Data has long been viewed as a strategic asset by financial service institutions. Driven by a continuing need to address regulatory compliance and fraud detection, these institutions have developed deep expertise in analytics around both streaming and non-streaming data sources. Each customer transaction, act of fraud, credit default, and complaint provides organizations the experience from which to learn and create value from their data and better address their regulatory compliance requirements.
Financial fraud is on the rise. The Nilson Report shows that losses from worldwide fraud on credit cards, debit cards and prepaid cards hit $16.31 billion in 2014, a growth of 19% from 2013 with the U.S. alone accounting for 48.2% of this total. Fraudsters use very sophisticated and technology-enabled techniques. Some of the major victims of fraud are credit card companies, insurance companies, retail merchants, and telecom service providers. Fraud analysis is used to take various forms of stored data and streaming data and convert them into actionable insights to detect fraud and take necessary required actions.
The exponential rise in real-time customer data and transaction information, as well as the growing complexity of regulatory compliance, have driven the demand for advanced event-monitoring and fast response systems to support organizational processes. Because organizations in insurance, banking and capital markets industries are mandated to follow compliance standards, such as BASEL III and FISMA, there is a growing need for organizations to more efficiently monitor and predict compliance risks in advance, which ensures smoothness of business processes and removes inconsistencies in the face of changing regulatory and organizational requirements. The need for high-speed detection and alert mechanisms to support compliance processes led to technical enhancements in the form of rule-based, query-based, and status-based event-processing technologies.
The key parts of fraud and compliance analyses are the creation and management of data sets from streaming data and non-streaming data sources. As the complexity, velocity and volume of data, fraud patterns, and regulatory rules increase, the need to support complex, continuous queries across both streaming and non-streaming data sources have emerged as critical requirements in the financial services industry. In these applications, the focus is on real-time pattern and anomaly detection based on analytics for relationship discovery.
Because many of the non-streaming data sources are shared among multiple systems, these resources are updated by different systems during continuous query executions. As a result, continuous queries supporting relationship discovery may reference resources inconsistently and lead to problems. The need for transactional processing across continuous queries on both streaming and non-streaming data sources is taking on an increasingly critical role.
At Objectivity, we have been working for years on technological solutions to address the need to efficiently build data sets for relationship discovery involving both streaming and non-streaming data sources in the context of transactional processing. Our products have been deployed as part of a growing constellation of solutions for pattern and anomaly detection involving fast and big data. As the variety and volume of streaming data continue to increase, Objectivity will follow its tradition of leveraging semantic technology, streaming technology, and big data technologies to continue to provide the best platform for supporting analytics for relationship discovery across streaming and non-streaming data sources.
VP of Marketing and Partner Development