Big Data In The Number Crunching Financial Services Industry Posted on : Jun 24 - 2016

Information of extreme size, diversity and complexity – is everywhere. This unsettling phenomenon is destined to help organizations drive innovation by gaining efficient insight into their customers. The bulk of information flowing is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content. Historically, there was no way out to use this inflow of data with technology. But with the increase in social media interactions, use of multimedia, as more and more devices are getting connected to the internet, companies can capture data with higher granularity.

The Financial Scenario

The Financial Services Industry is amongst the most data driven of industries. The regulatory environment that commercial banks and insurance companies operate within requires these institutions to store and analyze many years of transaction data. The popularity of electronic trading has meant that firms generate and act upon hundreds of millions of money market related messages every day. In 2012, the US government released hundreds of more regulations on the financial services industry. Throughout 2013 and maybe beyond, the industry will continue to see more, if not less regulations. Numerous data sources that can be made available, from social networking demographics to consumer micro-payment consumption history which can be used to greatly improve models. With less and less client loyalty,  financial services companies can implement Big Data solutions to increase conversion rates and address customer retention issues by delivering increasingly more individually customized services. The opportunity for the sector is to unlock the potential in the data through analytics and shape the strategy for business through factual insight rather than intuition. Recognizing the data and creating value out of it is a significant corporate asset.

Creating Value Through Big Data

There can be four broadly applicable ways to dominate Big Data that offer more than potential to create value and have involvements for how organizations will have to be designed, organized, and managed.

Creating Transparency

Making data accessible to evangelists in a timely manner can create value. Also making relevant data more readily accessible across otherwise separated departments can sharply reduce search and processing time.

Enabling Experimentation

Organizations can collect more accurate and detailed data related to performance and other metrics. Using data to analyse performance, enables data managers to experiment with various methods to improve the productivity levels. These experiments can occur naturally or can be in an controlled environment that enable leaders to manage performance to higher levels.

Segmenting to Customize

Big data allows organizations to create highly specific segmentations and to tailor products and services precisely to meet those needs. This involves dividing a broad target market into subsets of consumers who have common needs and then be designed and implemented to target these specific customer segments.

Supporting Human Decision-Making

Analytics can help in improving decision-making, minimize risks and can dig out valuable insights that otherwise may not be possible. Such decisions may not always be automated but augmented by analyzing huge datasets using Big Data techniques rather than just smaller samples that individuals with spreadsheets can handle and understand. Big data techniques uses algorithms to optimize decision processes.

Big Data Applications

 Big data in Insurance

Insurance is a business based on information, analysis and relationships. Its core function of profitable risk management relies on the capacity to apply the concerned mathematics against data on investments and markets. The introduction of business intelligence software began the evolution of computing in insurance from a calculated, transaction focus to a strategic, business planning focus. Insurers capture data and information from every customer interaction, including information about customer needs, products, services, and claims. In addition to structured transaction data, insurers are collecting huge storehouses of information from unstructured sources. The natural language processing abilities that enable old automatic interaction with users also enable it to understand almost any unstructured text it can access.

Marketing materials, regulations, advertisements, as well as underwriting rules, claims adjuster and contact center agent notes can now be made available for analysis. More automated solutions are now possible that could give agents/financial advisors, or even the prospect, the opportunity to ask and answer a series of questions in conversational mode that would lead to product advice. Advanced analytics technology can integrate multiple streams of inquiry and produce consolidated views of near real-time activity to speed appropriate responses.

Big Data in Banking

In the world of banking, where the value of information cannot be over-emphasized. Besides the data that banks generate within their organizations and organize neatly within their databases, digital consumers are creating a profusion of unstructured data across non-traditional, largely mobile touch points. Banking has always been a data-driven business. Not surprising then, that according to Gartner, it is the most active industry in making Big Data inquiries (25 percent), followed by services (15 percent) and manufacturing (15 percent). As consumer banking relationships get fragmented across multiple channels and as data volumes increase exponentially, banks will have to invest in Big Data capabilities to manage volume as well as to extract new value. Big Data is increasing exponentially in volume, variety and velocity, and banks should be mining its depths to make an early discovery of emerging trends, customer needs and competitive practices. The problem however, is that most unstructured data - trapped in social network conversations, online ratings, YouTube videos, Facebook likes, blogs, user-generated content and so on - is unstructured and beyond the analytical capability of traditional systems.

Big Data for Lenders

Lenders are increasingly using sophisticated analytical tools, acting on data from a variety of sources, to identify relationships and patterns. Big data and deep analytics capabilities offer new opportunities to incorporate more data from more sources, including adjuster notes, agent comments, and news articles. Conversational front ends that better understand queries provide for more real-time assessments. The data can help analyze internal bank records and correlate them with other sources of information–to give banks a more accurate picture of their customers, and a better ability to predict which customers are likely and credit-worthy buyers of new financial products. This solution analyzes each customer’s liability to accept a particular offer and match that customer to the product offer that both maximizes profitability as well as sticks to underwriting guidelines, regulatory constraints, and internal business rules.

It is essential for banks to figure out how to put the information to good use. Being selective is the key to avoid getting buried under a heap of data. Indeed, the real value of Big Data lies in "small data" insights, which are uncovered after going through huge piles of information. So, while searching for Big Data for the next big thing, institutions should be aiming at clear visibility to value.  A number of major financial institutions are planning to use Big Data analytics to generate insight into risk which clearly impacts return and ultimately shareholder value. This can be a way forward into a future of better financial security. Source