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How to Automate Data Extraction from Mortgage Documents

automated mortgage data extraction to process voluminous documents
Leveraging bots and RPA for data extraction from mortgage documents accelerates loan processes and enhances lender’s ability to process voluminous documents.

Digitization is changing the way mortgage industry operates, empowering it to recognize critical competencies, performance metrics, and measures for process improvement. The number of non-traditional lenders is fast increasing. In the US alone they account for 37% of total mortgage originations. And these modern-day lenders demand fast loan processing.

Mortgage data extraction calls for automation to address key pain points such as compliance and document security; technology lags and customer experience. It is an imperative for mortgage lenders seeking to accelerate their mortgage processes.

Around 90% of C-suite executives are interested in leveraging technology to streamline data extraction for mortgage processing. Reducing errors, achieving shorter cycle times, and optimizing costs are the reasons why mortgage companies are opting for automated loan data extraction.

Automated capture of 700K property records per month from 500 US counties helped a leading real estate data aggregator update its property data pool in real-time. The data, primarily from Mortgage documents, enhanced customer experience and engagement.

Read how →

Challenges in manual data extraction from mortgage documents

Challenges in manual data extraction from mortgage documents

Benefits of automated mortgage data extraction

Automating processes addresses the inherent challenges associated with mortgage data extraction. Some of the benefits of automated mortgage data extraction have been discussed below.

  • Accuracy improvement – Loan application processing accuracy is tremendously enhanced. If with manual processing, there are 10 out 100 loans with errors then automation brings the rate down to 1-2 out of 100 loans.
  • Processing-quantity improvement – Manually processing a single loan case runs into a number of days. Automation brings down the loan processing time and increases per-period number of loans processed.
  • Better adherence to compliances – Rule-based data-capture mechanisms create predictable and auditable processes that allow you to be specific in terms of meeting compliances.
  • Time and cost optimization – When you are required to process increasing number of mortgage transactions; only automated workflows help you meet stringent deadlines.
  • Iterative procedures – Having bespoke solutions for you can help you to keep iterative processes at your disposal. These processes allow you to analyze the mortgage processing efficiency and arrive at the step(s) that requires improvement.

A loan providing company, upon implementing automation, increased productivity 300% without any increase to the size of their staff. They successfully reduced file review time by one third; from 24-48 hours compared to the 3-7 days previously. Manual processing restricted them to handle just over 300 loans per month; but automated document processing increased it to nearly 1000 loans in a month. Built-in reporting capabilities to track and evaluate productivity metrics is the added advantage of automated process they enjoy.

Source: loanlogics.com

How to automate data extraction from mortgage documents

Professionals constantly dealing with mortgages should essentially have an in-depth understanding of the implementation workflow to assess the benefits in your business context. Summarized below are important steps to a robust automated data extraction ecosystem for your mortgage processing tasks.

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.

Implementing automated data extraction helped US-based online-lender, handle a targeted volume of 100K+ documents per month, with 90% plus accuracy. The Machine Learning – ML backed algorithms improved the processing speed by 10 times and reduced the cost up to 70%.

Source: bisok.com

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.

Want to handle volumes of mortgage document swiftly and accurately?

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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.

Conclusion

Moving from manual to automated mortgage data extraction is a long-term process. Before you decide to bring in automated data extraction, assess if you have mortgage data extraction specialists onboard, and the budget to enable automation. Find out how well you can translate your mortgage processing knowledge into technology-led processing. How would you ensure that your drive to implement automation does not affect your customer services?

Having answers to these questions is important for creating a sustainable value across your mortgage processing chain. Remember, when you are ready to invest in mortgage automation, but lack direction, collaborate with mortgage documents data extraction experts.

About Author:

 heads the digital content for Hi-Tech BPO, an India based firm recognized for the leadership and ability to execute innovative approaches to data management. Hi-Tech delivers data solutions for all the aspects of enterprise data management; right from data collection to processing, reporting environments, and integrated analytics solutions.

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