Banks and financial institutions are constantly collecting our personal data whenever we transact with them. The result being that these organizations hold vast amounts of data, often lying dormant. In recent years banks and credit unions especially, have started to wake up and are mining this data to extract behavioral and spending information. However, this data doesn’t come in a nice, organized little package ready to be used. The process of sorting, categorizing and making sense of the mountain of data is also known as big data analytics.
Big data analytics is performed by examining large pools of data to unlock previously unknown client preferences, market trends and tons of helpful business information. By uncovering these hidden trends and analyzing the information, financial institutions are able to exploit new revenue streams, implement better customer service and build a more focussed marketing strategy.
The development of big data mining technologies is creating profitable opportunities for banks, especially with the growing domination of the internet and the ever increasing usage of mobile devices. But how exactly can big data analytics be used?
Big data IT technologies
With the emergence of big data and data mining came the need to develop powerful new IT technologies and processes to cope with the analysis of information. Stream analytics, predictive analytics and data virtualization are some of the technologies now associated with big data analytics. Although each technology has a different purpose, all of them are developed with a common goal in mind: the analysis of large volumes of data, producing value-added results in a timely manner. As more investment is plowed into IT technologies, these processes are bound to become quicker and more effective.
Point analytics simply refers to different data analytic procedures focused on a specific area, demographic or sector. So instead of analyzing the entire data set to expose certain trends, it will look for very specific information. It can look at specific risks or market potentials, for example, and perform an analysis on that data set only. This makes the process quicker and much more focused.
Benefits of Big Data Analytics in Banks
Banking is ahead of the crowd when it comes to big data analytics. We can see from the above how data analytics are implemented, including some of the processes used. But why go through all the effort of mining and analyzing data?
By analyzing its customer’s personal circumstances, spending habits and earnings potential banks can get a clearer understanding of personal needs. Understanding individual needs helps them to focus their marketing efforts. Instead of just having a blanket marketing strategy, they can now focus it on a client’s circumstance. So if one of their client’s took out a mortgage for a new or first time home, they might also then offer the same customer home and contents insurance. This will greatly differ from the services they would offer a student who’s looking for a student loan. Focused marketing greatly increases the likelihood of customers buying into the services offered.
Performance analysis for products and employees
Internal data can be analyzed to determine individual employee performance, including the identification of ideal times to schedule training and education (i.e. off-peak times). Goal progression and achievements can also be analyzed in real-time, instead of only doing it once a quarter or once a year like most industries. This real time analysis can afford banks to intervene much quicker if needed and identify training needs early on.
Additionally, data analysis can also be implemented to determine the performance of specific products or services. Again this will afford institutions the ability to develop or discontinue services depending on the response they receive, thereby maximizing profit and cutting losses.
Risk is embedded within banking operations. Every loan, investment or overdraft granted to a customer carries a risk of not being paid back. Data analytics provides banks and financial institutions with fresh insights into their customers and the environments which they operate in. Data captured from a regions economic performance and sales trends could give valuable understanding into that specific housing market. This will help them with their screening process when providing mortgage services, including what type of mortgage would be suitable. Additionally, financial institutions can analyse external as well as internal data to uncover fraudulent practices and implement measures to mitigate this risk in the future.
Complying with regulations
Regulations within the banking industry are growing ever more stringent, not without good reason. However, this does put the financial sector under a lot of pressure and it can be very difficult to keep up with all the new legislative and regulatory requirements. The process can be made simpler by data collection, analysis and implementation. Instead of looking at every piece of regulation, data analyses can highlight specific sections applicable to certain industries or organizations. This will make it easier and more cost effective for them to comply.
By incorporating big data analytics into their core processes and platforms, banks and financial institution will be able to take advantage of the opportunities that it offers. It goes much deeper than traditional statistics and it has the potential to reinvent and improve almost every aspect of banking.
This article originally appeared in nymbus.com. To read the full article, click here.