Using big data to unlock the delivery of personalized, multilingual real-time chat services for global financial service organizations

Financial service clients demand increased data-driven personalization, faster insight-based decisions, and multichannel real-time access. Tim Walpole details how organizations can deliver real-time, vendor-agnostic, personalized chat services and explores issues around security, privacy, legal sign-off, data compliance, and how the internet of things can be used as a delivery platform.

In today’s data-centric world, clients are now demanding increased personalization, faster insight-based decisions, and the ability to access data at any time, using any channel. Financial services organizations are working to be more efficient, reduce costs, and improve customer engagement to drive revenue while building trust, mitigating the risk of successful cyberattacks, and complying with increasingly stringent regulatory frameworks. With the advances computational power and the richness of available data , delivery of services to help organizations with these challenges is now possible. These services will require personalization, artificial intelligence, machine learning, and big data at their core and need to be designed and delivered with an architecture that allows the use of today’s best-of-breed components while enabling the flexibility to rapidly change the components as newer technologies emerge. For geographically dispersed organizations, the issues are further complicated by the need to deliver services that work across different cultures and in multiple languages and conform to local data governance and compliance rules. BJSS has developed a working model of such an architecture, based on work delivered for clients in the financial and educational sectors. Tim Walpole offers an overview of this model and shows how it has been architected, developed, tested, and deployed using AWS cloud-native services. Tim also explains how the internet of things can be used as a delivery framework for conversation and voice chat and explores the associated security issues involved in supporting both unauthenticated and authenticated users for nontransactional and transactional queries. Tim concludes by examining some of the services available today to support natural language processing, sentiment analysis, language identification, intent identification, analytics, and the additional services required to support live agent hand-off, authentication, anonymization, personalization, and conversation fulfillment.