Presentations

Bank with Big Data – Data Science Use Cases in Finance with Hortonworks and Couchbase

Bank with Big Data – Data Science Use Cases in Finance with Hortonworks and Couchbase

Corporate IT organizations in the financial industry have been tackling data challenges at scale for many years now. Traditional sources of data in banking include Customer Account data, Transaction Data, Wire Data, Trade Data, Customer relationship management (CRM), General Ledger and other systems supporting core banking functions. Shortly after these “systems of record” became established, enterprise data warehouse (EDW) based architectures began to proliferate with the intention of mining the trove of real world data that Banks possess with an intention of providing Business Intelligence (BI) capabilities across a range of use cases – Risk Reporting, Customer Behavior, Trade Lifecycle, Compliance Reporting etc. .Banks, insurance companies and securities firms that have begun to store and process huge amounts of data in Apache Hadoop have better insight into both their risks and opportunities.


So what capabilities do Hadoop & NoSQL add to existing RDBMS based technology that did not exist before?The answer is that using Hadoop & NoSQL a vast amount of information can be stored at much lower price point. Thus, Banks can not only generate insights using a traditional ad-hoc querying model but also build statistical models & leverage Data Mining techniques (like classification, clustering, regression analysis, neural networks etc) to perform highly robust predictive modeling. Such models encompass the Behavioral and Realtime paradigms in addition to the traditional Historical mode. This talk will explore such key business challenges and how Hortonworks/Couchbase are driving innovation in the industry around Descriptive Analytics and Data Science.