How business intelligence in banking is shifting the paradigm

Banking has always been a competitive environment, even before the digitization of the industry acquired its present pace. Thanks to financial technology, the competition has become even tougher. Fintech companies to banks are what Uber is to taxis. And, as we know, taxi drivers aren’t happy about Uber.

Apart from having their profits endangered by fintech companies, banks also experience extreme pressure from regulators. After the 2008 crisis, regulatory agencies, such as FRB, OCC and FDIC, are carefully watching banks. And while most of the banks didn’t participate in activities that led to the crisis, all of them have to follow strict compliance rules adopted after the market crash.

Competitive business intelligence solutions for banking have to reflect all these requirements. They have to be flexible and transparent to adapt to competition and regulatory environment. They have to be scalable to keep up with the growing digitization of the industry, as more clients are starting to forget the last time they visited the bank physically. They have to be “smart” and drive better financial and operational decisions. Just having the data spread out across multiple graphs and pie charts is no longer a viable business intelligence approach.

It’s important to understand that there’s no single perfect BI solution for every aspect of banking. It’s impossible to have a system that will cover the whole pipeline, from data storage to data interpretation. Sure, some systems are trying to cover most of these aspects, but none has succeeded yet.

That’s why one of the biggest trends in banking BI is the multi-dimensionality of the toolkit used by business analysts in banks. Given that there are so many tools and so many ways data can be accessed and processed, banks are pushed to find flexible solutions that will make these systems work together and complement each other.

AI and specifically machine learning are taking the banking industry by storm. These technologies transform the meaning of ‘intelligence’ in ‘business intelligence,’ and there are many reasons for that.

AI also becomes more accessible and integrable with common technological standards, like Postgres. So, while banks do risk introducing a new tool to their toolkit, the technical issues with this introduction are often minimal.

Then, there’s the cost/benefit analysis. According to Goldman Sachs, AI will deliver up to $43 billion in savings and revenue opportunities in the financial sector by 2025. Not many banks can pass up on that much money. That’s why all of the major U.S. banks are investing hundreds of millions of dollars in AI/machine learning.

This trend stems out of the previous two. You can’t build a stable BI infrastructure without data professionals. Within the past couple of years, the demand for data engineers has doubled. The situation is even more promising for data analysts and data scientists looking to start their career in banking, as more banks are exploring advanced algorithmic trading systems and other tools that can help them gauge their investment risks. These professionals make sure that data within the bank is stored correctly, accessible widely, and analyzed thoroughly.

The growing demand for people proficient with data also redefines the management requirements for such specialists. It’s not uncommon for banks to have a Chief Data Officer, who’s responsible for data, analytics and business intelligence agendas of the bank.

Banking is one of the most complex industries in terms of the available data and the need for it to be processed. At the same time, many banks don’t have enough time or capacity to tailor various BI tools to their specific needs.