As customers expect to transact with speed and convenience anywhere they choose, the adoption of AI in banking has seen early success with innovation in digital-first payments and lending products. However, the widespread adoption of AI products is still to come with an overall $1 trillion in opportunity.

Driven through real-world use-cases in credit risk modeling and lending decisions, this talk will explore the impact of AI in banking from the lens of value creation, governance and technology. The talk will draw parallels between the traditional software engineering process and AI workflow and analyze the challenges in operationalizing at scale. We will investigate cloud-native solutions that are available in PyData ecosystem to answer for challenges in model training, model management, model deployment and monitoring. Furthermore, an overview of AI and Machine Learning techniques that are particularly relevant in banking (e.g. Gradient Boosting) will be provided. All code examples used the materials will be provided as a Github repository for the audience to explore the topic in more detail.

This talk is intended for data scientist and product owners who are interested in analytics as applied to consumer finance products.