytics Roadmap – Part 3 Prescriptive Analytics (Optimization) • Models and data simulate or optimize a course of action based upon a set of rules, models and business process • Built upon rules engines and algorithms to simulate probable outcomes • Leverages multiple types of data • Usually fed by specific data and models • Ex: Simulate a scenario multiple times with multiple factors to identify the fastest, cheapest, safest option Descriptive Analytics What happened? How many, often and where? What exactly is the problem? What happens if trend continue? Forecasting Predictive Analytics How do I prioritize this? What will happen next if…..? What is the probability this will happen? Prescriptive Analytics How can we achieve the best outcome? What order should I address this? What should I do if I am constrained by this? AnalyticsSophistication
- 22. Advanced Analytics Capabilities Capabilities • Deploy advanced analytic platform for AML, fraud that can scale and provide capabilities for: • Predictive Analytics • Segmentation / Cluster Analytics • Anomaly Detection • AML Predictive • Text Analytics • Social Media Analytics • Content Analytics • Increase maturity and ability to utilize more of the available data (e.g. unstructured data) Benefits • Shorter model development and refresh times • Predictive models and insights into future outcomes. E.g.: predict fraud, risk, up-take, arrears, AML predictive models, optimize AML decision etc… • Augmentation of current systems with new models and data • Segmentation / cluster models focused on risk, behavior, demographics, etc… • Insight into client sentiment Solution Characteristics • Platform supports Advanced models • Hadoop extension • Real-time support • Case management feeds machine learning • Fuse alerts, risk scoring to set priorities
- 23. Anomaly Detection • Detects changes in behavior based on history • Clusters to detect behavior that is drastically different from similar peers
- 24. Predictive Analytics • Using known fraudulent cases to identify the patterns that normally results in fraud • Using machine learning to train models, predict outcomes and establish risk levels Gender Male Credit Score > 800 Low Risk Credit Score < 800 Age < 30 High Risk Age > 30 Low Risk Female Credit Score < 600 Age < 25 High Risk Age > 25 Low Risk Credit Score > 600 Low Risk
- 25. Network Linking • Many frauds involve collusion • Identifying associations by link analysis creates significant insights • Using static and/or transactional data • Risk inheritance based on strength and distance of associations
- 26. Analytics Models Anomaly Detection Predictive Scoring Engine Rules Scenarios Block Activity Continue to Monitor Customer/Vendor Risk Score Labels (responses) feeds machine learning and informs future prescriptive actions Actions (Workflow and Case) Investigate Reporting
- 27. Procurement Use Cases • Vendor submitting non-PO invoices when they and others in their cluster always submit PO invoices. • Identifying strong correlation between requestor and vendor that is different from other vendors in the same cluster. • Using network link analysis to detect unknown relationship between vendor and employees.
- 28. Procurement Use Cases • Identifying invoices for product classes that are outliers based on vendor type, existence of PO or price ranges. • Identifying unusually high contract wins for a vendor when compared to others in the cluster. • Data enrichment with third party sources: e.g. World-Check, D&B, Country Corruption Index, Tax Haven Locations, etc.
- 29. Key Takeaways • CaseWare Monitor offers – Smart detection of issues and anomalies to detect complex issues and reduce false positives – Uncover schemes that could otherwise go undetected using rules based only – Business engagement for what is important and to reduce effort – Investigate issues easily and thoroughly for comprehensive resolutions – Machine learning capabilities within the model to continuously improve processes
- 30. Questions?
- 31. CASEWARE MONITOR – NEW IN 5.4 RELEASE WEBINAR Visit casewareanalytics.com Email connect@caseware.com