Data analytics is rapidly transforming industries across the entire world’s economy. Nowadays, it is rare for companies of any significant size to not use analytics in some way, and banks are leading the charge in many ways. In fact, according to Mckinsey, the leading management consultancy firm, more than 90% of the world’s top 50 banks are already using advanced analytics.
In this article, we’ll look at how and why analytical technology is being used to revolutionize the banking industry.
In recent years, analytics has gone from something only really pursued by large corporations and governments to something used by businesses of all sizes and industries. This is largely due to it becoming more affordable. As computers have become more powerful, affordable, and more accessible, analytics has followed.
Various industries utilize analytics, and banking is no exception. Within the banking industry, a new type of bank is even emerging as a result of technology, partly driven by the power of analytics - the so-called ‘neobanks’.
What Are Neobanks?
The term neobank describes a banking company that operates entirely digitally. Neobanks have no brick and mortar locations, relying instead solely on websites and mobile apps to serve their customers. Examples include Chime and Simple in the US, Monzo and Starling in the UK, and N26 in Europe, among others.
These forms of banks have only risen to prominence over the last few years, but are now used by millions of people worldwide and are growing rapidly in popularity. This is partly due to the explosion in the accessibility of smartphones, but also the availability of banking licenses. In the UK, for instance, more than a dozen new licenses have been granted since 2005, significantly shaking up the industry status quo and paving the way for competitive innovation.
Neobanks are known for their willingness to adopt cutting-edge technologies such as artificial intelligence (AI), machine learning, and big data analytics to help with their operations, and are seen by many to represent a new era of banking.
How do Banks Use Analytics?
Compared to other industries , banking is one of the most prolific adopters of analytics. Since banks have a huge customer base and are facing stiff competition, they are leveraging analytics to gain a competitive edge.
Banking was one of the first industries to adopt analytics, with it initially being used to assess credit scores. The use of analytics technologies has been turbocharged in recent years, and now covers many important elements of the industry such as:
- Risk analysis
- Measurement of the profitability of specific customers and products
- Finding high-value target customers
- Fraud prediction and prevention
- Market research
- Fine-tuning specific products and marketing efforts
Detecting fraud is a crucial part of banking. The stakes are incredibly high, with huge sums of money and people’s personal savings at risk. To add to this, there are many regulations surrounding fraud protection in banking. For these reasons, banks invest a significant proportion of their resources into predicting, detecting, and preventing fraud.
Mitigating fraud is a complicated task and banks have to deal with threats from their customers, organized crime, and even banking staff. The large scale of many banks also adds to the difficulty, creating a daunting number of potential threats.
This is precisely where analytics is useful, helping to cut through the complexity and remove the reliance on human analyses. Using analytics, banks can sort through a huge amount of data, find patterns, and identify outlying events that may require investigation.
Furthermore, the field of predictive analytics can help banks to model future events and reduce the risk of fraud before it happens. This is one of the key areas of growth in banking analytics and, with the increasing sophistication of AI and computing power, it is changing the way banks approach fraud.
Like any business, banks wouldn’t exist without their customers. It’s crucial for banks to understand their customer base, and modern analytics can help greatly with this.
Most banks now use data analytics to study customer preferences . For example, uptakes and responses to certain changes in fees can be analyzed across entire customer bases with minimal effort. This kind of automated and resource-light insight helps banks to perfect their services and engage more customers.
The neobanks are particularly strong in this regard, aided by the fact that all customer activity is digital and trackable. These banks are able to minutely analyze almost everything customers do, helping them target services incredibly effectively.
Banking is all about understanding risk and managing it effectively. Investment success is made or lost by gauging levels of risk accurately, so banks rely heavily on being able to do this reliably.
By looking at past and current data, banks can use advanced algorithms to predict future risk in specific contexts. For example, they can assess a customer’s credit risk more accurately than via more traditional methods, and expend fewer resources.
This type of forecasting using AI and machine learning is only set to increase and become more accurate as the technologies behind it progress further.
What Does the Future Hold?
All of these applications of data analytics are only set to increase in the future. Forecasting of risk, prevention of fraud, understanding of customers, and general improvements to efficiency are all critical to the future of banking.
The emerging neobanks pose an exciting challenge to traditional banking and, powered by advanced analytics, are likely to drive innovation in the industry to new heights. It’s not hard to imagine a near future where hyper-targeted services are delivered to us in real time, and it seems as though many of the neobanks are aiming for something resembling this vision.
Do you want to harness the power of analytics for your business?
At Lynx Analytics, we specialize in developing and applying technology solutions for businesses. Our services cover all aspects of analytics, including data engineering, AI, machine learning, and more.
If you’re interested in finding out how we could transform the way your business gathers and uses its data, go on and schedule a call back.