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.
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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.
In this article, we’ll cover some of the trends and tools that transform business intelligence in banking or at least serve as the evidence of this ongoing transformation.
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. […]