Infrrd, a leading provider of AI-driven document processing solutions, has officially announced the launch of its brand-new MortgageCheckai solution, which happens to be a revolutionary platform designed to expedite pre-fund and post-close loan audits for mortgage lenders and servicers. According to certain reports, MortgageCheck is understood to have a large part of its value rooted in mortgage auditing, empowering loan closers and QC reviewers to speed up loan reviews with unmatched data accuracy, while simultaneously upholding the audit’s integrity. You see, in a more concrete sense, the solution’s cutting-edge AI technology makes it possible for loan closers or QC auditors to reduce the manual review of loan documents by at least 50%. More on the same would reveal how MortgageCheckai banks upon patented AI algorithms to automatically index, categorize, and scrutinize loan documents with unprecedented data accuracy. On top of that, by effectively automating repetitive manual tasks, the solution allows auditors to focus on high-impact activities, and therefore, realize a more expedited brand of audit completion and enhanced operational efficiency. Having referred to the initial bits and bobs, we must talk about the whole value proposition on a slightly deeper level, beginning from the solution’s ability to auto-stack loan documents. This translates to the way MortgageCheckai can organize document packages into lender-specified sequence by document type and version. Next up, we must get into the prospect of advanced document versioning, a prospect which means that the solution can automatically identify the latest and correct version of the document.
“Our mission with MortgageCheckai is to transform the mortgage auditing landscape and make auditors future-ready with AI,” said Amit Jnagal, CEO of Infrrd. “We understand the hurdles faced by mortgage lenders and servicers in conducting thorough and efficient audits. With MortgageCheckai, we’re delivering a revolutionary solution that enables auditors to streamline their processes, mitigate risky loan decisions, and stay compliant effortlessly.”
Another area where the solution tends to automatically identify relevant components is document, data, and fee issues. All in all, MortgageCheckai can detect more than 60 common issues, such as missing documents, data mismatches, and expired CDs. Once detected, it can then also flag them for auditor review. Moving on to the solution’s built-in CD balancer, it basically comes in handy when the agenda is to extract and reconcile estimated and final closing details from Loan Estimates and Lender/Title Company Closing Disclosures. Such functionality, like you can guess, does a lot to help users spot any discrepancies and out-of-balance conditions, thus enabling them to focus only on anomalous cases. Then, there is a facility dedicated towards intelligent data validation. This particular facility is tasked with comparing digitally supplied and extracted document data across different documents and data sources, doing it all for the purpose of flagging mismatches. Joining the same is an ability to track changes. By that, we are referring to how MortgageCheckai can track on its own user-driven data changes in a comprehensive audit trail including date, user, and comments, with automatic and user-generated issue generation and tracking.
“The financial services industry is undergoing rapid transformation, and by 2030 AI will change the way loans are audited,” said Jnagal. “With MortgageCheckai, we’re leading the charge towards automation in mortgage auditing, setting the gold standard for efficiency and a competitive advantage for AI believers.”
In the context of this development, Infrrd has also partnered with Asurity, a leading provider of compliance-focused services and solutions. Under the agreed terms, Infrrd is applying its cutting-edge technical capabilities to harness Asurity’s extensive mortgage industry expertise. Assurity’s technology is actually what would help Infrrd automate laborious mortgage closings and QC processes with compliant, complex document types, and variations in loan structures
