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Get the most out of raw data by choosing the best attributes

Without the right attributes in place, businesses will lose access to vital intelligence behind the ever-expanding data landscape.

“The information consumers generate will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025 across the globe,” predicts market intelligence firm IDC. Along with an average growth of 27% per year from 2018 to 2025, IDC estimates that by 2025, “every connected person in the world on average will have a digital engagement over 4,900 times per day – that’s about one digital interaction every eighteen seconds.”

With this data explosion, businesses have access to more data than they’ve ever had before. Once companies have tapped into the right mix of data sources, they still need an efficient testing and production process. This is where better attributes come into play. They help deploy analytics faster to deliver more customer-focused products and services.  

Attributes are the building blocks of models and strategies that help improve predictiveness and performance. “Think of attributes as the representation of data that businesses feed into a model or decision strategy,” says Marcus Bruhn, General Manager, Data Science at Equifax. “When dealing with attributes in data, companies need to be able to access this information quickly and use them in production to build the decision strategies they need.”

Building attributes on transaction data

Transaction data is a vital tool for organisations wanting to take advantage of Open Banking and adapt to stricter Responsible Lending requirements. Finding value within transaction data is a highly complicated undertaking. Knowing how to pick the best attributes to help make sense of this raw data is crucial for financial institutions looking to enhance their existing decisioning models and predictive power.

“Despite the huge volumes that come with raw transaction data, we have learned that with a few smart, innovative techniques we can provide detailed and business-ready insights and scores”, says Marcus Bruhn.

“Using our data analytics and insight platform, Ignite®, we’ve been able to turn large volumes of raw data into clear, relevant insights within weeks.” 

Marcus explains that the platform has the processing power to load and analyse a complete set of data, rather than only using a sample. This improves the predictiveness of models because they are built and refined using full information. The extremely high processing power of the platform enables data analysis to occur at high-speed. In some cases, turnaround can be as short as a few minutes.

With the right attributes, bank transaction data can reveal more than expected, and newly discovered indicators can create significant uplift to existing models.

Explaining the attributes used in model development

The ability to predict risk accurately, with explainability and speed, is an essential competitive advantage in today’s Open Banking environment.

There’s an increasing obligation for businesses to be able to understand how their models work and to be able to justify the results. To do so, they must be able to explain the attributes used in model development, especially as more attributes are added with each new model.

“Using NeuroDecision® Technology, we’re able to analyse the nuanced interrelationships between thousands of transactional attributes”, says Marcus. “By applying constraints to a neural network, our machine learning tool can understand the impact each attribute has on the model.”

Importantly, understanding the role of each attribute enables businesses to defend their analysis because they’re able to comprehend what’s going on behind the scenes. For financial institutions, this means the ability to give consumer-specific explanations for the key factors that impact credit lending decisions.

Find out how to push models and scores to market quickly with Equifax Ignite®.

Book a demo to see how this ‘do-it-yourself’ solution can extend and enhance the data and analytical tools available to your in-house analytics teams.

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