Financial institutions collect data from countless touchpoints, including call transcripts, chatbots, branch visits, transactions, and more, yet many struggle to extract meaningful insight from these interactions. Specifically, understanding the “customer task” or the paths that lead to significant outcomes remains a persistent challenge. Solving this unlocks something valuable: a clearer view of the intent behind customer behavior, enabling better offers, smarter services, and stronger retention.

While these discrete event sequences aren’t traditional NLP, they carry their own vocabulary and context and timestamps. This talk explores how to build both white-box and deep learning transformer/generative models and walks through the tradeoffs between accuracy, explainability, and inferencing complexity. The result is a practical framework that lets businesses select the right model for the right use case, whether regulatory constraints apply or not, while still achieving the same core objective.