Technology has opened up a myriad of options for financial management. In the ‘80s and ‘90s, IT systems transformed the way banks managed their processes. Nowadays, it’s Big Data and advanced data analytics that are helping banks and businesses reinvent themselves. It is also providing a platform that may assist them in fending off FinTech competition.
Big Data analytics is the process of examining or analysing large data sets to unearth market trends, customer preferences, unknown patterns, data relationships, and other insights that can be useful to businesses and organisations. Data analytics helps companies make informed decisions and draw inferences so they can improve their processes and offer truly outstanding customer experiences.
Banks Capitalising on Big Data Analytics
Data analytics was born out of the need for analysing large and semi-structured internal and external data—something traditional analytical tools couldn’t handle with ease and efficiency. Big Data provides significant opportunities for banks. Since financial services organisations generate immense volumes of data, they can now use this information to monitor customer transactions in real-time, drive revenue generation through more targeted marketing, predict defects and fraud, and boost overall profitability.
What Statistics Reveal
Statistics published by the IDC suggest that worldwide revenue for Big Data analytics is expected to reach $150.8 billion this year. The report also indicates that banking is one of the industries that will be making huge investments in Big Data and data analytics over the next few years. As more and more banks are making the switch to Big Data, they continue to harvest huge amounts of data and are continually enhancing how to use it to meet their business objectives more efficiently.
How Big Data Can Improve Banking Services
Processing information faster
As customers continue to adopt digital channels rapidly, banks have to keep up with the increasing volume of information that’s becoming available. Small scale databases or traditional data management systems aren’t robust enough to handle large amounts of data. They also lack the multi-dimensionality of Big Data by which probabilistic behaviour can be more deterministic. Without making a move to data analytics platforms, banks won’t be able to process huge amounts of disparate data without encountering issues. Data analytics uses data intelligence to process information quickly and securely.
The financial services industry is increasingly relying on data analytics to fight fraud and identify threats. One of the biggest advantages of Big Data is that it’s able to predict malicious attacks based on consistent trends. Big Data also supports behavioural biometrics—the latest in online user authentication. These newly released behaviour-based authentication platforms unobtrusively collect data on users and create a unique profile for them, one that can’t be stolen or replicated by unauthorised or fraudulent users. Without data analytics, banks are going to find it very challenging to ensure online account security. They need a layered approach to prevent fraud, and an effective way to achieve just that is through strengthening authentication with Big Data.
Better Compliance Reporting
Governments and regulatory authorities continue to issue new regulations. The new standards and rules are coming into effect at a rapid pace. Banks are therefore forced to take a closer look at their compliance practices. Since it’s no longer cost-effective nor efficient to deal with these new rules and regulations using traditional methods, banks need data analytics to strengthen their compliance risk programs. Big Data platforms can integrate data from multiple sources and analyse large volumes of information in a short time, thereby reducing compliance analytic cycle times. Data analytics gives banks the capacity to adjust to the ever-changing needs of the industry and significantly lowers the cost of compliance reporting.
Track Customer Behaviour
Big Data gives banks deep insights into customer behaviour and spending habits. By analysing purchasing history, profile data, social media data, and browsing history, data analytics can help banks acquire new customers and retain existing ones. It identifies customer needs and assists the bank in categorising clients on the basis of certain parameters. There is a clear beneficial effect on cross-sell and upsell of products and services when such personalisation drives targeted campaigns
Banks can then use this information to develop new products or create further marketing campaigns to reach even more targeted audiences. Established banks in Australia are now using Big Data to reach all of their diverse customers. Westpac, for instance, is leveraging the power of data analytics to provide more targeted offers and campaigns to customers. They currently have an app with a geolocation service. If you turn on this geolocation feature when you’re travelling overseas, the bank might offer you additional foreign exchange or insurance services that suit your needs—a small peek at how Big Data can be used by banks to more effectively and creatively interact with their clients.
Fully Realising Big Data’s Potential
The future of data analytics looks promising. It can be used to dramatically add value to organisations, improve business outcomes, prevent fraud, and even combat crime. However, if you really want to harness Big Data’s full potential for your organisation, be it in the financial services or other marketing sectors, you need the right data management and interpretation platforms to do the heavy lifting to unlock otherwise unobtainable insights.
Latize’s intelligent data management platform Ulysses gives you the ability to seamlessly and intuitively derive useful insights from Big Data which you can use to improve business processes, boost marketing campaigns, and better understand your customers.