Big data technology has had massive implications for the financial industry. Banks, credit card companies and other financial service providers are leveraging big data in unprecedented ways.
One of the biggest benefits of big data is with loan and mortgage processing. The use of data can be crucial for credit card companies and those offering loans and mortgages. The Forbes Technology Council discussed these benefits in an article last spring.
The ability to carry out checks on credit histories, income, employment and expenses can play a huge role to learn from previous customers and determine which are the best customers to pursue and which to avoid.
We speak to two finance startup founders to get a better idea of how they use data effectively to do underwriting, grow and scale their businesses.
Initial Approval
Data is vital for the initial approval of a credit card or loan. A customer’s journey typically starts with filling in a few details via an online form, often taking around 5 to 10 minutes. From this, the lender is able to determine a customer’s profile and whether they meet the initial criteria which might be having a certain minimum age (e.g 18 or 21), having a permanent residence and regular employment.
This initial data can help siphon out any applicants who will not be eligible and who are not worth pursuing.
For Underwriting and Funding
“For underwriting and deciding who is going to be approved for a loan, the use of data is everything,” explains Richard Dent of Finger Finance. This is a great example of how big data has changed the financial industry.
“We are always looking at data, including new customers that come in and fundamentally historical data and trying to spot trends of customers who paid us on time and who have not.”
“We use this data to change our decision rules, and this could let in more customers to the final stages or stop any that are not deemed worthy. We will find examples whereby a woman over 35 in a certain location and earning a certain amount have a 8% chance of defaulting on their loans. From here, we know that we can operate profitably at a 15% default rate – we will use data to alter our decision rules and look at this type of customer more favorably.”
To Minimize Default Rates
Trying to lower default rates is of paramount importance to credit card, mortgage and loan vendors. You ideally want to find customers who repay and do not default, even though this is inevitable. This is where loans like those that consolidate unsecured debt can be risky, as there are less assets to seize if the customer does not repay their loan.
“We therefore try to find shared characteristics of customers who default,” continues Dent.
“Do they live in certain areas, have certain professions or is it the loan amount or credit limit that they cannot handle?”
“We will analyze this data constantly to improve our processes and lower the default rate wherever possible.”
To Reduce Fraud
“Fraud is a huge financial burden for us as a lender and credit provider,” explains Richard Allan of business loans provider Funding Zest.
“Whether it is through fake applicants or stolen details, fraudsters are always looking for new and innovative ways to game our system to loans with no intention of paying them back.”
“We use data to find any patterns in the fraud. There are some obvious cues when the name of the customer does not vaguely resemble their email address or if we have seen the same mobile number come up on various occasions.”
“Otherwise, we look for patterns in the time of day, number of applications and even loan amounts to spot fraud and avoid it from being a huge liability on our business.”
It is expected that big data will continue to change the trajectory of the financial industry for years to come.