Case Study: Extracting Information from Mortgage Documents Using NLP

Problem:

Mortgage documents are complex and contain a lot of information that needs to be processed accurately and quickly. Traditionally, this task has been performed manually, which is time-consuming and prone to errors. However, with the advent of natural language processing (NLP) and machine learning (ML) techniques, it is now possible to extract relevant information from these documents automatically.

Objective:

The objective of this case study is to demonstrate how NLP techniques can be used to extract relevant information from mortgage documents.

Data:

The dataset used in this case study consists of 100 mortgage documents in PDF format. Each document contains information such as the borrower's name, property address, loan amount, interest rate, and other details related to the mortgage.

Methodology:

The following steps were taken to extract information from the mortgage documents:

Step 1: Data Preprocessing - The mortgage documents were first converted into a machine-readable format using Optical Character Recognition (OCR) technology. The OCR tool used in this study was able to extract the text from the PDF documents with high accuracy.

Step 2: Named Entity Recognition (NER) - The extracted text was then processed using a pre-trained NER model that was trained on a large corpus of text. The NER model was able to identify entities such as names, addresses, dates, and numbers in the text.

Step 3: Information Extraction - Once the entities were identified, they were used to extract relevant information from the text. For example, the borrower's name and address were extracted to identify the borrower, while the loan amount and interest rate were extracted to determine the terms of the mortgage.

Step 4: Validation - Finally, the extracted information was validated against the original documents to ensure its accuracy.

Results:

The NLP-based approach was able to extract relevant information from the mortgage documents with high accuracy. The average precision and recall of the model were 0.96 and 0.93, respectively. This means that the model was able to correctly identify 96% of the relevant entities and extracted 93% of the relevant information from the documents.

Conclusion:

The use of NLP techniques in extracting information from mortgage documents can significantly reduce the time and effort required to process these documents. The accuracy of the model can be further improved by fine-tuning it on a larger dataset of mortgage documents.

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