How can data optimisation be used in the lending process?

Lenders these days have the task of carrying out checks digitally, and ensuring borrowers understand the exact details of the loan. This process can be hugely time consuming if done manually, but solely relying on automation can put your company at risk of errors. This is because running automation on data that has not been optimised could generate erroneous results.

Data insights have become an essential part in the decision-making process for many lending institutions. These insights are used to reduce risk, remove the need for subjective assessments, and ensure responsible lending. Data can be used to quickly respond to customer needs, seize opportunities and avoid risk.

 

What is a credit scorecard?

Credit scorecards are made up of a formula that uses data elements to determine a score that represents a company’s level of tolerance for risk. Examples of variables (data elements) used in a credit scorecard include:

1. Delinquency score: likelihood a business will be overdue for a payment
2. Failure score: likelihood a business will file for bankruptcy or seek legal relief from lenders

Lenders can use optimise their credit scorecards by using data optimisation and insights.

 

How can data be used to reduce risk to lenders?

Lenders can use data insights to get an overview of movements in the market and news about their customers. This cannot be done manually without huge effort, and becomes less and less feasible as you onboard more clients.  Lenders can use data insights to generate automated reviews that scan for changes in credit risk quality. This helps them make safer lending decisions.

Accounts might then get flagged for review so lenders can re-evaluate their terms. If a risk is spotted, lenders can choose to place a credit hold. On the flip-side, these reviews can also show lenders when the market is improving, so they can increase their fees or upsell their products.

If needed, accounts are then flagged for review, so you can re-evaluate credit, change terms, or if a risk is identified, place a credit hold – before they go to collections or impact your bottom line. However, automated reviews can also help you to identify positive changes in the market, presenting potential upsell opportunities in the process.