by Darya Shmat

How business intelligence in banking is shifting the paradigm

Mar 08, 2018
AnalyticsBusiness IntelligenceFinancial Services Industry

Business intelligence in banking is a complex and robust pipeline that incorporates a variety of tools. Find out what goes into a BI solution for a bank.

FinTech - Financial Technology - dollar sign, circuits, data
Credit: Thinkstock

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.

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.

Robust toolkit

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.

Artificial intelligence

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.

Data people in the spotlight

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. There are also specialized banks that deal with particular types of financial services, which requires a dedicated approach to the business intelligence logic.

That’s why many business intelligence tools appeared that cater to specific industry niches within banking. And the likelihood for a bank to find something that suits their particular needs is very high. For example, there’s Motivity that specializes in BI for mortgage banking. Beye specializes in banking analytics, with the sole focus on banks and financial institutions. Tools like BankBI come pre-packaged with specific features relevant to banks.

Banks can see the results that specialized BI tools deliver to fintech companies. Take an example of TradeStop’s BI system for stock behavior tracking. The system is fine-tuned to process, analyze and visualize a particular kind of data relevant to fintechs, which makes the solution much more efficient than available general tools.

Internet of Things

IoT technologies are usually associated with manufacturing and retail industries, due to how IoT penetrates them. Of course, that’s because these industries are also more likely to benefit from the IoT.

Financial institutions are heavily investing in IoT to drive smarter business intelligence capabilities and improve services for their customers. For example, they can offer contactless payment options through wearable devices. Your standard contactless credit card that can be used at most of the payment terminals is another example of the prevalence of IoT technologies in banking. But it gets even better, as there are now cards that allow two-way communication with the bank directly through an interface on the card itself.

All of the data generated by this and other similar devices can be analyzed by the bank’s BI team to drive even more revenue. For example, the banks could learn more about the locations that their customers prefer and offer additional incentives or discounts for these specific locations, driving better engagement within their loyalty programs. The possibilities are endless.


This is a major talking point around banking in general. A lot of the tools that banks use essentially become repositories for personal information.

The recent hack of Equifax that exposed data about millions of banking customers is yet another proof that banks have to consider fraud prevention and cybersecurity as main features of any BI platform. So, to fit the security requirements, a BI solution should have advanced features, such as automated threat detection.

Equilibrium for banking

So how should a bank build a business intelligence pipeline, keeping all of these trends, technologies, and requirements in mind? Banking executives need to understand all of the components that go into a fully functioning BI pipeline.

Data sources

How information is stored is essential because it identifies the systems that can access the data. The source also defines the types of transformations that could be performed with the data. As you can imagine, a NoSQL database is much more flexible than an Excel sheet.

Managing data

These are the systems that combine data from various data sources. Their job is to de-silo the data and merge it into data lakes.

Preparing data

There’s a lot of modeling involved in financial operations. Be that a statistical model for credit risk, or an algorithmic system for trading – all of the processes and technologies that enable modeling work with adequately structured data. These tools often substitute data management tools or work in conjunction with them.

Building models

Apart from building statistical models and engaging in advanced quantitative analytics, many banks now realize the power of machine learning. Many of the tools used to develop models or generate financial predictions require advanced technological capabilities, like the availability of various machine learning libraries or automation features that help feed models with data from data management and data preparation tools.

Managing models

If you’ve built yourself a model that addresses your business intelligence needs, it then has to be stored, updated, and operationalized by connecting it directly to your customer-facing systems. This also takes a specialized kind of system.

Consuming business intelligence

It doesn’t matter if you build a system that generates predictions or you don’t – your employees still need an interface to work with data. Not everyone knows how to query a database. Not everyone knows how to visualize different kinds of data independently. That’s why there are tools that let your bank’s employees alleviate all of these issues.

This list is not exhaustive. Especially for banking, where there are, as we mentioned, specialized tools that might be more suitable. However, it’s important to remember that using them for business intelligence in banking might be a dead-end, as specialized systems often don’t have a robust API and the backend flexibility to support a growing banking operation or emerging technologies.