big data analytics for fraud detection · data warehousing & big data: etl, data quality...
TRANSCRIPT
Agenda
• Introduction• Case Studies
– Procurement Fraud Analytics– Claims Fraud Analytics– Insider Threats Analytics
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“Two Routes to (Innovation) Resilience”
A B
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NCS Data Scientist Team
>30 Data ScientistsPh.D., Masters, Bachelors
Data Mining SkillsMachine learning, anomaly detection, time series analysis, text mining etc
Software CompetenciesR, Python, SAS, SPSS, Watson
Cross-Industry ProjectsInsurance, Financial Services, Government, Transport, Defence, Public Safety, Education, Healthcare etc
Cross-Disciplinary TeamStatistics, Machine Learning, Computer Science, Engineering, Business, Psychology
Delivered Iconic ProjectsSince January 2015
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NCS Data Science Methodology and Partners
Actionable insights for insurance
Data sourcesData
protection & preparation
Data modeling & sense-making
Deploy-ment & visual-isation
Data warehousing & Big Data:ETL, Data quality management, Storage, Metadata management, OLAP, Hadoop
Business intelligence, reports & dashboards, for planning, operations & chatbots
Open source
Telco network
(cellular, wifi, weblogs)
Customer (1st party
data)
Sensors
(Wearables, IoT, M2M, Video)
Foundation analytics
Network analysis
Distributedanalytics
Complex event processing
Data mining Forecasting Optimization Text analytics
Agenda
• Introduction• Case Studies
– Procurement Fraud Analytics– Claims Fraud Analytics– Insider Threats Analytics
77
High Profile Cases for Procurement Fraud
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Procurement Fraud Detection System
VISUALIZATION
• Data Acquisition• Data Extraction• Data Cleansing• Data Integration• Data Standardization• Data Integrity Check• Data Exploration• Data Transformation• Data Sampling and
Partitioning
DATA PREPARATION
• Anomaly Detection• Predictive Analytics• Clustering• Association and Sequence
Analysis• Forecasting• Network Analysis• Text Analytics• Optimization• Complex Event Processing
(CEP)• Hadoop Analytics
SENSE-MAKING
Procurement Officer
Procurement Manager /
Auditor
DATA SOURCESEssential– Purchase Order details– Payments related to
purchases– Vendor details– Employee details
Agenda
• Introduction• Case Studies
– Procurement Fraud Analytics– Claims Fraud Analytics– Insider Threats Analytics
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High Profile Cases for Organization Claims Fraud
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Productivity & Innovation Credits (PIC) Grant
The PIC scheme was introduced to encourage productivity and innovation activities in Singapore. It provides support to businesses thatmake investments to improve their productivity.
Tax Deductions/ Allowances
400% tax deductions/ allowances on up to $400,000 of spending per year in each of the six qualifying activities.
PIC+ SchemeFrom YAs 2015 to 2018, qualifying businesses can enjoy 400% tax deductions/allowances on up to $600,000.For more details, please refer to How PIC Benefits You.
Cash Payout
Option to convert up to $100,000 of total spending in all six activities for each YA into a non-taxable cash payout, in lieu of the tax deduction/allowance.For YAs 2013 to 2018, the cash payout rate is 60% of qualifying expenditure incurred.For more details, please refer to How PIC Benefits You.
PIC BonusA dollar-for-dollar matching cash bonus, subject to an overall cap of $15,000 over YAs 2013 to 2015 combined.
Additional information: https://www.iras.gov.sg/irashome/Schemes/Businesses/Productivity-and-Innovation-Credit-Scheme/
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Claims Fraud Detection System
VISUALIZATION
• Data Acquisition• Data Extraction• Data Cleansing• Data Integration• Data Standardization• Data Integrity Check• Data Exploration• Data Transformation• Data Sampling and
Partitioning
DATA PREPARATION
• Anomaly Detection• Predictive Analytics• Clustering• Association and Sequence
Analysis• Forecasting• Network Analysis• Text Analytics• Optimization• Complex Event Processing
(CEP)• Hadoop Analytics
SENSE-MAKING
Claims Processing
Officer
Finance Manager /
Auditor
DATA SOURCESOrganizations– Invoices– Claims– Payments related to
claims– Organization details
Agenda
• Introduction• Case Studies
– Procurement Fraud Analytics– Claims Fraud Analytics– Insider Threats Analytics
14
An insider is “a trusted entity that is given the power to violate one or more
rules in a given security policy” and“the insider threat occurs when a trusted entity abuses that power”
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High Profile Cases for Insider Threats in Singapore
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High Profile Cases for Insider Threats Overseas
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Insider Threats Detection System
VISUALIZATION
• Data Acquisition• Data Extraction• Data Cleansing• Data Integration• Data Standardization• Data Integrity Check• Data Exploration• Data Transformation• Data Sampling and
Partitioning
DATA PREPARATION
• Anomaly Detection• Predictive Analytics• Clustering• Association and Sequence
Analysis• Forecasting• Network Analysis• Text Analytics• Optimization• Complex Event Processing
(CEP)• Hadoop Analytics
SENSE-MAKING
Internal Audit
Officer
Internal Audit Director
DATA SOURCESCyber– Network flow (e.g.
flexible)– Higher-level transport
protocols – Audit records – Application-level content
Disclaimer: This material that follows is a presentation of general background information about NCS activities current at the date of the presentation. The information contained in this document is intended only for use during the presentation and should not be disseminated or distributed to parties outside the presentation. It is information given in summary form and does not purport to be complete. It is not to be relied upon as advice to investors or potential investors and does not take into account the investment objectives, financial situation or needs of any particular investor. This material should be considered with professional advice when deciding if an investment is appropriate.