Getting Real on AI:Case Studies and Practical Applications

Session 9

A Step Ahead of Financial Crime and the Competition

This is the best training out there right now. You guys are making an

effort to keep it that way.

Senior Director Compliance;Financial Services

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ACFCS membership equips individuals and organizations with first-class practical tools, information and education that improve results in financial crime detection and prevention.

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Join the Next Generation of Financial Crime Fighters with CFCS Certification

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Preventing Criminal Infiltration Of The World’s Financial System

Economic Crime

Human Trafficking

Terrorist Financing

RDC’s Screening Technology Provides The Transparency To Detect And Disrupt:

Reputational Risk

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Scavenger Hunt – Let’s Go on a Goose Chase

Exhibit Booths – Take a Virtual Walk

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More Resources – Yes, I Want More!

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Boring but Important Things – Event Logistics

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• Daily reminder emails to let you know what’s on tap

• Most sessions being recorded, available until August 14

• Certificates of participation • Attend 80% of the session• Fill out the survey – Triggers at end, available

throughout

Boring but Important Things – Troubleshooting

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For customer support: • eventsupport@acfcs.org• 786-591-1346

AI and Written Text – GPT-3

AI and False Identities:No Face, No Voice, No Problem?

Meet the Experts

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Jim Richards

Former EVP, BSA Officer, Wells Fargo

Founder and Principal, RegTech Consulting

Meet the Experts

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David Creamer

Senior Manager, AML Model Management

Scotiabank

Meet the Experts

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Max Lerner

VP, Global Head of Sanctions

State Street

Meet the Experts

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Jeff Sidell

Chief Technology Officer

RDC

Definitions: A Crash Course

•What is robotic process automation (RPA)?

•What is machine learning?

•What is artificial intelligence?

Just Getting Started?

Believe in the potential…

• Don’t be too afraid to try something new – You’re not the first

• Criminals and bad actors are not sitting still – incumbent on industry to innovate

Just Getting Started?

Don’t believe the hype…

• Don’t run out and hire large teams of data scientists (probably)

• Be aware of snake oil – Is the vendor telling you the false positive rate reduction before they start the engagement?

• Vet whether vendors have considered regulatory aspects

• Recognize uniqueness of AML space – Few yes/no answers, targets are trying to evade you

Case Study: Large Investment

• Scenario: A firm has a screening system that, as is normal, generates many false positive alerts, which are dispositioned by human staff. Often, those false positives are repetitive. The firm is looking to improve efficiency of its resourcing structure by reducing false positives, deploy staff toward more complex alerts, and enable its staff to disposition non-repetitive alerts.

• Potential Solution: While there are multiple ways to reduce false positive alerts, one solution uses a ML solution – namely, a “decision reapplication” solution.

• Overview: Where a firm experiences repetitive false alerts dispositioned by a human alert dispositioner, a ML tool could be employed to learn from the human alert dispositioner and, eventually, auto-disposition repetitive false positive alerts based on certain criteria.

Case Study: Large Investment

• Risk Considerations: Firms deploying such a ML tool must act to understand and mitigate key risks, such as:

• Data elements in the alert that are in-scope for the system to review to determine if the alert is in-scope for its actioning;

• # of instances / events the system must record before it can act on its own;

• Controls to monitor the quality of alert disposition by human staff to ensure machine is learning correctly;

• Controls to ensure that the system acts the way it should once turned on; and

• Controls to ensure that expansion of the system (e.g. if one screening process is included in pilot, then is the system adaptable to be used for more screening processes).

• Governance: It is crucially important to ensure that deployment of the ML tool is made aware to and approved by relevant senior stakeholders. Further, it is advisable that regulators are made aware and educated prior to go-live.

Case Study: Small Investment

What can you achieve with a small team?

Data, Data, Data

• Data vs. features - What is the distinction between these and why does it matter?

• Strategies for preparing data for AI initiatives

• Mitigating data quality/quantity issues

• When is not having data useful data in and of itself?

Incomplete Data - Interpretation

• Missing data IS data

• Sometimes the fact something is missing or not is indicative of an underlying fact

• Missing data itself can be used as a feature in modelling

• Missing data in some cases may be intentional

Overall Principles

• Understand the story of the data• How was it collected, why does it differ, what does it mean,

when did it change, who inputs it, where is it from?

• Neither add nor remove inherent bias• If the data doesn’t contain bias, don’t add it, if it contains bias,

don’t remove it.

• Keep in mind your purpose• We rarely want ‘the data’ we want the information. Data is a

means to that end.

Governance and Privacy

• Data governance • Standards for vendors• Continuity over timeframes

• Dealing with legacy data during migrations/acquisitions

• Privacy concerns on a global scale

Regulatory Viewpoint: Evolution and Progress?

• Changing comfort level/interest in AI and machine learning among regulators

• How can you encourage a shift in perspective?

• Treat the regulator like your mother-in-law

• Collaboration vs. confrontation – Canadian experience

Great Expectations, Great Results?

•What have been actual outcomes in terms of reduced costs, efficiencies, etc.?

• Are you being realistic about risks you’re managing and efficiencies you expect?

• Educating the business and user space

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Alert

No Alert

47% Auto No-Alert

Lower Threshold

23% Auto Alert

Upper Threshold

Using ML in Level-1 Analyst Screening

Great Expectations, Great Results?

Where can things fall down? • QC and oversight - Human mistakes accumulating in

models you're not checking

• Guardrails around systems - Input anomaly detection

• Events that scramble “normal behavior” – E.g. COVID-19

Questions?

THANK YOU!

Next session: Anatomy of a Synthetic ID Fraud Scheme1:00 PM ET