Fraud and AML convergence, public and private sector partnership and improved detection and prevention in Canada – these are all topics and questions on senior executive’s mind, and likely on yours. This is the Evolution of Financial Crimes.
Could an alignment of Fraud & AML reduce costs, improve customer service and reduce risk? With better internal and external partnerships, could Canadian FI’s attack criminal behavior, making financial services safer for everyone … and reduce losses to your bottom line? What role does Artificial Intelligence play today and in a future where business processes are aligned, data is available and holistic prevention is the outcome?
Join the conversation with your senior executive peers over an exceptional dinner and compelling debate, featuring Scott Doran, Chief Superintendent of the RCMP and David Stewart, Global Director of SAS’ Security Intelligence Practice for Banking. You will have the opportunity to discuss current and strategic questions regarding the convergence and alignment of fraud and AML at the level of people, process, partnerships, technology and data. Our goal is to create insight, ideas and inspiration to help you lead these critical discussions within your organization.
The dinner will be an exceptional senior networking opportunity attended by SVP level Fraud, AML and technology leaders. Engaging discussion topics will include:
- The role analytics plays in the convergence of Fraud & AML business processes
- People, process, technology, data, culture – what’s the real barrier
- The value AI and Machine Learning in Financial Crimes across the organization
- The importance (and resistance) of public/private sector alignment and support
|6:30pm||Arrival & Cocktail Reception|
|7:00pm||Welcoming Remarks from the Host|
|7:30pm||The Evolving Paradigm of Financial Crimes: Focusing on Private, Public & Technology |
Scott Doran, Chief Superintendent, RCMP
|7:45pm||Emerging Global Trends in the Financial Crimes Landscape|
David Stewart, Global Director, Security Intelligence Practice (Banking)
|8:00pm||Dinner is Served |
Facilitated Discussion Topics Led by Amanda Holden
|9:30pm||Thank You, Close & Aperitif|
601 King St. West, Toronto
One size never fits all, but a dynamic segmentation strategy does.
A typical anti-money laundering (AML) transaction monitoring program has scenarios that monitor the customers and accounts that pose the most risk to the institution. The fact is … this one-size-fits-all methodology isn’t very effective. That’s because customers transact differently based on many factors. So how do you incorporate that into your program?
In addition to addressing risks through transaction monitoring, financial crimes investigation units (FCIUs) are expected to proactively identify financial crime risk, such as the firm’s exposure to geopolitical events and terrorism financing. It’s not just a matter of protecting the organization from regulatory and reputational risks, but also helping law enforcement combat serious national and global threats.
And if an incident were uncovered, investigators would need to be able to answer questions about who, what, where, when and why. Which parties, accounts and geographies are involved? What products are they using? What transaction trends are seen? Is this an ongoing or short-term risk? What caused it, and what actions are being taken?
Big data. Yeah … so what? What does big data have to do with insurers? Just think about it. You sift and search and sort incredible amounts of data – adjusters’ hand-written notes, data from fraud lists and the information from claims management systems and the NICB claims database. Are you getting the most from that insurance claims data?
Fraud detection is a challenging problem. The fact is that fraudulent transactions are rare; they represent a very small fraction of activity within an organization. The challenge is that a small percentage of activity can quickly turn into big dollar losses without the right tools and systems in place. Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters have learned to change their tactics. The good news is that with advances in machine learning, systems can learn, adapt and uncover emerging patterns for preventing fraud.