ForwardAI Announces PreciseMatch(TM), an Intelligent Transaction Mapping Technology That Validates the Integrity of Accounting Data for Banks, Fintechs and Lenders
Originally shared on Accesswire
Utilizing accounting and banking data, PreciseMatch improves confidence in small business underwriting
NEW YORK, NY / ACCESSWIRE / September 15, 2021 / ForwardAI, a fintech providing aggregated access to business & accounting data and analysis, today introduced PreciseMatch™, an intelligent transaction mapping technology that automatically cross-validates accounting systems with synced banking accounts, allowing for smarter, more efficient small business lending decisions.
Today, banks, fintechs and lenders leverage ForwardAI’s Precise API for credit decisions as its technology provides a real-time evaluation of the financial health of their small business customers. While accounting data provides a more robust understanding of a company’s overall financial health, it is more susceptible to potential fraud and distortion because it is self-reported. Since banking data comes directly from trusted sources the chances of data manipulation are slim, but it is difficult to make sense of banking data in isolation. ForwardAI’s PreciseMatch is designed to address these challenges and provide lenders with a more comprehensive view of the credit worthiness of a potential small business borrower.
ForwardAI’s PreciseMatch technology was designed to fix data reliability issues in traditional business lending, using a combination of machine learning algorithms, statistical analysis, and custom made rules. The solution makes identifying risky/fraudulent transactions and outliers in financial data a simple process, accelerating business lending by reducing the time-consuming and error-prone “stare and compare” method used today.
“ForwardAI completely transforms how banks and lenders can qualify SMB clients in their loan application process,” said Kunal Patel, Vice President at People’s United Bank. “With PreciseMatch, companies no longer need to spend countless hours manually comparing PDFs to verify transaction data. The technology can now identify in seconds with certainty that every transaction is accurate, enabling the SMB lending industry to serve their clients faster with more personalized solutions to help them succeed.”
PreciseMatch uses state-of-the-art technology to match transactions in accounting and banking data, and rates each matched transaction with a normalized confidence level (Confidence Scoring) ranging from 1% to 100%. After Confidence Scoring, PreciseMatch analyses the data and identifies all outlier and suspicious transactions. Financial institutions or lenders can then manually review highlighted transactions to ensure they are real or create system event thresholds within the API to automatically deny untrustworthy borrowers.
“We believe ForwardAI’s PreciseMatch technology can revolutionize cash-flow-based small business lending,” said Nick Chandi, CEO and co-founder of ForwardAI. “It opens new opportunities for the bank to create an embedded finance ecosystem to better serve its small business clients. With access to real-time sales data, customer payment behavior, vendors, and more, banks can avoid lengthy application processes for small amount loans for existing small and midsized business clients and reduce the costs associated with underwriting.
About ForwardAI
ForwardAI is improving access to aggregated accounting, financial, and business data for banks, lenders, and fintechs. With leading small business software integrations available, the intelligent PreciseMatch™ tech, deep client analysis, unparalleled forward-looking data insights, and complete suite of calculated KPIs and ratios, ForwardAI customers can turn client intake and assessment into only three quick steps. Solutions available include an API, a partner portal, and a client-facing cash flow forecasting and planning tool. To get started or learn more, visit ForwardAI.com.
Media Contact
Philipp Jago
philipp@calibercorporateadvisers.com
SOURCE: ForwardAI
Originally shared on Accesswire
