How to Automate Data Extraction from Mortgage Documents

Leverage bots to auto-classify various mortgage documents

Bots classify mortgage and loan application, credit reports, billing statements, insurance and tax documents in valid category before the data extraction begins.

Leveraging technology and tools reduces time required for classification of mortgage documents across data extraction stages. Artificial Intelligence (AI) based bots index and classify structured as well as unstructured documents into different categories in a highly secured environment.

AI-bot led mortgage documentation classification framework:

• Taxonomy managers mostly rely on ML backed algorithms to understand the structure of documents and data which is to be processed
• Optical Character Recognition (OCR) is used to automate data capture
• Auto-classifiers are used to assess digitized text and look for patterns; which then leverages deep Learning algorithms to categorize that text.
• Based on text classification, documents are classified into respective categories.

Set and use Macro rules to detect inconsistencies

Irrespective of you leveraging automated data extraction, errors can creep in, and you must not let them go undetected and grow greater in magnitude. But how to detect inconsistencies?

Creating and setting macro rules is essential to prevent common inconsistencies during data capture. Rules allow your automation initiatives to mature and enhance your AI-capabilities. This is especially important when you are aware about dynamics of different mortgage types.

Create robust rule-based mechanisms that quickly reveal inconsistencies – errors, missing values etc. – and trigger alerts. Rules must take into consideration mortgage types and the stringent conditions they involve.

Rule-based mechanism to detect inconsistencies across mortgage types:

• Simple mortgage: There is an ambiguity in the interest rate (E.g. 99.75% instead of 9.75%)
• Usufructuary mortgage: Income amounts have been incorrectly specified or are not unique across all the fields.
• English mortgage: There is a mismatch in loan repayment dates. For instance, at one point it is 12/10/2025 and at another point it is 10/12/2025.
• Mortgage by conditional sale: Conditions of sales and repayment loans are as per local regulations.
• Mortgage by title deed deposit: Mandatory data on debt, deposit of title deeds, and security are not missing.

Use multiple scanning and data capture technologies

Mortgage process involves complex set of documents, and not each of them have neatly printed texts. Documents contain illegible handwritings, signatures and even stamps. So, sole reliance on a single data capture technology doesn’t help.

Have a combination of data-capture technologies as a part of your resource pool, which you must keep dynamic, bettering and adding technologies from time-to-time.

Optical Character Recognition (OCR) for simple-to-interpret documents like:

• ID and Social Security number
• Address Proof
• Federal tax returns
• Debt documents (long term debts such as car or student loans)
• Documents that prove educational qualification(s)
• Age proof (Passport, certificates from statutory authorities)
• Proof of any other sources of income

Intelligent Character Recognition (OCR) for complex documents like:

• Pay stubs from last 30 days/pay slips for last few months
• Hand-filled application form
• Signatures
• Property documents with handwritten texts
• Salary slips

Magnetic Ink Character Recognition (MICR) is the best way you can capture details from bank statements and cheques.

Use Natural Language Processing (NLP) to automate document abstraction

Document abstraction is critical to the entire data extraction workflow; especially when legal or financial information is being reviewed. Automated mortgage document abstraction built using Natural language processing (NLP) can allow you to streamline the abstraction process.

Technology backed document abstraction:

• Data capture tools convert documents into machine-readable HTML formats
• Natural Language Programming (NLP)-enabled mechanisms train the system to understand text grammar.
• Machine Learning algorithms evolve over the period and train themselves
• Important legalities are extracted and exported into the mortgage document abstract, consisting of a Word or Excel document.

Facilitate query-based verification and validation

Data extraction is complete only when extracted data is duly verified and validated. Reliable mechanism to understand the legalities of mortgage processing is imperative. Once data is plugged into the database into respective fields across different tables; assess what percentage of information is extracted accurately.

Here are some steps to building and using SQL business rules for the task:

• Build stored procedures to handle bi-directional workflows and repetitive tasks.
• Apply condition-based filters to drive segment-wise verification and validation.
• Validate the domain integrity of fields (columns) against check constraint. Create multiple column constraints to simultaneously check values in multiple columns.
• Create queries to count instances of NULL values across various fields.
• A great strategy is to go with pilot implementation: create test cases, execute them and export results. Compare results to conclude.
• Keep stored procedures, macros and queries iterative to adapt ongoing changes in the conditions.