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We develop a chatbot using Deep Bidirectional Transformer models (BERT) tohandle client questions in financial investment customer service. The bot canrecognize 381 intents, and decides when to say "I don't know" and escalatesirrelevant/uncertain questions to human operators. Our main novel contributionis the discussion about uncertainty measure for BERT, where three differentapproaches are systematically compared on real problems. We investigated twouncertainty metrics, information entropy and variance of dropout sampling inBERT, followed by mixed-integer programming to optimize decision thresholds.Another novel contribution is the usage of BERT as a language model inautomatic spelling correction. Inputs with accidental spelling errors cansignificantly decrease intent classification performance. The proposed approachcombines probabilities from masked language model and word edit distances tofind the best corrections for misspelled words. The chatbot and the entireconversational AI system are developed using open-source tools, and deployedwithin our company's intranet. The proposed approach can be useful forindustries seeking similar in-house solutions in their specific businessdomains. We share all our code and a sample chatbot built on a public dataseton Github.

A Financial Service Chatbot based on Deep Bidirectional Transformers