Business Intelligence Banking – By The Lab Consulting Banking Banking Transformation Business Intelligence in Banking Business Intelligence Services Credit Union April 13, 2018
A previous blog post examined the challenges of implementing business intelligence (BI) in insurance and its impact on claims handling. This article will focus on business intelligence in banks.
Business Intelligence Banking
Business intelligence in banking is defined as the use of analytics software or software as a service (SAAS) to create interactive data visualizations that end users in banks and financial services can create at the desktop level. Commonly used banking business intelligence software includes Microsoft Power BI, Tableau, Tibco Spotfire, and Domo. Banking BI applications can be hosted in the cloud and configured to run private dedicated servers for financial services companies with stringent data security requirements.
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We’re not surprised that Microsoft Power BI tops our list of best business intelligence solutions in banking software. The internal choice of the Lab.
We initially started business intelligence in our banking adventures with Tableau five years ago and found that the learning curve was steep, the price was very high, and the market was not mature enough for competitors come in with a range of features. However, over time and new versions of competing software from the king of software vendors (Microsoft) left Tableau and chose Power BI. We’ve tested lesser-known, more specialized BI companies, but couldn’t keep pace with banking data and will face an uphill battle as users consolidate into larger banking BI platforms General public.
Business intelligence gives banks the adaptability they need to excel in normal and more turbulent economic conditions. Globally, BI processes and software are enabling banks to gain a better understanding of their business, their customers and their future. Additionally, it can open the door to efficiency by revealing areas where cost reduction is possible and new business opportunities.
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Banking BI implementations allow users to link together sets of disparate systems to display interactive data visualization dashboards that are not normally able to communicate across platforms. This banking business intelligence case study follows a retail and consumer banking executive responsible for multiple product lines. Mortgages, home equity loans, auto loans and credit cards are all under the supervision of our management and are required to report monthly results on an ongoing basis.
Now imagine that all the data about them is in a completely different backbone computer system and needs to be pulled every time analysis is required. Integrating this banking data is a monumental task and takes four people two weeks each month. This is the current state of affairs for most banks trying to extract economic intelligence from their banking operations.
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Imagine being able to install a software layer on top of all the individual central banking systems and the databases that connect them, so that all data can be reported “live” at the same time. Ladies and gentlemen, this is how banking economic intelligence works. While this may seem like the easiest solution ever, a lot of work needs to be done to normalize the underlying data before it can behave in a useful way.
As you can see, financial services are a ripe target for BI innovation. You have data to examine and you need to uncover efficiencies. So why are so many banks struggling to get a return on their business intelligence investments? And more importantly, how can banks move past the current barriers to BI success in banking?
At The Lab Consulting, we like to say that we can help you “achieve top quartile business intelligence analysis using bottom quartile data quality.” What does this mean for business intelligence and banking?
Implementing Business Intelligence In Banking
Well, if we’ve done this for dozens of big banks, from local banks to multinational banks, you’ll find that we always have a lot of data when we go to the bank for a business intelligence contract. Sounds like that’s all you need, right? After all, leading banking business intelligence solutions such as Microsoft Power BI or Oracle Business Intelligence tap into a virtually limitless supply of sources, then select the filters you want to see amazing business intelligence revelations to support your business improvement decisions. . You plug it in and you get that “aha moment”. right?
Exactly. As we said, you’ll see banks touting many financial metrics, whether it’s sales, loans, deposits, or financial information. But the devil is in the details. These are the “lower quartile data” mentioned above. Banks in general lack the ability to take a closer look at banking operations and see “who or what is performing at an appropriate level relative to expectations”. Of course, they will be able to count after this has happened. It produces decent aggregate forecasts and reports, but it doesn’t let you view financial information at the regional, branch, or individual knowledge worker level.
This is the important information in banking economic intelligence. This is the type of BI input you need and helps you use:
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For example, what is the sales volume per person? Sales by region? Overall cost versus individual salary. The total cost of creating the loan versus individual yields and spreads. you got the idea.
Here are some of the essential banking KPIs you need to talk about BI in banking, which will ultimately help you to take full advantage of business intelligence in banking.
For example, you will see banks whose operating segments exceed stated profit targets. cool, right? But if you unbundle by region and dig into individual banking, you’ll see an entirely different story. For example, with 4 regions, you can see which regions are hitting the ball out of the park (of course, when all regions are averaged, it makes them better than the others). The following may be slightly ahead. And the third and fourth areas could be far behind. Surprisingly, most banks cannot access this level of detail. We usually take the initiative and show how much it reveals. It is the key to successful banking business intelligence and the key to understanding and improving banking operations.
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Often, banks will tell us: “We can do economic intelligence ourselves! So the question arises. So why not ? Why not unzip each region, drill down into individual grower data from each region, and generate BI from the banking reports? Well, individual employee data is often found in Excel spreadsheets…Maintained by the exact same individual staff! They keep their own statistics on their own production. Then you have to enter and search. This is often easier said than done, especially for underachievers.
Whether local, branch or individual, underperformers can hurt the performance of large organizations. If a bank only uses “average” for its BI system, this streak is hard to spot and may even hide. “Use bottom quartile data quality to drive top quartile business intelligence analytics.” Otherwise, banks will never really see the importance of business intelligence.
Here’s a dirty little secret. Ask your bank to transmit data so that they can create a dashboard using business intelligence.
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Then when you find “it”, “it” is not what you expected. And too often in banking, BI just isn’t viable.
Often we can exchange data. You can find the database where the employee is listed as “John Smith”. Another described the same employee as “J. Smith”, and the third calls him “Smith, John”. Humans can recognize identical employees. Unfortunately, databases cannot. Therefore, the bank must clean up the database to make it useful for BI.
It’s getting worse. When banks hand over critical databases entirely to their IT teams without sufficient administrative intervention, they see data tagged with incomprehensible tags such as “47ABG9”. That in itself is confusing. But if “47ABG9” is
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The data? This means that the whole column is not available. And the higher the pillar, the greater the loss for business intelligence projects.
Defined as an analytical display tool linked to multiple sets of banking data from multiple systems, a banking dashboard is an integral part of banking business intelligence. When you have (enriched and standardized) data to process, you can track and view everything from business process results to financial performance to key performance indicators, as in the example below.
Key Performance Indicators (KPIs) are specific quantitative measures of business efficiency. Being able to visualize this allows you to track the current and historical performance of your organization. Additionally, predictive analytics can be used to visualize future performance to some extent.
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Business intelligence in the banking sector
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