Super Early Bird Fee
Register and Pay
by 6 Jun ’19
Early Bird Fee
Register and Pay
by 28 Jun ’19
Regular Fee
Register and Pay
after 28 Jun ’19
Singapore-registered companies $1,920.65 (SGD) $2,027.65 (SGD) $2,134.65 (SGD)
Non Singapore-registered companies $1,795 (SGD) $1,895 (SGD) $1,995 (SGD)

 

Group Discount!
Enjoy 10% off when you register for 3 or more OR
For groups of 3, 4th comes for free

IMPORTANT NOTES
  1. Super Early Bird and Early Bird promotion: Discount will only be valid if payment is received by the stipulated date.
  2. Group discount only applies to registrations from the same company, attending the same event in the same country location. Delegates must register at the same time and be of the same billing source. Only a single invoice will be issued.
  3. Only corporate registrations will be accepted.
  4. Bank charges & taxes are to be borne by registrants, if applicable.
  5. Full payment is mandatory upon registration for admission to the event.
  6. Walk-in delegates will only be admitted on the basis of space availability at the event and with immediate full payment.
  7. Fee includes lunch, refreshments and event documentation.
  8. The organiser reserves the right to make any amendments that it deems to be in the interest of the event without any notice.
  9. Information provided will be used for event administration and updates on upcoming events. For more details, please visit: http://www.conferences.com.sg/personal-data-protection-statement/

CANCELLATION & REPLACEMENT

A replacement is allowed if registered participants are unable to attend. For cancellations received in writing before 28 Jun 2019, a full refund will be given with a 10% administrative charge. For cancellations received in writing before 11 Jul 2019, a 50% refund will be given together with the event documentation. There will be no refunds for cancellations received after 11 Jul 2019 or “no show” participants. However participants will receive a copy of the event documentation.

Are Complex Fraud Schemes falling through your Detection Gaps?

Data analytics can unmask emerging fraud patterns and reveal investigative insights. But are you collecting the right data for analysis? What are the data analytic approaches to identify anomalies? Are you using data results to create actionable information for fraud investigations?

Harness Data to Effectively Detect Fraud

Join this practical 2-day workshop to acquire key skills to develop a data-led fraud detection and investigation approach. Discover how to identify relevant datasets, extract to data warehouses and prepare the data for analysis. Acquire different data analytic techniques to detect irregularities including designing hypothetical models. Learn how to create actionable intelligence for investigation and examine how to use data visualisation to present findings. Find out how to implement proactive fraud monitoring and AI solutions for your business.


Hands-on Exercises for Practical Learning

    • Create your own data-led investigative approach to three different fraud scenarios:
      • Conflict of interest
      • Procurement fraud
      • Fraudulent travel and entertainment claims
    • Practice using Excel and Tableau:
      • Designing a work plan
      • Identifying datasets
      • Determining what tests to run
      • Deciding type of follow up action
      • Presenting results

Benefits of Attending

  • Examine the issues with current investigative processes and how to create a data-led approach
  • Tackle common problems when working with data including quality, integrity and reliability
  • Overcome challenges when collecting, storing and mapping disparate data sources
  • Acquire a two-prong approach and specific red flag analytical tests to detect irregularities
  • Design hypothetical and scenario models based on known instances of fraud
  • Learn how to create a risk model to rank data results for better decision-making
  • Determine the types of follow up actions that can be undertaken for investigation
  • Discover how to use data visualisation for reporting and automatic discrepancy reporting
  • Convince C-suite to add analytics into your anti-fraud and corruption framework
  • Assess key considerations when adopting proactive monitoring and AI solutions
  • Takeaway lessons learnt and pitfalls to avoid from real life fraud case studies
 

Workshop Leader

Allanna Rigby

Regional Head Data Analytics, Asia,

Control Risks

CR logo_High res

 

Allanna Rigby is the regional lead for data analytics in Asia Pacific. She specialises in providing data-led proactive solutions to reduce fraud and financial crime exposure, fusing intelligence techniques and data analytics to enhance investigative methodologies and providing insightful analysis and forecasts to reduce risk exposure. She has nine years of experience providing strategic intelligence and financial crime advice, as well as leading high profile investigations for various investment and retail banks worldwide. Allanna’s global experience includes Europe, the Middle East, Asia Pacific and Australia.

 

Prior to joining Control Risks, Allanna was a senior manager at the Commonwealth Bank of Australia (CBA) where she helped build and lead the Intelligence Team within the Corporate Security function and provided proactive financial crime advice and geopolitical intelligence to global strategic decision makers across the bank. Prior to joining the CBA, Allanna worked as a regional intelligence analyst within Macquarie Bank’s Global Security team in Sydney, where she provided geopolitical, country risk and fraud advice to senior management. Allanna began her career working at Goldman Sachs in the UK as part of their Mergers and Acquisitions team before moving to Deutsche Bank as an intelligence analyst, focusing on geopolitical risk.

 

Allanna holds a bachelor of science (chemistry with mathematics) from University College London and a certification in Intelligence. She is also a Certified Fraud Examiner.

Who Should Attend

Senior level executives responsible for Internal Audit, Finance, Risk Management, Fraud Detection and Investigation. No experience in data analytics is required.

 

Agenda

  • Session 1: Fraud Detection using Data Analytics

    How to create a data-led approach to detecting and investigating fraud

    • Types of fraud schemes
    • Difference between proactive and reactive fraud investigations
    • Issues with current investigative process
    • Benefits of a data-led solution
    • How to create a data-led approach to fraud investigations

    Case study: How a simple compliance review during a post-acquisition identified systemic fraud by the company’s accountant totaling USD235k

  • Session 2: Data Collection

    How to identify relevant datasets, extract to data warehouses and prepare the data for analysis

    • What data can be used?
    • Things to consider when collecting and storing data
    • Common problems with data, including quality, integrity and reliability
    • Mapping disparate data systems

    Hands on exercise: Identify relevant datasets for a whistleblower allegation
    Case study: What to do when disparate data systems cannot be mapped?

  • Session 3: Data Analytics Skills and Techniques for Fraud Detection

    Different data analytic approaches to identifying and detecting fraud

    • The two-prong analytical approach to identify potential fraudulent behavior
    • Specific red flag analytical test that can be used for fraud detection
    • Use of Benford’s Law and other statistical technical to identify anomalies
    • Designing hypothetical and scenario models based on known instances of fraud

    Hands on exercise: Create your own hypothetical model to detect an insurance fraud
    Case studies: Various examples of detecting fraud using data analytics

  • Session 4: Creating Actionable Intelligence

    How to use the results from data analytics to create actionable information to investigate anomalies

    • Create a risk model to rank results to make better decisions
    • Types of actions that can be undertaken to investigate fraud
    • Enhancing and fine tuning algorithms

    Case study: Using data analytics to investigate counterfeit and parallel products in the pharmaceutical industry

  • Session 5: Reporting and Automation

    How visualisation can help you add value to reporting; automate to enhance the company’s resilience to fraud

    • Use data visualisation in presenting findings
    • Automate discrepancy reports to business units
    • How to convince C-suite to add analytics in your anti-fraud and corruption framework
  • Session 6: Hands-on Exercise – Creating your own Data-led Investigative Approach using Excel and Tableau

    Create your own data-led investigative approach to three different fraud scenarios:

    • Conflict of interest
    • Procurement fraud
    • Fraudulent travel and entertainment claims

    Including designing a work plan, identifying datasets, determining what tests to run, decide type of follow up action and presenting the results

  • Session 7: Proactive Fraud Monitoring and other AI Solutions

    The benefits of implementing a proactive fraud monitoring solution, how it works and what you need to consider, other advanced AI solutions to detect and monitor for fraud

    • Benefits of proactive fraud monitoring solution
    • Designing and implementing a proactive fraud detection solution
    • Other advanced AI solutions to detect and monitor for fraud