Open banking data cleaning - Nordigen

What is data cleaning, and why does it matter in open banking

| Article by: Abilio RodriguesProfile Image Abilio Rodrigues 6 min

Data cleaning is a procedure that aims to remove imprecise, duplicate or plain incorrect information from a dataset. This can be achieved through a plethora of strategies, with the intent of improving the quality of the data to facilitate decision-making processes that rely on that data.

We are definitely in the era of big data, and it’s easy to observe the importance information has amassed in our daily lives. Data is now more readily accessible than ever, arriving in greater variety and volume. 

With this in mind, we could say more data is synonymous with better data, right? Well, that’s not usually the case. Although ideally FinTechs are more than capable of putting data to good use, it’s not just the quantity that matters. Quality of information plays the most relevant role, as it allows a greater deal of control and consistency. 

In big data, the term “veracity” refers to the quality of the data. Information now comes from so many sources, making it very hard to categorise and link. In order to come up with proper innovative products and services, businesses need to find correlations between multiple data streams. 

But if all of this is not so much about how much data you have but how you use it, how can FinTechs make sense of all the information they collect? Well, enter data cleansing, or data cleaning. 

In the next few lines, we will try to demonstrate howopen banking and data cleaningare tightly related and how one can help the other to grow and bring the most benefits to customers, businesses, and legacy institutions alike. 

What is data cleaning?

The basics of data cleaning are actually quite simple to understand. Imagine you are presented with a complex database composed of unstructured, duplicate or even erroneous information. There is really not much you can do with that, at least not before you apply some sort of action that makes it consistent, improving the quality of your data — either by eliminating superfluous information or by completing the dataset by filling in the gaps. 

Since big data relies on many different sources to create datasets, you may also need to aggregate the information to have a logical framework to use as a starting point. Data wrangling will also remove errors from datasets, helping to make data more understandable and easy to analyse.

Data cleaning consists of different approaches, both manual and automated, that tend to vary from team to team. 

Is data cleaning important?

Whatever strategy you use, there is only one bottom line: to improve data quality, in order to provide more consistent and reliable information that will serve as a baseline for decision-making. Uniformity makes data more actionable, helping people and businesses to make better decisions. 

So, in short, yes, data cleaning is paramount to the success of products and services in today’s financial markets, as well as for marketing teams, sales reps or operational workers. 

Taking the necessary steps to cleanse databases can even have a dramatic impact on organisational costs, reducing ill-considered strategies and operational setbacks. Structured data offers the most valuable information that can then be used to add value to a business or product. 

Data-driven businesses should take data cleaning as a foundational step to improve their offerings, with data management taking the front seat when it comes to being one step ahead of the competition — you tell me but, for sure it’s a worthy investment.

Here’s how the data cleaning process works:

  • Inspection and profiling — Gathering the data comes first. There is an initial assessment of the quality and picks out ​​errors, discrepancies and other problems. 
  • Cleaning — This is the meat and potatoes of the entire process. This is where duplicates are erased, irrelevant information is disregarded, and valuable data is grouped together. 
  • Verification — After the cleaning process is completed, what follows is verification of the process that the outcome is indeed what you’re looking for and falls under the right rules and regulations. 
  • Reporting — Reporting refers to the process of visualising the findings, giving you insights into what the data were initially and what the result was after the cleaning was done. 

Data cleaning in open banking: you can’t have one without the other

Open banking is probably the main technology behind an extraordinary boost in innovation in the banking and payments markets. One of its main goals is to enable better financial decisions and increase financial literacy.

In order to do this, open banking heavily relies on the financial data it gathers from banks and other legacy institutions. Data science is capable of adding value through the identification of patterns in datasets. Here are some practical examples of how open banking leverages data science:

  • Enables applications that offer valuable insights about spendings and income;
  • Enables faster access to credit by facilitating risk assessment and affordability estimations;
  • Enables advanced KYC (Know Your Customer) and identity verification, boosting security;

Multiple streams of data require an extended effort to identify patterns and inconsistencies through millions and millions of strings of information. Artificial Intelligence and Machine Learning, for example, are some of the tools that businesses can use to promote creative ways of adding value to open banking data.

A careful thought-out approach to data cleaning can serve as a starting point to build personalised financial products, one of the core values of open banking. We live in a “smart” era, where products and services are more desirable if they offer a tailored approach to the specific needs of individual customers

Moreover, data ownership and control shifted greatly with open banking, giving back customers the power to choose what data to share and with whom. So there should be no doubts about the capital importance of structured data to open banking, as it’s an invaluable tool to help improve people’s lives. 

How Nordigen is using open banking to clean transaction data

Clean transaction data can have multiple use cases, and Nordigen can help you improve your business offerings through its open banking solutions. 

You can find below some examples of how clean transaction data can be helpful in many scenarios:

  • Lending: structured data insights for credit and lending decisions, by identifying income and active liabilities with greater accuracy – reducing credit risk;
  • BNPL: streamline customer approval processes, reducing friction in customer risk verification;
  • Personal Finance: identifying more earnings and spending patterns with greater accuracy, helping build a user experience that promotes new habits;
  • Banking: building better customer engagement tools, identifying frequent purchases and subscriptions;

At Nordigen, we recognise the main purpose of each transaction, based on its description, amount, date and contextual metadata. Nordigen Premium is:

  • Better: transaction information and data points that are organised more clearly, with both transaction purpose and business category specified; 
  • Faster: Nordigen Premium is 10x faster than the initial version;
  • Cleaner: information is cleaned to provide quality data that is simple to understand;

To find out more about how Nordigen Premium can help you get your business to the next level, visit our website and contact our sales team!

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