Deliver Smarter, Securer Financial Services

• Bernd Leukert, Management Board Member for Technology, Data and Innovation, Deutsche Bank

The world of finance is undergoing seismic change, with every process and touchpoint being transformed by technology. Banks face fundamental choices that will determine the role they play in the future. Deutsche Bank is overhauling its approach through focused bets on technologies such as cloud, AI, and machine learning, laying the foundation to seamlessly integrate its offerings into customer journeys. Hear from Bernd Leukert, Deutsche Bank’s management board member for technology, data and innovation, on how the bank is making moves to increase the value and convenience for customers by applying data-driven insights and transitioning the financial services ecosystem into virtual environments.

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• Srini Nallasivan, Executive Vice President, Chief Analytics Officer and Head of Artificial Intelligence, U.S. Bank
• Soumitri Kolavennu, Artificial Intelligence Leader, Enterprise Analytics and AI, U.S. Bank

The enterprise analytics group at U.S. Bank embarked on a journey to create a center of excellence (COE) for application of artificial intelligence to the bank’s problems across various business units. The artificial intelligence developments, specifically in natural language processing (NLP), computer vision, and graph analysis, are experiencing tremendous and rapid advancements in the technology and application areas they impact. To keep up with these advancements, U.S. Bank’s strategy office created the COE, embracing an open innovation model where homegrown solutions for the bank’s problems are juxtaposed and contrasted with commercial offerings. In this presentation, we’ll walk through the development of the center and highlight a specific use case—information extraction from collateralized loan obligations (CLOs)—that showcases the need for internal development, the sensitive nature of the data, ensemble development, and scaling up with GPUs.

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• Bryan Dai, Head of AI Lab, UBIQuant Investment

The quantitative model is the core of quantitative investment and the key carrier to realize the investment concept. With the continuous development of technology, AI models are becoming more and more widely used in the field of quantitative investment. With its powerful learning, cognition, and reasoning ability, an AI model can develop trading models for different markets and asset types more quickly and has faster self-iteration ability for rapidly changing markets. Through visualization, data mining, logical reasoning, knowledge graphs, and other technologies, the model construction and reasoning process is made transparent, and a certain understandability of the AI model can be presented to satisfy the attribution of returns and the control of investment risks in the process of quantitative investment.

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• Snehita Varma, Principal Data Scientist, Capital One
• Kal Mishra, Senior Manager of Data Science, Capital One
• Jesse Zymet, Principal Data Scientist, Capital One

Practitioners of automated, personalized online advertising are often challenged with the attribution problem—an inability to accurately credit a visitor’s conversion to appropriate experiences along their journey. The problem is commonly discussed in the context of ad performance reporting, but misattribution can limit the predictive performance of the ad recommendation engine itself. We present a state-of-the-art personalized recommendation architecture for Capital One advertising, powered by the ALBERT Transformer algorithm commonly employed in NLP tasks (Vaswani et al. 2017, Lan et al. 2019). We recruited the NVIDIA Merlin Transformers4Rec package (de Souza Pereira Moreira et al. 2021) to demonstrate that, for repeat visitors coming to the Capital One homepage, the Transformer outperforms an ensemble-based alternative model currently in production by a wide margin.

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