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Clean, Categorize, and Classify: 3 Steps to Making Financial Transaction Data More Useful

By Sandy Baker on Mar 6, 2018 2:36:18 PM

A step back in time takes you to a period when every banking transaction, account, and contract was done on paper. Every detail was managed by a person typing it in – the world of banking was long built on this process.

It worked, but was highly inefficient.

Let’s say, for simplicity, that you worked in a bank, needed a specific transaction document, and had the records on hand. You have the data, and you had a comprehensive filing system to find that data. But, we’re living in a digital world and, while you still have that data, and so much more than ever, you still need an effective way of using it. Clean transaction data makes this possible.

You Need the Very Best Data

Having data is good, but having data organized and readied in such a way that it allows you to stay ahead of the competition is ideal.

Yet many of today’s organizations don’t put these factors together. You may have data systems in place, but you are not transforming that raw information into usable information. The process is threefold. First, we need to cleanse that data. Then, we need to categorize it. And, finally, we need to classify it.

The process is a bit complex, but the benefits are incredibly beneficial. With clean, classified data, your organization can simply use it to make key decisions – whether that is in providing a special offer to a select group of customers or minimizing your risk behind the scenes. This type of data makes it possible to better operate your business.

Why Cleanse Transaction Data?

Chances are, you've seen raw transaction data — data that’s brought in with numerous random-looking characters and strings of nonsense. When you're using data for any purpose, such as within your own organization or for a third party application, this raw data simply is not usable.

When cleansing data, you make it more readable, more functional, and better in terms of being interpreted into something usable. For instance, you turn "XX56-VS-LMART-98fTXXX" into "Walmart."

Clean data is the first step to making transaction data something you can work with, whether it comes to building a chatbot, offering fraud protection, promoting hyperpersonalized offers, or more.

However, cleansing alone is not enough.

Why Categorize It?

Now that it is cleaned up a bit and more readable, the next step is to categorize it. Having dozens of strings of clean data does not mean that data has any real goal yet. When you have categorized data, on the other hand, you start to put the pieces together. You correlate key pieces of data that match specific goals. This should be done automatically; it's not something users want to do by hand.

Categorizing data requires a good amount of time and analysis of your specific goals. Thousands and thousands of transaction points are great insight into a user’s banking use, but we now need to organize it by what type of transactions those are. It’s possible to categorize data in a variety of methods.

We categorize it to put it into more understood formats. But, again, we can take this further.

Why Classify It?

Finally, with this information we can begin to make key decisions. To do that, we classify the data. This makes it functional and usable. Now, it is possible to gain insight into the “why” questions. Does this data lead to any specific conclusion? Perhaps someone is working to build a home, invest in retirement, or make other decisions regarding their finances. This alerts your company to opportunities. Now you can take that data and link it to specific goals you have within your organization.

With classified data like this, it is finally able to give you the insight you need to make decisions. With it, you can be one step ahead of the competition, delivering a better product and service to your customers and clients. And, with it, you can present opportunities and provide support far before your client even realizes that he or she needs it. It empowers your business to remain one step ahead.

All of this comes from cleansing, categorizing, and classifying transaction data. Transactions happen countless times each day. Without this process, all of that is lost information for your business. With cleansed data, it gives you insight into achieving your financial goals.

MX cleanses, categorizes, and classifies transaction data with the highest accuracy rates on the market. To learn more, read an MX categorization case study, which details how a top 100 financial institution ran 5 million transactions through their system and found that MX was 30 percentage points more accurate than the closest competitor.

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Use Data + AI to Empower True Financial Strength

By Erin Caldwell on Feb 28, 2018 9:11:30 AM

When it comes to leveraging consumer data to optimize the user experience, Amazon is king. Amazon’s recommendation engine has been honed to perfection over the last decade, but recommendations are just the beginning. Amazon uses its troves of data to optimize operations across the organization from procurement to fulfillment, but where data has really made an impact has been its use to shape the development of products such as Amazon Prime, Prime Services (i.e. Music & Video), Alexa, and Echo (to name a few).

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The Biggest Thing Banks Should Be Doing: Surveys from the Latest Fintech Festival

By Jon Ogden on Feb 26, 2018 3:25:41 PM

MX recently hosted another Fintech Festival in the beautiful mountains of Sundance, Utah. These exclusive events, which are for key players in digital banking, give financial institutions the chance to learn and collaborate with some of the best minds in the industry.

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Banks Need to Use Data More Effectively. Here's How to Start.

By Sandy Baker on Feb 22, 2018 3:47:29 PM

Data is the topic that every financial institution is talking about – it seems to be everything to their future. There are many ways that data is accessed, and there are a variety of tools that are available for gathering and analyzing it.

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Is the Overseas Money Transfer Industry Going the FinTech Way?

By Jon Dela Cruz, iCompareFX on Feb 14, 2018 10:32:17 AM

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Banking consumer's expectations, Digital banking and clean data, Fintech's untapped market: Essential Banking News

By Thomas Walter on Feb 12, 2018 3:14:08 PM

Last week's news talked about banking consumers technological expectations and how clean data might mean getting more out of you digital banking technology.

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Why Digital Banking Must Be Built on Clean Data

By Sandy Baker on Feb 8, 2018 11:30:37 AM

Digital banking has become one of the most important investments for financial institutions today. From the onset, it seems simple enough to put in place the right tools, gather data, and then use that data to make decisions. Yet, like with anything else, you get what you put into it. In short, if you do not have high-quality, clean data to build a foundation from, you cannot achieve the best possible outcome. For those looking to utilize artificial intelligence, for example, to achieve the most bottom-line benefit from digital banking, it is critical to build a strong foundation of clean data.

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AFCU receives rave mobile reviews, 2018 feeling like 2006, Wells Fargo discloses gender pay gap: Essential Banking News

By Thomas Walter on Feb 5, 2018 2:36:48 PM

This week look back on the rave reviews coming from AFCU's mobile app and the scary thing about 2018.

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Rocket Mortgage meets Amazon, Coast Capital partners with MX, Banking and the digital era: Essential Banking News

By Thomas Walter on Jan 29, 2018 11:58:37 AM

This week we look into what Amazon did to get their foot in the metaphorical door of mortgage financing and the MX and Coast Capital partnership.

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How Banks Are Embracing and Benefiting from Chatbots in 2018

By Sandy Baker on Jan 24, 2018 9:08:52 AM

Financial institutions are facing increasing competition, bigger demands from customers, and the need to reduce costs all at the same time.

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