AI-Enabled Banking

With data science continuing to emerge as a powerful differentiator across industries, almost every organization is now focused on understanding their business and transform data into actionable insights. For example, organizations derive insights from vastly growing private and open data for improving services, to automate existing processes and to predict activities. In this context, organizing vast amount of data gathered from various private/open/Social/IoT data islands, i.e. Data Lake, will facilitate dealing with a collection of independently-managed datasets, diversity of formats and non-standard data models. The notion of a Data Lake has been coined to address this challenge and to convey the concept of a centralized repository containing limitless amounts of raw (or minimally curated) data stored in various data islands.
The rationale behind a Data Lake is to store raw data and let the data analyst decide how to cook/curate them later. While Data Lakes, do a great job in organizing big data and providing answers on known questions, the main challenges are to understand the potentially interconnected data stored in various data islands and to prepare them for analytics. In this project, we introduce an Intelligent Data Lake to facilitate the analysis of Big Banking Data. We introduce a scalable Data-Driven Intelligent Platform (DIP) to support growth in data storage and analytics and to prepare the foundation for digital banking.
The proposed platform will be a centralized repository containing virtually inexhaustible amounts of both data and contextualized data that is readily made available anytime to anyone authorized to perform analytical activities. This will enable the banking analysts to unlock the value of data so that better quality and faster insights can be delivered to the business. The proposed platform will be the strategic initiatives for the banking and the foundation for future compliances. The final outcome will be novel techniques in the fields of Data science, Cognitive Technology, Human-Computer Interaction and Visualization.

Innovation and Outcome. The anticipated outcome of this project includes:
• Objective 1: Significant scientific advancement in understanding the practical problems of understanding the banking data, resulting in a unified scalable Data-Driven Intelligent Platform (DIP) for banking.
• Objective 2: A generic and unified framework and models for contextualizing and summarizing the big banking data and prepare it for intelligent analytics.
• Objective 3: Innovative, fine-grained and intuitive analytical services to prepare the foundation for intelligent data-driven and knowledge-intensive banking.