How Big Data Is Changing The Nature of Consumer Lending
Big data is changing the nature of consumer lending in major ways. Here's how that's making a difference in the lending process and experience.
The history of lending can be traced back to the beginning of civilization. Some of the earliest records are from 2000 BCE, in Mesopotamia. The concept of lending has changed dramatically over centuries, largely due to changes in technology, social norms and evolving financial habits. The first major breakthrough in the lending industry took place in 17th century England, where the First Central Bank (The Bank of England) was born. The second major breakthrough, although we may not realize it, is unfolding right now. It is the evolution of big data. In the digital era, loans are transformed by the emergence of disruptive technologies. Thanks to recent advances in Big Data, the lending process is now less about banks and more about the customer. They say that hard times fuel innovation, and the new era of data-driven lending was jumpstarted by the 2008 economic crisis. When banks were struggling with layoffs and had little money and resources left to enhance services, small start-ups capitalized on the major technological innovations of the time. This is how FinTech was born – the combination of financial and tech services – and loans were directly affected by this disruptive field.
The Lending Process
Big data is driving a number of changes in the financial industry. The lending business is one of those that has changed the most. Here are a few of the changes that we have seen over the years.
For many years, banks have been the go-to institutions for lending. Other options existed, of course, but their reputability has always been somewhat disputed. In terms of the lending process, traditional banks and bureaucracy went hand in hand. People who wanted to get a loan first had to go to the bank, talk to an employee about available credit options, fill in an application, spend days looking for all the required documents and then wait for approval. This last part could span across weeks, because the verification process was mostly manual. It was not uncommon to get a rejection letter one month after the application and then the client had to repeat the process.
Once banks and independent lenders started leveraging the power of Big Data, the lending process automatically became simpler, faster and more convenient for users. The biggest change is that borrowers no longer need to have personal contact with a bank representative to get service. It all starts online, where they can use tools like LoanStar to compare lending options and then they can head over to the lender’s website to fill in an application. As for the approval process, it was reduced to a minimum. Thanks to Big Data, lenders can calculate risks much faster, even without paperwork, and approval is given in as little as 24hrs.
Big data hasn’t just changed the logistics of the lending industry. It has also led to a number of new options for consumers that need capital. Here are some changes that have taken place, due to the evolution of big data.
Client-oriented service has never been a strength of traditional banks, who are historically known to deliver standardized packages designed for a certain average. Getting a loan ten years ago involved very little flexibility, because for each type of loan (mortgage, personal, automotive, business, and so on) there was only one available package. The customer could not choose from a wide range of options, nor could they modify the conditions of a loan depending on their particular preferences. The institution came first, then came the customer.
Failure to understand customer needs was one of the reasons why banks have been so rigid for so many years, and it all happened because banks didn’t have the means to collect and analyze customer preferences. This is another factor changed dramatically by Big Data. Now, as part of the private banking trend, banks and lenders in general can help their clients with customized financing solutions, tailored to their needs. Additionally, lenders now use Big Data for pricing automation, targeting and risk assessment, generating personalized loans that are beneficial to both parties.
Big data has also changed the way that risk assessments are conducted. Many lenders use predictive analytics algorithms, which have been shown to be very effective for actuarial decision-making. Deluxe has touched on this topic.
All loans involve a certain amount of risk, but calculating this risk was a tricky process before Big Data. Because customer information wasn’t as widely available as it is today, the risk assessment procedure relied on only one main factor: the credit score. However, the credit score does not paint the full picture. The strict acceptance criteria made it almost impossible for certain categories of applicants to be accepted because they had a poor credit score or no credit score at all. Millennials, for instance, are forming more responsible spending habits compared to the older generation, but because they refuse to follow the conventional path to establish a credit score, they do not make successful bank applicants.
Modern loan providers understand that a customer is more than a credit score, which is why they consider the big picture when deciding their eligibility. Big Data streamlines the risk assessment process, taking into account other variables to determine financial responsibility, including behavioral triggers and spending patterns. The result: a young entrepreneur who has an innovative business idea can get a business loan although the traditional credit score system would reject him. Or, a student who just graduated can get a mortgage loan to move into a new apartment. Modern risk assessment tools are more accurate and comprehensive than ever, to the benefit of the lender and the borrower alike.
Big Data is Changing the Nature of Consumer Lending
Big data has had a very big impact on the lending industry. Many companies are using it to get better actuarial assessments and offer more options.
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