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Automatic Relationship Construction

Cognitive Banking Architecture - Human Centric AI framework for automated Customer Engagement in Banking

By Dr. Sivaramakrishnan R Guruvayur
Bankbuddy Dubai, UAE
Email-Id : shibu@bankbuddy.ai

The purpose of this paper is to inculcate the concept of a Human centric in Cognitive banking framework in an attempt to deliver customer-centric AI-driven customer experience and engagement platforms. The modern-day banks have the compelling need to hyper personalize their customer experiences across all digital and physical channels. They need to leverage the power of data analytica and AI to bring back "personal" in banking, to create micro-moments based on dynamic customer journeys and context based streaming recommendations. Building an intelligent banking customer engagement model that can achieve human like intelligence and still a provide "personal Touch" is an uphill task due to the heterogeneous nature of banking systems, processes, data sources & more importantly, the prevailing regulatory requirements. Cognitive architectures have been designed to deal with the problem of complexity. But, when it comes to Banking systems, most of the existing cognitive architectures like SOAR, SiMA, LIDA, etc., have not solved the problem of complexity completely. The main objective of the proposed work is to develop a domain specific customizable Artificial Intelligence (AI) cognitive architecture to enable banks and financial institutions a human-centric approach to banking. The proposed framework employs a mix of AI faculties like domain and context specific Prebuilt Multilingual NLP chat bots (voice and text) across banking products, big data analytics, recommendation system to build dynamic customer persona & suggest next best action, customer journey hub, machine vision for biometric authentication & document ingestion, Task bots for with micro services to enable frictionless transactions , Channel manager to add conversational AI to bring ubiquitous presence across traditional (mobile banking, branch, contact center and new generation channels like WhatsApp, Facebook messenger etc. and an in-built middleware to connect to a variety of core systems ranging from core banking to CRM and contact centre solutions.Key performance indicators such =as Customer and Channel profitability, ease of customer navigations, customer engagement measures, and cross-sell abilities, Operational efficiencies can be derived out of metrics and compared with the current monolith banking systems.