There is a new universe called Data created by mobile devices, social media, web blogs, RFID and other sources. Big Data is the next generation of data warehousing and business analytics and is poised to deliver top line revenues cost efficiently for enterprises. Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze. Big Data analytics are the natural result of major global trends: Mobile computing, social networking and cloud computing. Traditional data management and analytics software and hardware technologies, open-source technology are merging to create new alternatives for IT and business executives to address Big Data analytics. Embracing big data is to gain valuable insights for your organization customers and by looking at data in a new way. Organization in every industry needs to explore big data and gain insights. We understand that, technology advances over time, the size of datasets that qualify as big data will also increase. It can vary by sector depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry.

Big Data in Financial Services:

Financial services have always dependent on a data-driven business. The advent of the widespread commercial use of the Internet was helped to relatively manage the interaction between professionals using specialized systems and it has ballooned into a deluge of big data from wide range of sources with strong implications for traders, bankers, brokers, agents and consumers.

Big Data and Banking

Retail and commercial banks have their own concerns for their customers. Shareholders place pressure on larger banks to increase deposits and improve overall profitability. One of the major obstacles to doing so comes from the corporate structure of many large retail banks, which credits individual branches for accounts and services obtained by those branches. Global banking and the rise of ATM, Internet banking has made banking in multiple locations relatively easy for the customers. For this, large banks themselves still struggle to align properly with ever changing customer. Smaller banks are unable to cope with new technology used by the larger banks and to increase its businesses; they are striving to manage their cost for new big data implementation. For example, a leading global bank has more than 100 branches and more than 1 million customers have the capacity to perform hundreds of millions of banking transactions monthly. Such banks are faced with the challenge of analyzing all of the data generated by its diverse network of branches and customer interactions.

A typical customer might open an account at one branch, change residence/business address address several times over the period of years, and perform most of his transactions at a branch other than the base branch or closer to his workplace. Yet the base branch where he opened the account gets the credit for the product he acquires and the deposits he makes. Even though the branch that performs most of the interpersonal direct marketing to that customer may not get benefit which is a problem if he’s conducting 90% of his transactions at a different branch or location. To address this challenge, the global bank use big data analytics to provide executives with improved analytics for better decision making. The application takes feeds from multiple sources including a multi-terabyte Teradata warehouse. It monitors all transaction records, allowing the bank to understand customer behavior so that they can conduct target cross selling, offer better customer service and maintain a more accurate internal revenue and compensation system. Customers now receive targeted offers from the branch they’re using regularly rather than the branch where they opened the account. The result can be visible in deposits balance growth over set goals, a large amount considering the bank’s national market position.

Big Data and Securities and Investments

The pace and scale of electronic securities trading has intensified tremendously over the past decade, and the amount of information available from execution venues such as exchanges, market data from commercial vendors, and internal analytical systems has never been greater. Many trading firms have had access to premium market data feeds from the likes of Bloomberg, Reuters, and FactSet for many years. Each of these systems has its own formatting and built-in analyt­ics. However, locating trends and anomalies across these systems and comparing them to internal datasets has always been challenging for financial firms, which now add vast reserves of unstructured data from news sources and Internet trans­actions in reservoirs such as Hadoop. Using big data, financial firms can now combine trader performance data, mar­ket data, unstructured news, user data, and general ledger data to gain previ­ously impossible insights in one user experience. This affords the split-second decision-making power that makes a difference between winners and losers on Wall Street.

Big data analytics business discovery capabilities are vital not only for competitive pur­poses. They also help firms respond effectively to a regulated community on high alert in the face of rogue trading and rates-manipulation scandals. Additionally, regulatory requirements such as Basel III and Dodd-Frank require added transpar­ency, agile analytics, and confidence behind decisions. For example, a leading Wall Street firm uses big data analytics to directly enable federal regulators to analyze hundreds of millions of trades without having to assign staff to the task of producing error-prone manual reports, or to accompany auditors on exhaustive digs through electronic and paper files. Instead of camping out at the firm for days on end, waiting for employees to scramble to get answers to ad-hoc questions, auditors can launch these queries on their own and get answers

Big Data and Insurance

The overall profitability of insurance companies is heavily impacted by the amount of money collected on underwriting policies (premiums) and the amount of money paid on losses (claims). Generally, these decisions are made based on pooled groups of historical customers related to statistics around geography, cata­strophic events, risks, and so on. This abundance of data makes targeted decision-making very difficult, especially as insurance companies have many isolated, legacy data sources and analytical tools. Several major US insurance firms now use big data analytics to analyze transaction-level claim, underwriting, and investment detail. Insurers can now correlate data across individuals and groups, cross-referencing claims with underwriting, actuarial, risk, and fraud detection data. Additionally, much of the data in an insurance claim is non-numerical and contains unique textual commentary. Big data analytics makes it possible to analyze unstructured text data against structured forms in the same platform. With millions of claims filed each year, large insurers can’t examine each claim at the same level of detail, but big data analytics helps these firms effectively analyze each incoming claim, flagging issues that merit further manual investigation by a Special Investigations Unit (SIU).

An American insurer entering the international market uses big data analytics to accelerate and enhance policy management as its customers move from country to country, all of which have different laws, regulations, and data standards. Patterns of fraud and demographic trends that were once completely opaque have now emerged because of bg data analytics ability to gather all forms of data from multiple sources and immediately display associations. A Tier 1 American insurer was using a traditional big data analytics to conduct claims analysis on large volumes of frequently updated claims transactions. More importantly, the insurer was able to find cases of fraud using big data analytics that went undetected using traditional reporting methods.

Conclusion

Big data represents a big opportunity for many industries, if organizations make an effort to place the right tools in the hands of decision makers. Much of the value of unstructured big data from new sources will come from correlation with the standardized, structured enterprise data businesses and institutions have carefully been collecting and managing for decades. Big data analytics platform will play a major role in the next decade to deliver big insights for many industries. Big data becomes a powerful asset for supporting a universe of organizational goals, from complying with regulations to holding down costs to keeping the human race happier and healthier.