Over the last decade, financial institutions have increasingly relied on data and data analytics to gain a competitive edge, as well as to minimize their exposure to risk and compliance issues. For instance: institutional investors apply sophisticated data analytics algorithms to historical data and streaming data, trying to find patterns and associations between the data, in order to determine which stocks to buy and sell. According to the Wall Street Journal, algorithmic trading now accounts for roughly one-third of foreign currency exchanges. When it comes to their own business operations, financial institutions use data tools for tracking compliance, assessing risk, and detecting potential fraud or security breaches.
For these reasons, many institutions are now utilizing data analytics platforms to help them make informed decisions based on contextual analysis of historical and real-time data. An enterprise-class graph analytics platform is a vital addition to this toolset as current methods generally rely on statistical pattern-matching. A graph approach would allow detection of cause-and-effect chains that are apparent by analyzing transactions and stock prices.
2015 marked an exciting year for all of us at Objectivity due to three important developments: we saw the continuing emergence of new technologies for Big and Fast Data, we also saw the growth in demand for Industrial IoT applications and solutions, and lastly, it was also a year of major accomplishments at Objectivity.
Over the last 12 months, we have seen a growing number of organizations elevate the strategic value of their data assets. The first generation of Big Data systems primarily focused on data ingest and batch analysis by leveraging more cost-effective scale-out and cluster computing. Now the emerging importance of Fast Data from various steaming sources, such as sensors, has brought about the recognition that tremendous competitive value can be achieved by narrowing the time gap between data coming in and actionable insights coming out.
The importance of narrowing this gap between data arrival and value realization is particularly great in applications around the Industrial IoT. From utilities and various manufacturing sectors to financial services, public safety and logistics, we witnessed an increasing instrumentation of devices in the form of massive sensor networks generating volumes of streaming Fast Data. As the volume of this new data grows, the need to accelerate sensor-to-insight has been growing at even greater urgency.
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 Federal Bureau of Investigation recently released its annual report, “Crime in the United States,” which stated that in 2014 there were 1.2 million violent crimes committed in America, 63.6% of which were aggravated assaults. In addition, 8.2 million property crimes were reported by law enforcement agencies; victims suffered financial losses of approximately $14.3 billion.
However, not all larceny occurs in “the real world,” per say. In a separate FBI report on Internet crime, cybercrimes accounted for nearly 270,000 documented incidents last year. These illicit activities, which include auto and real estate fraud, government impersonation scams and extortion, resulted in total losses of $800 million.
With the sheer volume of crimes being committed on a daily basis and the severity of the resulting financial damages, clearly more could be done to deter future incidents. Unfortunately, as technology advances, criminals become more sophisticated in their methods, and it becomes even more paramount to remain multiple steps ahead.
As a writer and marketer, I’m no stranger to using catchy buzzwords to succinctly explain an important concept that many people are facing. Despite the fact that buzzwords are used so ubiquitously that they are often added to the Oxford English Dictionary (including emoji, twerk, and cakepop, to name a few), they are not as beloved in the enterprise technology industry as they are among consumers.
The irony behind our love/hate relationship with buzzwords, such as Big Data and IoT, is that everyone throws these labels around, but no one can agree on what they mean.
To dispel some of this confusion, I’m here to discuss a term that Objectivity has been at the forefront for years: Information Fusion.