Artificial Intelligence/Machine Learning: The CRO’s Agenda

  • Lois Tullo, Executive in Residence, Global Risk Institute
Graphical Image depicting streams of information convening and being filtered through an artificial brain.


Artificial Intelligence[1] (AI) was googled for 3.5 billion searches per day in 2018, over 40,000 search queries every second on average, and 1.2 trillion searches per year worldwide as listed by Internet Live Stats. With all the hype, what is important for CROs and financial executives to cull from the noise? I spoke with John Hull[2] and Ryan Riordan[3] at GRI’s 2019 RISK SUMMIT about how CRO’s are approaching AI opportunities and risks.

Business Strategies that drive AI Investment

To be successful AI must be embedded in a business’s strategy, deployed either to address a problem that the business is trying to solve, or to capture an opportunity that the business has identified. Financial services organizations have in recent years identified applications like:

  • KYC/AML[4]: accounted for 92% of all operational risk losses in 2018

  • Cybersecurity: the 2018 Cyber Incident & Breach Trends Report reported an estimated two million cyber-attacks in 2018, resulting in more than $45 billion in losses worldwide, while 2019 is seeing increases in overall attacks with the greatest increase in malware style attacks[5]

  • Fraud detection: the proliferation of misinformation through text and video has the power to potentially create a trust barrier to relying on information provided digitally

  • Portfolio Management: increased momentum towards fully automated trading, monitoring (risk management and compliance) and clearing and settlement (P2P, Centralized, Blockchain)

  • Customer Service: under pressure to become more efficient through automation, deploying AI across financial services has a $1 trillion potential. Predictions have been articulated of potential cost savings of $490 billion in front office (distribution), $350billion in middle office, and $200 billion in back office (manufacturing) functions.[6]


[1] Machine learning is an analytic technique that “learns” patterns in datasets without being guided by a human analyst. AI refers to the broader application of specific kinds of analytics to accomplish tasks like driving a car or identifying a fraudulent transaction.

[2] John Hull, Maple Financial Professor of Derivatives & Risk Management at the University of Toronto

[3] Ryan Riordan, Associate Professor & Distinguished Professor of Finance, Queens University

[4] OXR 2018 largest losses study for Know Your Client/Anti Money Laundering