In the digital age, the emergence of disruptive technologies has changed the nature of lending. Thanks to big data, the lending process is now less about the bank and more about the customer.

Often, more innovation arises in tough times. The new era of data-driven lending grew rapidly in the wake of the 2008 economic crisis.

While banks struggled with layoffs and had little money and resources to enhance their services, small startups had entered the market using the major technology innovations of the time. And this is how fintech was born, through the combination of financial and technology services, to the point where lending was directly impacted by this disruptive technology.

1. The business of lending

 Before

For years, banks have been the institution of choice for lending. In terms of the lending process, traditional banks and management agencies went hand in hand. Customers who wanted to get a loan first had to go to the bank to discuss available credit options with bank employees, fill out an application form, spend days submitting all required documents, and then wait for approval.

This last process can take several weeks, as the verification process is largely handled manually. It is not uncommon for customers to receive a rejection letter a month after applying and have to continue repeating the process.

 After 

Once banks and independent lenders began to harness the power of big data, the biggest change was that borrowers no longer had to make personal contact with a bank representative to access services. They could use tools like LoanStar on the web to compare loan options, and then they could log on to the lender's website and fill out an application.

As for the approval process, it's been reduced to a lower limit. With big data, lenders can calculate risk faster and get approved within 24 hours, even without written documentation.

2. Lending options

 Before

Customer-facing services have not been the strength of traditional banks, which have traditionally been known for offering standardized packages designed for specific averages. Ten years ago access to loans involved little flexibility, as there was only one available for each type of loan. Customers did not have multiple options and could not modify loan terms to suit their specific preferences. Usually Companies took precedence, followed by individual customers.

 After 

Not understanding customer needs is one of the reasons why banks have been so rigid in their business for years, and all because they had no way to collect and analyze customer preferences. Now, as part of the trend toward private banking, banks and lenders in general can offer financing solutions tailored to the needs of their customers. In addition, lenders are now using big data for pricing automation, targeting and risk assessment to provide personalized loans that are beneficial to both parties.

3. Risk Assessment

 Before

All loans involve some risk, but calculating that risk before big data was a tricky process. Because customer information was not as widely available as it is today, the risk assessment process relied on only one major factor: credit scores. However, credit scores did not paint the full picture. Strict acceptance criteria made it nearly impossible to accept applicants in certain categories because they had poor or no credit scores at all.

 after

Big Data simplifies the risk assessment process by taking into account other variables to determine financial responsibility, which include behavioral triggers and spending patterns. The result is that a young entrepreneur with an innovative business idea can obtain a business loan, even though a traditional credit scoring system would reject it. Or, a recent graduate can obtain a mortgage to move into a new apartment. Modern risk assessment tools are more accurate and comprehensive than ever before, to the benefit of both lenders and borrowers.