The scale of data in financial services is, although significant, dramatically smaller than it is for online consumer companies like Google or Amazon. Nevertheless the need for real-time throughput makes financial markets data a unique challenge and there are tremendous learnings and benefits to be had from the Big Data world not only in terms of how we manage data at enterprise level, but also in how we are able to present and manipulate that data through financial markets
Luckily, you don’t need exabytes of data for Big Data technologies to be useful. In financial services we are able to use these technologies to deal with the data issues we do have, and, once we have a Big Data infrastructure in place, it is then very easy to create and manage all kinds of related data with knock-on benefits in terms of how quickly desktop users can find, analyse and manipulate information.
The financial services industry has different issues to deal with specifically due to the structured nature of its data. We are dealing with complex data that supports a number of analytic use-cases, including large matrix retrieval, complex time series analytics, aggregation and screening. And this means that we have to adapt Big Data technology rather than apply it wholesale.
Data management, storage and retrieval is just one aspect of Big Data. The other equally important aspect is what becomes possible when you can quickly process large amounts of data. The more data you have, the more statistically significant it becomes, which means that you can use a variety of statistical methods to tease magic from all that data. These statistical methods lie at the heart of Google’s search engine and Amazon’s recommendation engine. They allow for the clustering of related news articles and for LinkedIn and Facebook to suggest people you probably know. Similar methods can be used for the financial markets desktop to help customize search and navigation for a particular user. For example we can look at patterns in news readership and make sure the search engine is continually learning and improving the results it returns.
Customer data is stored across different divisions responsible for specific functions like loan or portfolio management, as well as legacy system in many banks prevent them from integrating the data which leads to the lack of seamless single view of the customer.
Another significant issues are time taken to analyse large data sets and shortage of skilled people. Also, big data is not viewed strategically by management, rather like another IT project.
In order to take an advantage of big data banks need change their traditional IT approach and start implementing new technologies and processes, otherwise they will continue seeing a return of just 55 cents on every dollar that they spend on big data.
Big Data to drive personalized videos for customers as part of the onboarding process as well as to promote new products to banking customers. Its SmartVideo solution supports initiatives for banking institutions to move to top of wallet, increase customer loyalty and increase share of wallet by providing individuals differentiated experiences. It drives activation and early utilization with a personalized video presentation that proactively educates the customer about his or her specific account, briefly explains the first statement, highlights value-added services to get the most out of his or her new account and recommends actions that deliver a better customer experience.
With banking fraud on the rise, big data analytics represents one of the best potential security solutions. In particular, online credit card fraud remains a major issue, so big data software provides banks with a resource to stay a step ahead of cyber criminals.
You can map the entire data landscape across your company with Big Data tools, thus allowing you to analyze the threats that you face internally. You will be able to detect potentially sensitive information that is not protected in an appropriate manner and make sure it is stored according to regulatory requirements. With real-time Big Data analytics you can, for example, flag up any situation where 16 digit numbers potentially credit card data are stored or emailed out and investigate accordingly.
Data gets really big when you start to look at unstructured text and this is the heart of the Big Data challenge. Financial markets have seen a dramatic rise in the volume and influence of industry blogs, social-networking and commentary websites and, as behavioral finance plays an increasing role in investment and trading strategies, we are able to use Big Data techniques to look beyond structured numerical data and apply statistical methods to news text that provide sophisticated sentiment analysis.
The banking sector stands to benefit significantly from big data, provided institutions collect the correct information, process it quickly and effectively and use the resultant insights to make informed changes to marketing and sales efforts, approaches to risk management, customer service strategies.