Technology may provide the best competitive advantage to lenders in the current economic climate. With fintechs, lenders can accelerate the loan origination process — smoothing underwriting, improving decision quality and continually identifying opportunities for greater efficiency.
When choosing modern technologies, lenders must assess the benefit of hosted or on-premise solutions and determine which works for their needs. Hosted options are mainly of interest to lenders that lack IT staff or do not want their staff dedicated to managing IT infrastructure. Regardless, the technology must scale to accommodate processing demand cycles, deliver continual functional improvements, and integrate with other hosted fintech services that enhance underwriting efficiency and decision quality.
Dozens of companies offer capabilities to enhance decisioning by providing a wealth of consumer data, machine learning and proprietary algorithms. Lenders can easily add capabilities that meet their specific needs and automatically initiate calls to applicants during the underwriting process.
Alternative credit data complements bureau data to portray a complete picture of an applicant’s financial position or provide evidence of creditworthiness for those with limited or nonexistent credit histories. Alt-data gives lenders greater confidence in loan decisioning and increases the probability of capturing deals that may have otherwise been declined.
Trended credit data provides up to 30 months of detailed payment history, including minimum amounts due or past due, balances and actual payments. It can reveal recent changes in financial behavior, giving lenders valuable insight into an applicant’s current financial position.
Fraud analysis analyzes millions of loan applications to identify signs of potential fraud such as identity theft, employment or income misrepresentation and inflated collateral. Applying machine learning algorithms to loan applications at point-of-entry lenders eliminates the risk of fraudulent applications that are likely to become defaults.
Credit modeling uses machine learning to develop a credit and risk model based on application, portfolio, bureau tradeline and alternative credit data. It evaluates applicant credentials to approve new loans, assign risk-based pricing or provide reasons when adverse-action notices are required. When these capabilities are integrated into an automated underwriting workflow, lending decisions require little to no manual underwriter intervention.
Analytics used with loan origination systems provide preconfigured dashboards and reports that help lenders visualize performance. Users point and click to select, group or filter data fields to summarize activities, track developing trends, or reveal process improvement and risk mitigation opportunities. Reports can be run on-demand to monitor performance in near-real-time, or they can run on a schedule.
With more than 20 years’ experience in the auto finance industry, Lana Johnson leads the charge to drive innovation as chief client officer at defi SOLUTIONS. defi SOLUTIONS is the Technology Partner of Auto Finance Excellence (AutoFinanceExcellence.org), a sister service of Auto Finance News.