The 2020 large loan management software trends are focused on achieving two main goals: mitigating risks and identifying new money lending software. The ability to use data to support both goals will determine lending success in the new year and beyond.

New data sources reduce risk and empower

The Internet, cloud computing, mobile apps and the digitization of business transactions are generating more valuable data on consumer habits. While bureau ratings have served lenders well for decades, new readily available data sources enable lenders to make lending decisions based on a more detailed, up-to-date, and accurate picture of a consumer's paying capacity.

Alternative credit data reveals additional aspects of consumer behaviour with information about current and savings account balances, utility and cell phone payments, change of address, education, rental records and bankruptcies.

Credit trend data provide insight into recent credit card payments and indicate an improvement, decrease, or stable solvency.

Employment and Income Verification Services confirm or deny the information in the loan application.

These cloud-based data sources integrate seamlessly with modern lending solutions with no programming required. More importantly, these additional data sources enable lenders to:

  • Reduce the risk of making credit decisions;
  • Identify new lending opportunities that might otherwise be ignored.

Lenders already using workflow and decision-making rules to automate the underwriting process can now use these data sources to create a more detailed picture of the applicant's financial situation.

AI Helps Reduce Risk and Find New Service Opportunities

Lenders also benefit from service investment. Lenders use artificial intelligence (AI) to analyze their portfolio more deeply, provide advance notice of potential delays, and identify opportunities for additional products or services.

Artificial intelligence used to service loans can provide valuable insights into collection efforts. Based on continuous analysis of portfolio performance and a large amount of additional data about the borrower's profile, predictive models can correlate borrower attributes and payment behaviour to identify accounts that are likely not to default. As an automated process, AI continuously monitors portfolio performance and data collection. Predictive models define payment schemes that indicate that the borrower is struggling to make a monthly payment, for example:

ü Overdue payments in consecutive months;
ü Payment in two payments at once; and
ü Payments are received every month later and later.

AI can analyze hundreds of payment variables and trends to develop forecasting models tailored to a lender's unique market segments.

Automated workflows with advance notice of potential issues can guide reps through a process designed to prevent delays that lead to defaults, such as:

1. Contact preferences - phone, email, text, message;
2. The optimal day and time for a telephone conversation;


3. Suggested payment options or strategies avoid late payment.

An automated approach to proactively mitigate risk ensures process consistency. Well-defined workflows with specific rules, based on information obtained by AI, ensure that all steps and actions comply with the rules set by the Consumer Financial Protection Bureau (CFPB).

The detailed data obtained or generated during the loan cycle also gives lenders an advantage in offering complementary or complementary products to qualified borrowers in banking software development company DjangoStars. As loans and leases reach maturity, lenders can use borrower characteristics, updates from unconventional data sources, and portfolio risk analysis to offer offers tailored to the unique needs of the borrower, all with the goal of improving retention rates.