CDAO APEX Financial Services schedule

Srini Masanam

Global Head of Surveillance Data Quality

CITI

Lekha Bangerjee

Senior Vice President, Technology Executive, Head of Data & Analytics, Enterprise Blockchain Leader

TRUIST

Thomas Depaoli

Vice President, Digital Analytics Services, Engineering Manager

U.S BANK

Jasmeet Singh Bhatia

Vice President, Strategy and Product Analytics

TRUIST

• How can you gain, and maintain buy in from those horizonal and vertical to you?
• What are the key principles to driving a culture of data? Do these really work? 
• How to effectively navigate people process and change management as part of your data strategy
• Data monetization and new revenue streams; what are some strategies to monetize data assets and create new revenue streams, whilst ensuring compliance with privacy regulations?
• What does good engineering, infrastructure and architecture look like in your enterprise?
• What are the latest trends in financial technology and how can data analytics be leveraged for innovative solutions? 
• Designing and delivering data governance and analytics capabilities in line with business goals

-    Navigating your security and privacy obligations when using AI 
-    How to manage the risks of AI in a rapidly changing landscape in order to remain safe, but innovative 
-    AI governance and model explainability
-    How to create transparent AI models and what governance frameworks are needed to manage them
-    How to keep bias out of data to ensure fair and ethical AI
-    Ethics from the top down. Your commitment as a leader

This presentation is specifically designed for leaders, architects, developers, and business executives specializing in data, analytics, AI, and machine learning within the financial sector. It presents a practical and stimulating blueprint for the effective development of data and AI products and platforms within their organizations.

The discussion will focus on cutting-edge methods to improve decision-making and operational efficiency through data-driven insights, while ensuring scalability, compliance, security, and substantial business value. The aim of this session is not only to educate but also to invigorate attendees, equipping them with the necessary tools and concepts to adeptly navigate the intricate world of AI and data analytics in the rapidly transforming financial industry.

Diego de Aragao

Senior Vice President, Balance Sheet Management & Analytics

CITI

Rajiv Prakash, 

Head of Data Engineering, Bank & Digital 

ALLY

Erum Manzoor

Senior Vice President

CITI

Agus Sudjianto

Executive Vice President

Head of Corporate Model Risk, WELLS FARGO

• What are some of the potential risks associated with generative AI and the need for robust ethical frameworks
• Address challenges of protecting sensitive information, ensuring compliance with data regulations, and addressing the potential vulnerabilities
• Explore strategies for implementing privacy-preserving techniques to maintain integrity and confidentiality of data
• Insight into advances predictive modelling techniques for risk assessment, credit scoring, market analysis and investment strategy formulations
• Uncover how organizations can harness generative AI to generate novel ideas, prototypes, and insights, ultimately driving innovation and competitive advantage
• Discuss the potential of generative models to create realistic synthetic data

Pawan Verma

Chief Data Officer

BANK OF CHINA

Satya Choudary, Vice President, Data & Analytics, Global Head of Clout Platform, Data Engineering & Management, Data Science & ML/AI

CREDIT SUISSE

Aweh Teh

Vice President, Data Governance & Privacy Engineering

CAPITAL ONE

• Highlight the importance of fostering a data-driven culture essential to deliver growth and value amid cultural barriers
• Fostering an environment that enables self service data
• Addressing resistance to change within a large organization, and the importance of looking to the future
• Upskilling and reskilling the workforce to succeed in an evolving AI age
• How can enhanced culture and upskilling of the workforce improve business performance and data driven decision making
• Understanding the impact of a poor culture can hinder data sharing, quality and collaboration to foster new insights
• Discuss how to effectively encourage collaboration and leverage collective expertise
• The importance leadership in setting a data-driven vision for your organization

• Understanding how data management impact customer satisfaction and business growth. How can this be leveraged?
• How can data warehouses, data lakes, the cloud and advanced analytics tools can improve data management?
• Meeting regulatory compliances and privacy laws
• The future of data management in an increasingly digital world
• Best practice and strategies for data resilience

• Leveraging data analytics and AI to inform strategic decisions
• Implementing ML algorithms to personalize products and services
• Harnessing AI to extract actionable insights from vast datasets
• How to strategically move applications, data and workloads to the cloud
• How can moving to the cloud allow businesses to quickly adapt to changing market dynamics, scale resources as needed and respond to disruptions

• Discover best practices for ethical AI implementation, including responsible data collection, unbiased algorithm development, and transparent decision-making processes 
• Understand the importance of cross-sector collaboration and partnership-building to maximize the impact of AI for good  
• Keeping transparency at the forefront of AI in order to maintain trust and regulatory compliance
• Explore real-world case studies where data leaders successfully implemented AI-driven initiatives that resulted in tangible benefits for communities and the environment 
• Gain insights into strategies for measuring and evaluating the effectiveness of AI for good projects, demonstrate the value and outcomes of their initiatives to stakeholders and the wider community 
• Examine ethical considerations in algorithmic trading, robo-advisors, and investment strategies
• How can AI potentially impact market integrity
• Discuss the impact of ethical AI practices on public perception

• Defining clear data ownership, establishing data standards, and enforcing data quality controls
• Creating data dictionaries, data lineage tracking, and metadata management to enhance data understanding, traceability, and compliance with regulatory requirements
• Implementing data governance policies and procedures, including data classification, access controls, and data retention strategies, to protect sensitive information and meet compliance standards
• Conducting data audits, data quality assessments, and implementing data validation processes to proactively identify and address data quality issues
• Foster a culture of data governance and compliance, providing training and awareness programs to educate employees on their roles and responsibilities in maintaining data quality and adhering to data governance policies

Dimple Thakker

Head of Data for Machine Learning & Intelligence Operations

J.P MORGAN CHASE

Eliot Puplett

Chief Executive Officer

MEDICI.AI

Brandon Walker

AI Strategy Lead

Rocket Mortgage

• The pros and cons of natural language processing to provide instant customer support and streamlined interactions
• Exploring the intersection of AI and human collaboration in data analysis, focusing on how AI can augment human decision making rather than replacing it
• How to leverage AI and ML to make data driven decisions and improve risk management and therefore potential financial loss
• Ensuring C-Level executives and board members understand the newest developments
• How AI and ML can be used for behavioral analytics
• Innovative products and services paving the way for the future. How do you remain competitive?

• Successfully navigating the rapidly evolving landscape of emerging technologies and determining which ones hold true value for your organization
• Integrating emerging technologies into the current technology stack
• Overcoming resistance to change and fostering a culture of experimentation and innovation
• Investing in skilled resources and expertise - acquiring the right talent to effectively deploy and integrate these technologies into the current stack

• Deploy AI to gain a deeper understanding of your customers, make data-driven decisions, and deliver highly relevant and personalized experiences
• Process and analyze data in real-time
• Leverage predictive models to forecast customer behavior
• Promoting explainability and transparency in AI systems empowers to provide clear insights into how AI models make decisions, enhancing trust, and facilitating better understanding of AI-generated outcomes

• Balancing the need for robust data governance and compliance with the demand for agile and flexible data solutions
• Navigating the complexity of integrating disparate data sources and systems to enable seamless data flow and accessibility, while ensuring alignment with the evolving needs and goals of the business
• Continuously adapting and evolving your data management strategy to keep up with the scalability and agility demands of the organization
• Leveraging big data and advance analytics for developing sophisticated investment strategies, market analysis and portfolio management
• Effectively communicating the value and benefits of aligning data management strategy with business strategy to stakeholders and securing their buy-in

Ravi Prakash

Head of Data Engineering, Bank & Digital

ALLY

Ravi Bandaru

Director, Strategy & Innovation Lead, Enterprise Data Management Operations BANK OF AMERICA

Tu Le

Head of Retail Bank Operations Analytics

USAA

Jennifer Mezzio

Global Data Officer

SILICON VALLY BANK

• Understanding the importance of your data strategy being part of your business strategy
• Discuss the role of a data steward in ensuring data quality and compliance
• Understanding the importance of robust data architecture in effective data management
• How can data architecture improve and support scalability, performance and data integration?
• How can data quality be continuously be improved in order for data driven decision making