Embracing Big Data for Banks One Use Case at a Time

The Time is Now: Embracing Big Data for Banks One Use Case at a Time

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.

With big data, banking has arrived in the 21^st century

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.

How banks can profit from big data

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:

• Regulatory compliance: Faced with an ever-growing and fast-paced global economy, banks and other financial institutions must stay up-to-date on potential regulatory changes and new sanctions and PEPs that can affect their compliance management. AI-enhanced due diligence and ongoing monitoring technologies offer companies the opportunity to protect their business against potential regulatory and reputational pitfalls of compliance failures. These technologies derive useful insights when applied to datasets comprising Nexis news sources, legal sources, company information and more.

• Risk assessment: Today, banks perform tasks for an increasingly global client base. Paired with the 24/7 nature of the global economy, these factors often lead to a heightened exposure to third party risk, including from suppliers and customers. This is particularly true when it comes to identifying Politically Exposed Persons (PEPs) and staying up-to-date on the most recent international sanctions lists. Risks connected to both PEPs and sanctions can lead to a variety of financial, regulatory, and reputational setbacks that can seriously harm a bank’s business. Ongoing monitoring tools which analyze critical data sources on PEPs and sanctions can help banks to mitigate these risks effectively.

• Client relation management: Increasingly, customers are gaining awareness over their potential power when it comes to adapting and changing a company’s products or overall business attitude. Through personalized feedback opportunities as well as a broader approach to analyzing public sentiment, banks can improve and adapt their products accordingly. Here, sources such as social media, news, or online blogs play a significant role. Using a data-driven approach to gathering and analyzing this information can help banks and other financial institutions to better understand customer needs and make more accurate decisions when responding to public demands.

• Client analysis: With the growing access to data on clients’ incomes and expenditures, banks can make informed decisions about potential credit extensions, risk assessments and decide if the client is interested in investment opportunities. AI-enhanced technologies can support banks in analyzing these datasets and helping to draw the right conclusions.

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?

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