From ‘nice to have’ to ‘essential: Leveraging how organisations view digital for long-lasting innovation

• The pandemic has proven, once and for all, that the future is digital. How do you build on that proof of concept?
• Moving from the perception of digital being a nice to have to an essential, and the implications of forced adoption of digital channels.
• Leveraging that change in mood and opinion for long-lasting innovation and digitisation across your business

End-to-End Governance and Scale of AI and Model Driven Initiatives

Enterprises have strict risk, regulatory and compliance policies today.  When it comes to AI, those policies are continuing to evolve.  In this session, Stu Bailey will share a case study of a large financial institution that is using AI to better add new layers of defense for fraud detection, and how they successfully established an audit-ready ModelOps practice that also reduced model operational costs by 50%.

FOCUS GROUP TOPICS:

• Modernization of operating models – How will organizations structure themselves and think about themselves in a world where you have far more AI, cloud, and other technologies? Discuss what a modern financial services organization should be, and how to integrate change.
  Agus Sudjianto – EVP, Head of Corporate Model Risk – Wells Fargo
• Automation and back-end digital transformation – Exploring the technologies enabling back-end digital transformation, and how these are becoming more of a priority in the wake of COVID.
  Siddharth Mehrotra – SVP, Head of Data Science & Analytics Technology, Citi Velocity – Citi
• ML Innovation to Production in Consumer Financial Services
  Steven Dickerson – SVP and Chief Data Scientist – Discover

  Raghu Kulkarni – VP, Data Science – Discover

• Managing the machines: ensuring transparency and explainability with AI – Building a strong governance framework: where does AI pose the most risk to financial services organizations?

• Jacob Kosoff – Head of Model Risk – Regions Bank

Agus Sudjianto – EVP, Head of Corporate Model Risk – Wells Fargo
Jacob Kosoff – Head of Model Risk – Regions Bank
Siddharth Mehrotra – SVP, Head of Data Science & Analytics Technology, Citi Velocity – Citi
Richa Sachdev – Head of Machine Learning Engineering – Vanguard
Dave Trier – VP, Product – ModelOp

Enterprises are investing in AI with the intent that it creates market differentiation for them through unique service offerings and improved business operations.  But ModelOps (Model Operations) of AI models is still a barrier to high-quality and scalable AI for many organizations.   In this session, we discuss challenges and best practices for ModelOps based on lessons learned from industry leaders.

• Who leads AI/where does it fit organizationally/who is involved (functional team)?
• What is the role of IT in AI projects?
• Impactful AI initiative – what’s required?
• What gaps have you discovered as you implement AI?  How are you addressing those?  What types of tools have you added to make AI projects more efficient/effective?
• How important is AI in creating a competitive advantage for your company?

Using AI to Model Uncertainties in the Financial World

• Understanding the driving forces behind these uncertainties and how the pandemic has serviced as a catalyst for change
• Exploring unique challenges in the financial world related to:
  • Missing data
  • New patterns that keep emerging with the changing world
  • Developing long term strategies
• How AI can be used to effectively manage these uncertainties
  

Moving at the speed of data: Towards real-time analytics

• Understanding the critical elements of effective real-time analytics
• How can real-time analytics enable new products & services?
• Real-time risk management: what are the most powerful use cases?
• What kind of opportunities are new connected devices creating?
• How can behavioural and real-time data best be combined into actionable insights?
• What role can unstructured data play in developing real-time analytics?
• How far are we from realising the dream of real-time data management

FOCUS GROUP TOPICS:

• Modernization of operating models – How will organizations structure themselves and think about themselves in a world where you have far more AI, cloud, and other technologies? Discuss what a modern financial services organization should be, and how to integrate change.
  Ea-Ee Jan – VP Machine Learning – Goldman Sachs
• Automation and back-end digital transformation – Exploring the technologies enabling back-end digital transformation, and how these are becoming more of a priority in the wake of COVID.
  Ben Maxim – VP, Digital Strategy and Innovation – Michigan State University FCU
  
• Learn more from less data strategy reduce the need for labeled data to apply Machine Learning. How companies can apply Machine Learning and Reinforcement Learning with fewer data and less real-world experience.
  Prashant Dhingra – Managing Director, Machine Learning –  JP Morgan Chase
  
• Best practices for model risk management to promote AI benefits and mitigate risks – How should we manage policy and guidance updates to keep up with rapid changes in AI? How can we effectively manage risk from dynamic model changes or more frequent model updates? What insights have we gained from exploring various AI explainability tools or techniques for specific use cases?
  Xinyu Wu – SVP, AI/ML Model Validation – U.S. Bank

Ea-Ee Jan – VP Machine Learning – Goldman Sachs
Ben Maxim – VP, Digital Strategy and Innovation – Michigan State University FCU
Prashant Dhingra – Managing Director, Machine Learning –  JP Morgan Chase
Sri Krishnamurthy – CEO & Chief Data Scientist – QuantUniversity
Diego Oppenheimer – Executive Vice President – DataRobot

• Exploring the shifts in customer behaviour during the pandemic that reinforced the need to engage customers more effectively.
• What lasting changes will this lead to with respect to radically shifting customer demands, and how can we meet their new and altering expectations?
• How do you understand the new and evolving needs of your customers, in order to better serve them?
  
• Offering examples where training, mentorship, job shadowing, and cycling through different job functions improves retention, job satisfaction, and contributes to improved performance
• Capitalising on advances in digital onboarding and digital servicing to sustain lasting improvements in customer-centric services.