Keeping up with data in the financial landscape of the future

Keeping up with data in the financial landscape of the future

By Scott Hubbard, Managing Director ANZ, MapR

 

Changing consumer behaviour and the growing need for customer centricity are reshaping the banking and financial services industry (BFSI), however underpinning this is data and applications.

We are now living in a world where a massive amount of data is being generated by an increasing number of sources and it’s up to organisations to take advantage. In fact, IDC reports that worldwide revenues for big data and business analytics will reach $260 billion USD in 2022. For banks, the increase in both scale and speed of their applications and data processing, means they can leverage data analytics to create better services for customers. But a major problem for banks is that their fintech competitors can do the same thing, while offering lower fees and higher savings.

With KPMG reporting an approximate $86.9 million investment in the Australian fintech sector, the growth in this sector isn’t showing signs of slowing down any time soon. Additionally, Australia has a 37% fintech adoption rating, compared to the global average of 33%, according to the 2017 Fintech Australia Census.

So how can traditional financial institutions stay relevant in the banking landscape of the future? First, banks need to recognise the opportunities that data brings to their services, as well as the challenges.

Tackling the opportunities… and the challenges

Within this sea of big data lies opportunities financial services can leverage such as generating revenue streams through data-driven offers, using data to become more efficient to compete with fintech companies, and provide better services to customers, such as strengthened security.

Many companies today are striving to be data-driven, but the reality is that not all are successful. A common thread connecting companies that struggle with big data is their short-term focus. If companies want to succeed with data they need to avoid using it to accomplish one-off goals, instead they need to think strategically about how data can accomplish their long-term goals. This same principle applies to banks that want to keep up with their fintech competitors – plan for the future, or there won’t be a future for you.

The challenge however, is that this big data is both structured and unstructured and legacy data systems are simply unable to handle the volume, variety and especially the velocity of data flowing in. Without the help of next generation technology, banks will find it very difficult to store, analyse and manage this data.

To meet these challenges, financial services need to think about whether or not their digital infrastructure will be able to support this influx of data. Banking, financial services and insurance (BFSI) services need a unified data platform that brings all data into a ‘data fabric’ – an architecture and set of data services that provide consistent capabilities across a choice of endpoints. This way, financial services can break down data silos, allowing them to process data with speed, scale and reliability which intrinsically leads to faster innovation, collaboration and access to data.

Financial services need a comprehensive data plan

With the help of big data and the right platform to harness it, financial services need to devise a data strategy that aligns with their business goals, and act on it. To do this, companies shouldn’t only focus on singular goals, but consider the broader business goals. By developing a comprehensive strategy that spans across all departments, business not only can move forward with big data, but grow along with it.

Financial services can leverage the power of big data to solve core business issues and then expand on that solution to address other issues. For example, this data can be used to improve fraud detection, which is a major challenge in the growing BFSI sector. From this, financial services can then break up the overwhelming endeavor of using big data into smaller, bite-sized chunks that can be easily built upon in the future to address future issues.

While this may only be the surface of the multitude of challenges faced by banks when it comes to big data, companies that are willing to meet those challenges head-on will find the rewards to be well worth the effort.

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