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Executive Education
Clariden Leadership Institute
Enhancing Detection Of Fraud & Money Laundering Using AI (Melbourne)
Dr. Paul Condylis
Jewel Paymentech
Dr. Paul Condylis, Jewel Paymentech
  • Subject Matter Expert in developing cutting-edge fraud and anomaly detection solutions, with more than 13 years’ experience in data science, machine learning and neural networks, data analysis, and machine vision.
  • Formerly with The Centre for Quantum Technologies in Singapore where he spent a number of years in advancing developments in quantum computing
  • Regular public and seminar speaker in data sciences and experimental physics space
  • Paul has worked with a range of clients from different industries and academia, including a national payment system in Singapore, the largest e-commerce marketplace in Singapore, NUS, NTU, and SMART-MIT to name a few

Paul is the CTO of Jewel. He comes from a strong background in data science and the physical sciences. He was formerly with The Centre for Quantum Technologies in Singapore where he spent a number of years as a Senior Research Fellow, advancing developments in quantum computing.

 

Paul was involved in leading an active team of researchers and Ph.D students with responsibilities encompassing instrumentation, data analytics, software development and management of multiple experimental physics projects.

 

Paul hold a Ph.D in experimental physics from Imperial College London.

Program Summary

This interactive and dynamic session will provide you with the key fundamentals of Artificial Intelligence (AI) and Machine Learning technology implementation and how to unlock the key use cases on managing financial crime. By demystify the mechanism of AI and Machine Learning, it will help you to accurately uncover known and unknown fraud quickly, reduce false positives and minimize losses.

 

Led by Dr. Markus Kirchberg, former Senior Director of Innovation & Strategic Partnerships for Visa and Data Scientist expert Dr. Paul Condylis, they will walk you through state-of-the-art machine learning and decision sciences in fighting finance crimes with advanced fraud detection, financial crimes and anti-money laundering. You will also discover how to combine AI with big data analytics to reduce false positives, build advanced fraud scoring and design an operational framework to improve effectiveness. At the end of the course, you will also able to apprehend the significance of network sciences and apply it to your anti-money laundering framework for a more effective and accurate outcome. 

 


Programs, dates and locations are subject to change. In accordance with Clariden Global policy, we do not discriminate against any person on the basis of race, color, sex, religion, age, national or disability in admission to our programs.

Introduction

As we are advancing into the digital transformation world, the emergence of various technologies are causing fraudulent cases such as payment fraud and money laundering to become more sophisticated than ever. In a report by McAfee, it is estimated that cybercrime currently costs the global economy around $600 billion or 0.8% of global domestic product. With the growth in number of fraud cases causing massive financial losses, it makes intelligent fraud detection techniques significantly important. Fortunately, artificial intelligence and machine learning have enormous potential to decrease financial fraud with its capability to reduce manual review queues through fast iterating machine models, channel-agnostic, adopt new business lines using experiential data, augment decision-making with precision and reduce false positives with behavior analysis.

 

Led by Dr. Markus Kirchberg, former Senior Director of Innovation & Strategic Partnerships for Visa with more than 20 years’ experience in technology-driven innovation, and Data Scientist expert, Dr. Paul Condylis who has over 13 years of experience in incorporating data science with financial risk, they will walk you through the key fundamentals of Artificial Intelligence (AI) and Machine Learning technology implementation and how to unlock the key use cases on managing financial crime. By demystifying the mechanism of AI and Machine Learning, it will help you to accurately uncover known and unknown fraud quickly, reduce false positives and minimize losses. Dr. Markus and Dr. Paul will lead you to dive deep into key enablers such as supervised learning, unsupervised learning, neural networks, deep learning, auto-encoder and network science techniques in identifying patterns to detect fraud and suspicious transactions. You will also be enlightened with the crucial role of advanced data and good data models that will allow you to capture the right data to identify fraudulent cases by understanding the interconnection between big data, fast data, clean data and any data.

 

By understanding the function of data mining, you will be equipped with the insights on key challenges and opportunities of applying AI & Machine Learning in the production environments and determine the key instrument in solving the black box problem in fraud and money laundering cases by analyzing its real-time fraud scoring. At the end of the course, you will also able to apprehend the significance of network sciences and apply it to your anti-money laundering framework for more effective and accurate outcomes. With the insights that you learn throughout the 2-day program, you will be able to determine if your organization is fit to adopt AI and Machine Learning in combating financial risks.

 

What You Can Expect

By the end of this program, participants will be able to: 

  • Distinguish the key fundamentals and learn about the potential of state-of-the-art machine learning & AI, decision sciences, and network sciences approaches in fraud detection, fraud prediction and AML
  • Learn about data gathering, data life-cycle and machine learning best practices
  • Understand the differences and respective challenges between a proof-of-concept ML / AI study and deploying ML / AI models in production
  • Gain insights on common machine learning / AI frameworks and toolsets and their applicability to the various stages of exploration, proof of concept, proof of value and deployment in production
  • Be familiar with lessons learnt from building and managing real-time fraud detection and prediction solutions
  • Learn how to utilize recent technological advances to build better transaction laundering and anti-money laundering solutions
 

Who Will Benefit Most

This program is designed for, but not limited to, middle to senior level professionals who are involved in, Fraud & Risk Management, Forensic Accounting, Internal & External Audit, Internal Control, Corporate Governance and Compliance as well as Finance, and professionals who are keen to embed the prospect of AI and machine learning within the fraud detection & prevention and AML function.

Program Outline

Day 1:

 

09:00 - 17:00 | 16 September 2019

DE-MYSTIFYING AI AND MACHINE LEARNING

 

Session 1: Financial Crime - Basics 

  • Financial Crime; Fraud In FinTech, RegTech, SupTech, InsureTech, And Auditing; Consumer Expectations Vs. Consumer Experiences; Key Enablers

Session 2: Is AI / ML Suitable To Tackle My Business Problems?

Session 3: Demystifying AI & ML 

  • What Is AI / ML?; ML In Businesses; Supervised Learning; Unsupervised Learning; Neural Networks; Deep Learning; Auto-Encoders; Network Science

 

FRAUD USE CASES

 

Session 4: Fraud Scoring 

  • Beyond Rule-Based Systems; Real-Time Fraud Scoring; Step-By-Step Example; Supervised Versus Unsupervised Learning Approaches; Representation Learning

Session 5: Advanced Fraud Scoring

  • ML / AI Challenges In Production Environments; Fraud Scoring Beyond A Single Transaction

Session 6: Your Fraud Use Cases 

  • What Opportunities And Challenges Do You See Day-To-Day?

 

Day 2:

 

09:00 - 17:00 | 17 September 2019

DATA IS THE NEW GOLD

 

Session 1: Towards Better Data, Good Models And Theory That Explains Them 

  • Refresher And Outlook

Session 2: Getting The 'Right' Data

  • Big Data, Fast Data, Clean Data, Any Data, ... What Is The Right Data?; Strategies For Identifying / Creating The Right Data

Session 3: Data Challenges

  • Data Ownership, Data Privacy, Bias In Data Set, And Regulatory Challenges 

Session 4: Case Study On Interpretability And Explainability 

  • The Black Box Problem; Can We Ask The Machine To Explain Itself?

 

FINANCIAL CRIME USE CASES

 

Session 5: Money Laundering 

  • What Is Money Laundering?; What Is Transaction Laundering?; Practical Challenges

Session 6: Transaction Laundering Case Study 

Session 7: Anti-Money Laundering

  • The Importance Of Network Science; Case Study; AI / ML For AML

Session 8: ML / AI Adoption In Your Business 

  • Is Machine Learning The Right Fit? Is Your Company Ready?
 

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Venue: InterContinental Melbourne The Rialto
Date: 16 - 17 September 2019
Faculty: Dr. Paul Condylis
Early Bird 1: AU$2,195 (by 22 July 2019)
Early Bird 2: AU$2,395 (by 19 August 2019)
Regular Fee: AU$2,595
Group Discount: 2nd participant get 10%, or register 3 participants and 4th participant get a complimentary seat
(1 discount scheme applies)
Contact: [email protected]
 
 
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