FINTECH DISRUPTION IN LENDING 2019

Disruption: A noun with a decidedly negative connotation.

However, when mentioned in the context of a technology discussion, disruption invariably means innovation. The world of finance is undergoing disruption, and as financial services organizations evaluate and adopt the latest fintech innovations, both the organizations and their customers are realizing the benefits.

Lending is a beneficiary of fintech disruption. Cloud, machine learning, and alternative credit data are reducing risk, improving process efficiency, and supporting better quality decisioning, Fintech disruption in lending is expanding opportunities for lenders and their clients.

Cloud is the Foundation of Fintech Disruption in Lending

A cloud-based loan origination solution can easily take advantage of fintech innovations that provide functionality unavailable in the core product. Cloud-based integration lets lenders select from a wide array of fintech capabilities to meet their unique lending needs. SaaS offerings let lenders enhance the core loan origination product with best-in-class fintech services that:

• Retrieve data files from major credit bureaus with a single request, automatically removing duplicate tradelines across bureaus and allowing “waterfall” logic to eliminate the need to resend inquiry data;
• Automate trailing loan doc collection to support a paperless stipulation collection process, collecting stipulations faster, directly from borrowers and their banks to minimize document tampering; and
• Verify financial calculations to ensure compliance with individual state and federal interest rate regulations within the loan originations workflow.

Easily configurable integration of these and many other financial services helps lenders compete in the current market.

Machine Learning: Disrupting Hundreds of Credit Models

Machine learning—algorithms and statistical models that uncover patterns and rely on inference to effectively perform specific tasks without using explicit instructions—is being applied to the vast volumes of credit, transactional, and customer data to improve credit underwriting.

Most lenders have dozens or hundreds of credit and risk models. Machine learning can replace them all. It extracts greater value from credit bureau data and draws insights from a multitude of variables in a single credit model, leading to more accurate lending decisions, reduced default rates, and lower portfolio risk.

Machine learning is also disrupting the activities of individuals and cartels determined to fraudulently obtain funding. Through an in-depth analysis of tens of millions of loan applications and loan histories, machine learning creates models to detect indicators of false identity, income and employment misrepresentation, inflated collateral, and straw borrowers.

Lenders can apply these models to automatically evaluate new loan applications. When fraud is suspected, the model indicates the type of fraud and provides a fraud score (confidence level). High scores are likely candidates for declines. Mid-range and lower scores can be reviewed by an experienced underwriter who might add stipulations for conditional approval or issue a decline. Automated fraud detection applies a consistent method to disrupt fraud before it damages your portfolio.   

Alternative Credit Data: Underbanked and Unbanked Opportunity 

Fintech is disrupting long-held notions of creditworthiness. Millions of underbanked and unbanked consumers who have thin to non-existent credit histories are nonetheless creditworthy. They are overlooked when rated solely on their bureau credit scores. With widespread e-commerce, there are now many other sources of consumer data that provide insight into a borrower’s financial standing. Alternative credit data services aggregate utility payments, rental records, address changes, cell phone payments, and other non-traditional sources of information.

When bureau data is non-existent, a lender can easily access alternative credit data to assess an applicant’s ability to pay. An applicant who shows consistent, on-time payment of monthly expenses is low risk. An applicant with late payments and frequent change of address is high risk. Alternative credit data uncovers new opportunities for lenders and boosts the financial possibilities by giving a more complete assessment of applicant risk.

Fintech Disruption in Lending Means Opportunity

Fintech advancements prompt lenders to seriously evaluate their lending platforms and ability to compete in the current market. Although it can be a major undertaking, lenders with legacy lending platforms should consider the move to a cloud-based loan origination solution.

While we’ve only mentioned a few fintech capabilities, a wide range of new services allow lenders to select and quickly integrate the functionality they need. For lenders, fintech disruption in lending in 2019 means opportunity.