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6 articles tagged “underwriting”

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Mortgage

Underwriting the Bank-Statement Loan With AI: Deposit Analysis That Is Auditable, and the Ability-to-Repay Rule Non-QM Never Escapes

Bank-statement loans qualify a self-employed borrower on deposits instead of tax returns, which makes the income calculation the whole underwrite and the easiest number to get wrong. These are non-QM loans, and non-QM does not mean no ability-to-repay: Reg Z 1026.43(c) still requires a reasonable, good-faith determination on all eight factors. Here is how an AI agent classifies twelve to twenty-four months of deposits, the transfer that double-counted income until we caught it in shadow mode, and the reasonableness call the underwriter still signs.

Sep 23, 20264 min read
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Mortgage

Mortgage Fraud Detection at Origination: What an AI Agent Can Flag, What It Cannot Decide, and Where the SAR Obligation Starts

Most origination fraud is not a forged document, it is a set of facts that are each plausible and collectively wrong: an owner-occupancy claim that does not fit the file, an employer that only exists on paper, a gift that is really a loan. Where an AI agent reads the whole file for the pattern instead of each document in isolation, what FinCEN's mortgage AML rule and the Red Flags Rule actually require, and the line between a fraud flag and a fraud determination.

Aug 25, 20266 min read
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Mortgage

AI Income Calculation for Self-Employed Borrowers: The Part of Underwriting Where the Math Has to Be Auditable

Self-employed income is the hardest number in a loan file and the easiest one to get wrong. How to build an AI income engine that matches Fannie Mae Form 1084, holds up under ATR/QM, and carries an audit trail an underwriter and a model validator both trust.

Aug 8, 20266 min read
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Mortgage

Calculating Self-Employed Income With an AI Agent: The 1084 Cash-Flow Analysis, the 4506-C Transcript, and the Reasonableness Call the Underwriter Owns

Self-employed income is where mortgage underwriting is slowest, most inconsistent, and most exposed to fair-lending risk, because two underwriters can read the same tax returns and reach different qualifying income. An AI agent can run the Form 1084 cash-flow analysis the same way every time and document every add-back to its line on the return. What it cannot do is make the reasonableness determination Fannie assigns to the underwriter, and building the agent so it stops at that line is the whole design problem.

Aug 6, 20267 min read
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Compliance

Ability-to-Repay Under Reg Z 1026.43 in AI-Assisted Underwriting: The Eight Factors, the Revised General QM Price Test, and the Documentation Boundary the Agent Cannot Cross Alone

The ATR/QM rule at Reg Z 1026.43 has been through three rounds of major revision since 2013, and the current General QM definition anchors on an APR-to-APOR price threshold rather than the old 43-percent DTI cap. AI underwriting participates in ATR analysis by producing the eight-factor computation, but the documentation of the third-party verification the rule requires and the qualification decision the rule allocates to the creditor are places the agent's autonomy stops. Where AI actually sits in the ATR workflow and what the audit file has to contain.

Jul 3, 202614 min read
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Document Intelligence

Automating Mortgage Underwriting with AI Document Intelligence

How AI-powered document intelligence is transforming mortgage underwriting from a manual bottleneck into an automated, accurate, and auditable process.

Feb 5, 20252 min read
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