Have summaries of our latest blogs delivered to your inbox, so you can stay up to date on the topics and current events that matter to your business.
Smarter Research, Stronger Returns: Why Financial Services Leaders Are Rethinking Their Approach to Risk and Due Diligence Whether you’re vetting a potential acquisition, assessing third-party...
As a consulting partner , you're used to being ahead of the curve. You’ve built your practice and your reputation on your ability to spot shifts early and act decisively. But the nature of...
In consulting, firms aren’t just adopting genAI, they’re using it to change what productivity means. The report, Setting the Pace: How Management Consultants Are Leading the GenAI Revolution...
Innovation in investment banking has traditionally focused on speed, scale, and quantitative precision. But as generative AI (genAI) gains traction, a new focus is entering the equation: creativity. Creativity...
For professional services firms like management consultants, market researchers, and IT services, performing quality research is essential but time-consuming. Fortunately, new generative AI tools can automate...
* The views expressed in externally authored materials linked or published on this site do not necessarily reflect the views of LexisNexis Legal & Professional.
In today’s world, big data allows banks to reach new levels of innovation. Applying big data analytics to high-quality datasets guarantees the value and relevance of products clients are searching for. Nevertheless, numerous banks have yet to take full advantage of the potential offered by big data technologies such as Artificial Intelligence (AI) and Machine Learning (ML). Not seizing the opportunity of AI-enhanced innovations such as ongoing monitoring technologies can substantially damage a company’s financial performance and can even lead to reputational, regulatory, and strategic risks.
Like many industries in the global economy, the banking sector has been subject to sweeping changes to its business model in the past decades. Whereas customer relations used to happen directly at a branch, customer contact has increasingly moved online. This has not only changed the data and information banks have access to but also the experience of customers themselves, who are now often able to profit from a bank’s services all around the world and 24/7.
With the rise of this new and digital banking industry, data science has already shown its true value. Through big data technologies, banks have seized the opportunity to learn from their customers’ behavior and fully embrace the potential benefits of AI-enhanced technologies. Dutch multinational Rabobank, for example, started to embrace a data-driven approach in 2011. This has already led to more than 100 AI initiatives being successfully completed in fields such as customer experience and risk management.
Today, most banks have at least started to understand the potential benefit big data can bring to their business. As AI-enhanced technologies steadily develop, the use cases for big data application in the banking sector are growing by the day. For some use cases, big data has already proven to be indispensable for banks doing business today. These include:
The bottom line: While big data is finally gaining some traction in the banking industry, investing in these new technologies will only deliver value if organizations also have access to relevant datasets from both internal and external sources. With a compelling and growing field of use cases, AI is here to stay. Bearing the continuous rise of FinTech in mind, companies that fail to embrace AI-enhanced technologies today might find themselves at the losing end of their industry in a few years’ time. The only question that remains is: Is your company getting the most out of big data?