Join speakers from BMO, TD Bank, Accenture and more as we to explore current and future global trends in Financial Crimes. Designed for data and analytically driven leaders who are seeking innovative approaches to evolve their business, SAS Financial Crimes Executive Leadership as well as our customers and partners will be featured, highlighting best practices and the latest techniques leveraged to combat financial crime in the digital age across banking and insurance.
Registration & RefreshmentsAmanda Holden, Executive Lead, Financial Crimes, SAS
Welcome, Introduction & OverviewCameron Dow, President of SAS Canada
AI and the Changing Face of Financial Crimes: Beyond the Marketing Hype
The Impact of Digitization: Quick, Which is it? Approve, Review or Block
Case Studies on Next Generation AML: What is the Role of Artificial Intelligence
David Stewart, Global Director, Fraud & Security Intelligence (Banking), SAS
Global Trends in Insurance Fraud: Next Generation Network Analytics
David Hartley, Global Director, Fraud & Security Intelligence (Insurance), SAS
The Modern FCIU: Special Risk Investigations
Daniel Nagle, Global Banking Principal, SAS
Suspect Behavior Identification through Sentiment Analysis & Communication Surveillance
Specifically, the demo will showcase the deployment of artificial intelligence and machine learning in the context of Surveillance Analytics, Social Network Analysis, and Deep Emotion / Sentiment Analysis, for detection of misconduct and behavioral risk in security, commercial and public sector environments.
Constantine Boyadjiev, Head of North American Fraud, Risk and Compliance Analytics, Accenture
The Convergence of Fraud, AML & Cyber and the Emergence of Enterprise Financial Crimes
Panel discussion led by David Stewart, Global Director, Security Intelligence Practice, SAS
Networking Reception & Demo Breakouts (repeat every 15)
The SAS Building
280 King St. East, 5th Floor
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.