Mortgage Technical Enablement Manager

Ocrolus integrates with Freddie Mac's AIM Check API. 10 Sep 2026 TL;DR: Ocrolus has integrated with Freddie Mac's AIM Check API, enabling mortgage lenders to receive an early income assessment from Loan Product Advisor(R)(LPA(R) asset and income modeler (AIM) directly inside Ocrolus Analyze. The integration uses W-2s and pay stubs already uploaded to Ocrolus, with no prior LPA submission required and no duplicate data entry. Income values, AIM eligibility per income type, the AIM Check API certificate and its expiration date are returned in Analyze and stored on the loan, and results can be imported to Encompass(R) by ICE Mortgage Technology(R), Today, Ocrolus announced its integration with Freddie Mac's AIM Check API, enabling mortgage lenders to receive an early income assessment from Loan Product Advisor(R)(LPA(R) asset and income modeler (AIM) directly inside Ocrolus Analyze. Lenders can access this capability using the W-2s and pay stubs already in their loan file, with no prior LPA submission required and no duplicate data entry. "Mortgage lenders already use Ocrolus throughout the origination process to transform bank statements, pay stubs and tax forms into decision-ready data," said Nadia Aziz, GM of Mortgage at Ocrolus. "With this integration, that same data can now power an early income assessment from AIM inside lenders' existing workflows. It's a significant step in reducing manual touchpoints and further accelerating lenders' path to clear-to-close." What the integration does. As an AIM Check API integrator, Ocrolus submits extracted W-2 and pay stub data to AIM Check API, enabling lenders to receive an early income assessment directly in Ocrolus Analyze. Through this integration, lenders benefit from: * An income breakdown (base, overtime, bonus and commission) returned from AIM * AIM eligibility returned per income type, displayed in Analyze and stored on the loan * The AIM Check API certificate, with its expiration date, saved to the loan file, with one-click re-run capability and a full submission history for audit purposes * The option to import income values, the Report ID into the LPA submission, and the certificate into Encompass(R) by ICE Mortgage Technology(R) By leveraging documents already in the loan file, lenders can accelerate income assessment without a prior LPA submission and without duplicate data entry. Income data collection and assessment remain among the most manual and time-intensive steps in mortgage origination. Through this integration, lenders can streamline income calculation, reduce manual effort and improve efficiency while increasing confidence in income calculations. Part of Ocrolus' Freddie Mac & Fannie Mae integration roadmap. This is Ocrolus's second government-sponsored enterprise (GSE) integration. Ocrolus's existing Fannie Mae integration covers self-employed and rental income. Freddie Mac's AIM Check API integration covers wage-earner income (W-2s and pay stubs) and is designed to complement it for lenders managing borrowers with different income profiles in the same pipeline. Both integrations are built on the same principle: income results should flow from the documents lenders already collect, not from a separate submission process. Loans originated using AIM are half as likely to produce defects and become delinquent (source: Digital Innovation Drives Loan Quality) Lenders who originate Freddie Mac-eligible conventional loans and currently use Ocrolus can speak with their account manager to learn more and get started. Key takeaways. * Ocrolus has integrated with Freddie Mac's AIM Check API, returning an early income assessment from AIM directly inside Ocrolus Analyze. * The integration uses W-2s and pay stubs lenders already upload to Ocrolus, with no prior LPA submission needed and no duplicate data entry required. * Results include an income breakdown (base, overtime, bonus and commission) and AIM eligibility per income type. Income values, the Report ID and the AIM Check API certificate are stored on the loan and can be imported to Encompass(R) by ICE Mortgage Technology(R). * This is Ocrolus' second GSE integration, alongside the existing Fannie Mae integration, which covers self-employed and rental income. * Loans originated using AIM are half as likely to produce defects and become delinquent (source: Digital Innovation Drives Loan Quality). FAQs. What is AIM Check API and how is it different from a complete Loan Product Advisor(R)(LPA(R) submission? AIM Check API enables access to LPA and asset and income modeler (AIM) independent of a complete LPA submission. It gives mortgage lenders an early view of the income assessment, as far upstream as lender pre-approval, without first submitting a complete loan application into LPA. Does Ocrolus perform the income assessment? No. Ocrolus submits extracted W-2 and pay stub data to AIM Check API and displays the results returned by AIM. The income assessment is generated by AIM. What documents does the Ocrolus AIM Check API integration require? The integration uses W-2s and pay stubs already uploaded to Ocrolus. It covers wage-earner income. Self-employed borrowers and tax-return income calculation capabilities will be added with Freddie Mac at a future date. What can be sent to Encompass(R) by ICE Mortgage Technology(R) by Ocrolus Analyze? Income values, the Report ID for the LPA submission, and the AIM Check API certificate are stored on the loan in Ocrolus and can be imported into Encompass(R) by ICE Mortgage Technology(R). How is this different from the Ocrolus Fannie Mae integration? The Fannie Mae integration covers self-employed and rental income calculated from tax returns and related documents. The integration of Freddie Mac's AIM Check API uses Ocrolus-extracted W-2s and pay stubs and covers wage-earner income. Ocrolus is working with Freddie Mac to add support for Self Employment and Rental income calculations at a future date.

Ocrolus has integrated with Freddie Mac's AIM Check API to help mortgage lenders accelerate loan underwriting. The integration allows Ocrolus customers to access early borrower income assessments from Freddie Mac's Loan Product Advisor asset and income modeler directly within their existing workflows. The system submits borrower income data extracted by Ocrolus to AIM Check API, covering wage-earner income from W-2s and pay stubs. This enables lenders to assess income before completing a full submission, without duplicate data entry. The integration aims to streamline income calculation and reduce manual effort in mortgage origination. Ocrolus, a vertical AI workflow platform for lenders founded in 2016, currently analyses roughly 750,000 credit applications monthly and serves over 500 customers.

LLMs vs. specialized models in production lending. TL;DR: In production lending, not all AI workloads call for the same type of model. Ocrolus uses large language models for high-variance, long-tail and reasoning-heavy tasks, and purpose-built specialized models for consistent, high-volume document processing - delivering greater than 99% accuracy at roughly one-tenth the per-document cost of managed LLM providers. This post explains the decision framework and how Ocrolus orchestrates both model types across the full range of financial document workflows. The tendency to apply a single AI model uniformly across a lending operation is understandable, but the economics and performance data argue against it. Large language models are genuinely capable across a wide range of tasks, but that generality comes at a price: in compute spend, in latency and in precision on the structured, high-volume extraction work that most lending workflows actually require. Purpose-built specialized models solve those problems, but they are not built to absorb the variability at the edges of any real document operation. The productive question is not which model type is superior in the abstract. It is which model type is right for each class of workload. Ocrolus processes millions of document pages per month across mortgage and small business lending. The platform's greater than 99% accuracy is not the result of one model doing everything. It is the result of deliberately mapping model type to workload type, and building the orchestration infrastructure to make both work together. Where LLMs fit. Any lending operation's document workload follows a distribution. The head of that distribution is high-volume and predictable: standard bank statements, W-2s, common pay stub formats. The tail is everything else - purchase contracts with addendums and cross-outs across separate document revisions, tax documents with atypical income structures, statements from smaller institutions with non-standard layouts. Large language models are well suited to that long tail. They handle high variance, absorb noise and deliver actionable outputs on document types where no purpose-built automation yet exists. The precision may not reach 100%, but getting 80 to 90 percent of the way there on documents that were previously fully manual is immediate, concrete value. Those initial outputs also reveal where the remaining gaps are and where engineering effort should go next. LLMs make the long tail tractable today while the infrastructure to automate it more precisely gets built. LLMs are also the right tool for reasoning-heavy workflows: assembling conditions across a full mortgage file, performing multi-step analysis on a complex loan application or working through problems where the value comes from drawing connections across data rather than extracting a single field consistently. A latency tradeoff is acceptable when the work being done would otherwise take a trained underwriter 30 to 60 minutes. Where specialized models win. For the head of the workload distribution - high-volume, consistent, well-defined - purpose-built specialized language models are the right fit. Ocrolus deploys a portfolio of these models: initialized from frontier open-source LLMs, fine-tuned on proprietary financial document data and optimized for specific extraction and classification tasks across mortgage and SMB lending. On consistent workloads, these models outperform general-purpose LLMs on every production metric that matters: accuracy, latency and cost. The cost gap is concrete. Processing through a managed LLM provider runs approximately 30 cents per document. Running the same workload on Ocrolus' purpose-built internal models costs approximately 3 cents. At the volume Ocrolus processes daily, that difference is not marginal; it is the gap between a scalable cost structure and one that becomes a ceiling on growth. Foundation model partners who have evaluated Ocrolus' approach have confirmed this logic. For focused, domain-specific extraction tasks run at scale, tuning and distilling specific models for specific document types produces better performance than applying a general-purpose LLM to the same problem. A model doing less, with more focus, does that job better. How orchestration connects both. Ocrolus does not treat LLMs and specialized models as competing choices. They function as layers within the same production inference stack. LLMs handle reasoning and initial extraction on complex or novel inputs. Specialized models run the high-volume core and serve as validators, confirming or flagging LLM outputs in workflows where precision requirements are non-negotiable. The result is a system where neither model type is overextended, and where the strengths of each compensate for the limitations of the other. Ocrolus is also developing an internal forms model designed for key-value extraction from financial documents. Paired with vision or language models that handle document layout interpretation, it will process the extraction layer at a fraction of current cost - extending the same layered logic to a broader set of document types, including those that today require significant manual review. This model selection framework - LLMs for variance and reasoning, specialized models for volume and precision, orchestration binding them together - is the production layer on which everything else is built. It is also what enables the next step: using the observability and eval infrastructure Ocrolus has in place to automate continuous improvement of the entire stack. That capability, and the engineering approach behind it, is where this series goes next. Key takeaways. * LLMs are most valuable for high-variance, long-tail workloads and reasoning-heavy tasks where the value of the output justifies the cost and latency * Purpose-built specialized models outperform general-purpose LLMs on consistent, high-volume extraction tasks - on accuracy, latency and cost simultaneously * The cost difference between managed LLM providers and purpose-built specialized models is significant: approximately 30 cents per document vs. 3 cents, an order-of-magnitude gap that compounds at production scale * Orchestrating both model types - LLMs for reasoning, specialized models for validation and high-volume extraction - is what makes greater than 99% accuracy achievable without sacrificing throughput * Model orchestration is not just an efficiency strategy; it is the infrastructure that makes systematic, automated improvement of lending AI workflows possible over time FAQs. What is the difference between large language models and specialized models in lending AI? Large language models are general-purpose AI systems trained on broad datasets that handle a wide variety of tasks. Specialized models are purpose-built for a specific class of work - in lending, that typically means financial document extraction, classification or validation - and are trained on domain-specific data. Specialized models consistently outperform LLMs on focused, high-volume tasks in accuracy, speed and cost. When should a lender's AI infrastructure use LLMs vs. specialized models? LLMs are most effective for high-variance workloads, new document types without established automation, long-tail queries and reasoning-heavy tasks like multi-document analysis. Specialized models are the better choice for consistent, high-volume document processing where precision, latency and cost efficiency are non-negotiable. Most production lending operations require both, deployed according to workload type. Why does the cost of LLMs matter so much in document processing? At scale, the per-document cost of running managed LLM providers adds up quickly. Processing through a general-purpose LLM provider can cost approximately 30 cents per document. Purpose-built specialized models running the same workload in-house can bring that to approximately 3 cents. For a platform processing millions of pages per month, the compounding effect of that difference directly affects pricing, margin and the ability to grow volume profitably. What is model orchestration in lending AI? Model orchestration is the practice of using multiple AI model types in a coordinated production stack, where each model handles the tasks it is best suited for. In Ocrolus' approach, LLMs handle reasoning and initial extractions on complex inputs, while specialized models run the high-volume core and validate outputs. Orchestration is what allows greater than 99% accuracy to hold across diverse document types and workload volumes. What is the "long tail" in financial document processing? The long tail refers to the portion of a lending operation's document workload that is low-frequency, high-variance and difficult to automate with purpose-built models because there is not enough consistent volume to justify specialized training. Purchase contracts, atypical tax documents and non-standard bank statement formats are examples. LLMs provide immediate value on this long tail by delivering usable outputs quickly, even without dedicated training data for a specific document type.