Few organizations are as data driven as financial institutions! Banks have tons of customer information stored in the form of structured data and if you add unstructured data that flows in from emails social networking sites, blogs and search queries, et al… the amount of data financial services organizations are bombarded with every day is unbelievable!
As financial institutions look to architect more customer-centric strategies, sifting through the data deluge from multiple sources and separating the “noise” from actionable information is the need of the hour. While banks have invested in tools and resources to examine this ‘data’, bankers will tell you that they have traditionally relied on statistical sampling techniques because it was simply too unwieldy to search, organize and analyze the entire data set. This no longer has to be the case, using new technologies financial institutions can get better at intelligently organizing, analyzing, and translating data into actionable business insights.
Why Big Data Analytics?
It has been a big awakening for banks and financial firms to understand that big data is definitely a big deal and recognize that advanced analytics offers the opportunity to redefine the playing field.
It’s all about the customer: Today there is a new generation of customers who like to be served where they want and when they want. But many of them don’t know what they want with respect to managing their financial life. To serve this new generation of customers, banks must be able to understand and anticipate their needs. Financial firms can leverage big data analytics to deliver targeted information, subsequently improving customer acquisition and retention.
And about managing risk: Banking is, inherently, a ‘risky’ business. Inaccurate assessment of a customer’s risk profile at the time of acquisition or subsequent behavior or transaction patterns can mean increased likelihood of default and charge-offs. Although, banks are managing these risks today, the explosion of unstructured data on prospects and customers can provide new insights to improve risk management. Big data analytics can get to these additional insights on customer preferences, social influence, life events or their immediate financial needs. While we only discuss customer analytics in this blog, it is important to note that big data analytics can extend to other areas such as, performance, balance sheet and portfolio management as well as fraud detection and more complex risk management strategies.
Better Information and Analytics to Grow the Business
Before jumping onto the big data bandwagon, financial organizations need to develop a systematic approach with desired business outcomes in mind. Let’s examine how.
At the cost of sounding simplistic, let me say, any big data implementation needs careful planning, with clearly defined objectives. This could vary from something as well defined as detecting and mitigating fraud, improving customer acquisition rates, increasing number of relationships or increasing customer retention to something more exploratory as sentiment analysis of new product launches. To unlock the true potential of big data, it’s important to have these answers first. Otherwise, it’s like having all the solutions and not knowing what the problem is.
Once the objectives are defined, organizations need to focus on data management disciplines to address the 3 V’s of big data – volume, variety, and velocity. This includes addressing key questions on what data is relevant, how do we extract and organize this data, what data do I store, what data should I process in real time and then discard. Banks also need determine what is the right processing platform, and what type of analytics should be run on a particular data set to derive actionable insights. Critical to this effort is having the right team of experienced data scientists that know where to find the haystack and how to get to the proverbial needle in the haystack.
Finally, it’s important to select the right platforms that are flexible and scalable to support big data efforts. Open source platforms such as Apache Hadoop is becoming a de facto platform of choice for such large scale data processing. By investing in a fabric based architecture that supports Hadoop as well as other in-memory or data-grid vendor products in the market, banks and financial organizations can effectively scale-up and scale-out their big data platforms in the future
Financial services organizations should follow the lead of other industries like retail or e-commerce to make their data actionable, or risk new competitive threats from Google, Apple, and PayPal. Analyzing large amounts of data is not easy; with the availability big data technologies, banking and financial services professionals can tap into the information goldmine and not just improve customer experience, but also drive new revenue streams and increase profit margins.