An explosion of information has transformed the world around us. Big data has evolved as an industry in its own right and has had a tremendous impact on many vertical industries, including consumer lending.
As the timeless saying goes, “Money never sleeps.” And, consequently, neither does the massive amount of data that forms the basis of consumer credit performance. Data is at the heart of all lending decisions, and in 2016 the river of credit information runs deeper and wider as computing and cloud platforms have managed to gather and store massive volumes of data.
With new players entering the lending marketplace and applications driving engagement, the market is dynamic and evolving. Likewise, so are consumer behaviors and preferences. Today, people are more mobile, have the luxury of expanded choices in financial institutions, and can research many more financing services and products.
Traditionally, a record of historical credit data has enabled financial institutions to better understand the market and the changing trends in consumer behavior, preferences, and payment patterns. These insights allow institutions to have higher confidence in their decision making, giving them the ability to better satisfy consumer needs both profitably and sustainably. In general, the greater depth of historical data that a lender has, the better insights it has regarding anticipated risks. These insights are key to making daily business decisions.
Today, big data has provided the financial industry with what it never had before: context. With big data, lenders are able to get a much better and more precise understanding of the most current consumer behavior. And the industry is noticing. According to a recent IDC report, worldwide revenues for big data and business analytics will grow from nearly $122 billion in 2015 to more than $187 billion in 2019, with $22.1 billion of that going toward the banking industry.
One of the biggest benefits that big data provides lenders is the ability to aggregate data, which reduces the time it takes to query large sets of data. This subset of data provides lenders with the opportunity to track consumer actions to help identify important factors, such as overall risk levels, and to mitigate losses through early intervention.
Traditionally, lenders have considered past payment behavior a key indicator to predict future likelihood to pay. However, the depth and breadth of the consumer credit file (e.g., credit history and utilization across products ) and the incorporation of alternative data sources such as utility payments, payday loan, and checking account performance have significantly increased the ability to accurately predict payment behavior.
One of the biggest data challenges that financial institutions face is plowing through the massive amount of data that already exists without having to have specialized teams in place that can extract the value in a timely fashion.
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