Question: Can you recommend a credit underwriting tool that helps lenders make more accurate risk assessments and approve more loans?

If you're in the market for a credit underwriting tool to help lenders assess risk more accurately and approve more loans, Zest is a top contender. The AI-powered software uses sophisticated math and data science techniques to make more accurate predictions and replace bad borrowers with better ones. It includes custom underwriting models, risk scoring tools and detailed documentation and validation tools, and can help banks, credit unions and other financial institutions increase loan originations and decrease losses.

Another useful option is Nova Credit, which provides a platform that taps into alternative credit data from 26 sources, including international credit bureaus and payroll companies. Nova Credit can help businesses more easily verify income and assess credit risk, and its Cash Atlas and Income Navigator products can boost approval rates by up to 30% while cutting fraud. The platform is geared for lenders trying to expand to a larger market, such as immigrants who don't have a credit history in the country.

For lenders that need to rapidly and accurately process financial documents, Ocrolus offers an AI-powered Document AI platform that classifies, extracts and processes financial documents with high accuracy. It automates the process so lenders can better assess risk and guard against fraud. The tool is useful for consumer lending, mortgage lending and small business lending, where lenders can get a better view of cash flow and income with clean, normalized data.

Last, Conductiv is an AI-based lending platform that can improve lending decisions by filling in data gaps and providing machine learning insights. It can handle a variety of lending products and can be integrated with existing systems. Conductiv's digital data collection and rules-based systems for auto approvals let lenders make decisions more rapidly and accurately, boosting approval rates and reducing manual processing time.