Mining Sequence of Events using Deep Learning for Predicting Next Best Action Qorus Banking Innovation Awards 2023

In our continuous quest to enhance customer experience and tailor our products/services to individual needs, we have developed an innovative AI-driven solution known as the Event Sequence Mining Engine for Predicting Next Best Action. The AI engine leverages the power of Deep Learning (LSTM) to analyse time series led sequence of events happened in customer life to anticipate customers financial needs and provide them with personalized product recommendations. The bank has over 60 million customers conducting more than 480 billion interactions annually across various bank products and platforms, whether it's routine interactions like booking a cab or buying groceries, or significant life events such as Childbirth, marriage, buying a home etc. These interactions and sequence in which these interactions happen, leave behind a rich tapestry of insights about the customer, essentially capturing the financial journey of each customer. The objective is to analyse each customer's life journey captured in the form of events that happened over a period overlaid with their persona and life stage and recommend the most suitable product for cross sell. Conventional machine learning models lack the ability to analyse intricate patterns involved in event based sequential data and further fail to capture the full context of customer behaviour. As a result, recommendations tend to be less targeted and lack the necessary context for driving meaningful engagement with customers.