It is safe to assume that banking data is some of the most sensitive available. Money is essential in the society we live in: Pre-2020, global wealth estimates measured at short of $400 trillion. The same statistics suggest that 52 million people globally have a net worth of more than $1 million.

How we spend and earn money is changing, too. Recent global challenges gave rise to discussions regarding digital money. Cryptocurrency is more popular now than ever before, with Bitcoin hitting historic peaks

At a simple banking level, there is an incredible amount of data to crunch. Banks are expected to offer digital services and to streamline access to funds. Not only that, but financial fraud is still rife. 2019, for example, saw a record 246,763 credit card fraud complaints.

Let's take a look at some of the significant data challenges facing the banking industry.

Privacy and Protection
Quality of Data
Failure to Adapt
Risk Assessments Aren't Working

Privacy and Protection

With banking largely moving digital, privacy is a key concern, more so now than ever before. Banks have a duty of care to ensure that their systems are steadfast and impervious. Financial data is, by definition, big money.

Global financiers should be taking extensive steps to safeguard financial data. While the average user may have two-point protection at home, they need further reassurance. Failure to protect critical financial data is not only disastrous for the user but also the bank.

The impact of global issues on the banking sector is widespread. Financial behavior in 2020 shifted to a digital mainstream. Post-2020, 31% more people are expected to continue banking via mobile and the internet. This is a new cluster of data the banking sector must prepare for.

They must not only demonstrate to users that their data and money are safe but to their staff, too. Data protection in an age of cyber-terrorism must be the chief concern over profit.

Quality of Data

At WinPure, we strive to help improve the quality of data handling and data itself. Data quality is a chief concern in banking, and dirty data is costing the banking industry trillions of dollars a year. Poor quality banking data may include missing payment information. It may also include duplicates and processing errors and failed applications.

Financial data is critical in the modern age. Every day, people depend on the quality of information banks hold on them. As banks grow, so does data. We are no longer living in a time when you are merely communicating with a local branch.

Banking systems must structure data in a way that is easy to access. Financial data can often be some of the most complex. Payments, income, credit, lending, interest, depreciation, account administration – there is a lot to define.

Therefore, high-quality data in the finance industry relies on clear definitions and structures. Without them, there is no uniformity, and user expectations are unclear. People expect modern banking to be quick, simple, and always accessible.

To provide that standard of service, it all starts with data. That is why banks must ensure their structured and unstructured data is clearly defined.

banking industry data quality
big data in banking industry

Failure to Adapt

Some research shows that the banking industry may not be too quick to adapt to change. As of 2019, around 92% of the 100 leading banks worldwide were still using outdated mainframes. This data will, hopefully, be changing post-2020, particularly with behaviors evolving.

Banking bodies reliant on older systems will be unable to evolve with global demand. Data is growing all the time, and as mentioned, digital demands are growing, too. Failure to adapt could lead to complete demolition of infrastructure – which in and of itself is time-consuming.

Therefore, it stands to reason that banks should consider a flexible means for data handling. The concept of a ‘data lake’ may come into play. That is where financial information is free-moving yet organized, as opposed to pigeonholed and restricted.

To achieve this flexibility, banks must be willing to look for answers to big data challenges. They must be ready to keep an open mind and to be fluid in the face of future demands. Failure to act could result in costly rebuilding and customer dissatisfaction.

Risk Assessments Aren't Working

As of 2019, it became clear that up to 38% of banks and financial bodies simply weren’t ready for the risk. By this, we refer to evolving and increasing risk. This ties in with our above point.

Many banks up to the recent GDPR rollout may have been inflexible regarding data and demand. Considering global challenges in 2020, these banks may now be struggling more than ever. With outdated systems and data management, the ability to manage risk can deplete.

That leaves banks wide open to cyberattacks and evolving fraud. This ties in neatly with our first point – that privacy and protection must be the priority for all banks. However, with an inefficient data processing standard, everyday users are likely to be at risk.

Banks must not only be willing to take on flexible, scalable data management systems but must also be transparent. People want to know that their money is safe. They will also need to know that their banks are ready to manage risk and fight back against fraud.

Therefore, banks require clear data management and risk assessment to future-proof their running.

How Can WinPure Help?

WinPure works with businesses across various industries to clean and re-organize complex, sensitive data. We work with the Bank of America and the World Bank in the financial sector, among many others.

WinPure presents a platform to banks that can help to organize data without duplicates and bottlenecks. Everyone uses and needs money. Therefore, it is critical banks and financial bodies take their users’ data seriously.

WinPure’s scalable data quality solutions can bespoke fit a variety of banking requirements. It can help manage third-party data streams and provide data handlers with greater clarity and confidence.

The way in which people use money is changing. It’s essential that we make sure our data and the way we manage it are working efficiently.

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