Business Intelligence For Banking – By Lab Consulting Banking Banking Transformation Business Intelligence in Banking Business Intelligence Services Credit Union April 13, 2018
Our previous blog article looked at the challenges of using business intelligence, or BI, in insurance, and its impact on claims processing. In this article, we will focus our lens on business intelligence in banks.
Business Intelligence For Banking
Business intelligence in banking is defined as the use of analytics software, or SAAS (software as a service), to create data visualizations that are interactive and can be done at the desktop level by end users of banks and companies of financial services. Commonly used business intelligence software in banking includes: Microsoft Power BI, Tableau, Tibco Spotfire, and Domo. Banking business intelligence applications can be hosted in the cloud and configured to run on private servers dedicated to financial services companies with strict data security requirements.
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It’s no surprise that Microsoft Power BI tops the list of best business intelligence in banking software. This is our in-house option at The Lab.
We started our business intelligence journey in banking with Tableau 5 years ago and found that the learning curve was steep, the price was high, and the market was not mature enough for competitors to enter with different’ other features and functions. But, as time went on and new releases of competing software became available from the king of software companies (Microsoft), we moved away from Tableau and focused on Power BI as our choice. We’ve tried many lesser-known and lesser-known BI companies, but they can’t keep up with banking data and will face an uphill battle as users converge to become the largest business intelligence platform for banking.
Business intelligence gives banks the flexibility needed to thrive in both business-as-usual and turbulent economic times. Globally, BI processes and software give banks a deeper understanding of their business, their customers, and their future. It can also open the door to success by shining a light on areas ripe for cost cutting, new business opportunities, and more.
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How business intelligence works in banking, finance, and credit unions – detailed business intelligence in banking research
Business intelligence implementations in banking enable users to connect multiple and disparate sets of systems to display interactive data visualization dashboards that typically cannot communicate across platforms. In the banking business intelligence case study, we will follow a consumer and retail banking manager who manages multiple product lines. Home loans, mortgages, car loans, and credit cards are under the supervision of our executive and they must report each other’s monthly results on an ongoing basis.
Now, imagine that all that data is in completely separate IT systems and has to be retrieved every time it needs to be analyzed. Compiling that bank data is a huge task and takes four people two weeks to complete each month. This is the current situation of many banks trying to find business intelligence in banking.
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Imagine being able to put a software layer on top of all those disparate core banking systems and a database that connects them all to enable “live” reporting of all the data at once. Folks, that’s how the banking business works. While that seems like an easy fix all the time, a lot of work needs to be done to measure the underlying data before it becomes useful.
As you can see, financial services is a ripe target for BI transformation. There are insights to be discovered, efficiencies to be discovered… so why are so many banks struggling to make a profit on their investment in business intelligence? And more importantly, how can banks move on from the current barriers to success in BI in banking?
At The Lab Consulting, we like to say that we can help you “achieve high-quality business intelligence analytics, using low-quality data.” What does this mean for business intelligence and banking performance?
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Well, when we go to a bank to share business intelligence—and we’ve done this for dozens of leading banks, from regional to multinational—we always find that they have a lot of data on hand. This seems to be all that is needed, right? After all, a leading banking business intelligence solution like Microsoft Power BI or Oracle Business Intelligence can draw from unlimited resources, and let you choose the filters you want to see amazing revelations of business intelligence that empowers decisions to strengthen the business. . Plug it in, and you’ll have your “aha moment.” OK?
Not really. As we said, we can see banks boasting many financial indicators: be it commercial, loaned, invested, or financial. But the devil is in the details: the “bottom-quartile data” we mentioned above. Banks, in general, do not have the ability to go into their banking operations and see “who or what is performing at the appropriate level, compared to expectations.” Of course, they will be able to calculate things after the fact. They will produce comprehensive forecasts and some transferable reports, but they will not be able to see financial information down to the function level of location, branch, or individual information.
That’s the kind of information that is essential to business intelligence in banking. This is the kind of BI input that is needed, and we help make it available: it matters
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Like how many sales per person? How many sails are there in each region? Total expenditure compared to per capita income. The total cost of making a loan compared to the profit and spread of each individual. You get the idea.
These are some of the key banking KPIs that are needed when talking about BI in banking, and what will ultimately achieve the full benefits of business intelligence in banking.
We will see banks, say, commercial divisions that exceed their stated revenue goals. Looks good, right? But if you break it down by region, and dig into each bank’s operations, you’ll see a completely different story. In four states, for example, you’ll find the ball hitter in the park (and, of course, make everyone else look better than them, once they’re all rated). The next one may be a little earlier. And the third and fourth states may be lagging—by a lot. Surprisingly, most banks fail to access this level of detail; usually we take the lead and show them how bright it is. It is the core of a successful banking business and the key to understanding and improving banking performance.
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Often, banks will tell us, “We can do the business intelligence ourselves!” Which begs the question: So why not them? Why don’t they create for each region, put each producer’s data inside each other, and create BI on bank reports? However, it often turns out that individual employee data resides in Excel spreadsheets…. maintained by the same exact staff! They keep their statistics on their production. So, we have to go in and get it out… which is often easier said than done, especially for lesser players.
Small players – whether they are regions, branches, or individuals – can reduce the effectiveness of a large organization. And this drag can be hard to see, even hidden, if a bank only uses “measurements” to feed its BI system. As we say, “Achieve high-quality business intelligence analytics, using low-quality data.” Otherwise, you won’t really see the value of business intelligence in banking.
Here’s a dirty little secret: When we ask a bank to give us their data to use business intelligence to build dashboards, we
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And then when you see “it,” “it” isn’t what you expected. And more often than not, it doesn’t work like it does with banking BI.
Usually, we will be able to dispute the data. We find a database where a worker is listed as “John Smith.” Another lists the same worker as “J. Smith,” and the third calls him “Smith, John.” One may recognize the same employee. The details, unfortunately, are not. Therefore, the databases need to be cleaned to be useful for BI in banking.
It gets worse. When a bank simply outsources the creation of its most important information to its IT team, with insufficient administrative input, you end up with data with ambiguous tags such as “47ABG9.” that’s confusing. But what if “47ABG9” is the title of a
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Of data? This means that the entire column is not available. And the longer the column, the greater the loss in a business intelligence project.
Defined as an analytical display tool connected to multiple sets of bank data across multiple systems, a banking dashboard is an integral part of business intelligence in banks. If you have (enriched, standardized) data, it can track and display anything from business process results to financial performance to key performance indicators, as in our example below.
Key performance indicators, or KPIs, are specific quantitative measures of your business’s effectiveness. Visualization allows you to monitor your organization’s performance both currently and historically. It may also allow you to use predictive analytics to predict your future performance, to some extent.
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