Custom AI Solutions Deliver Fast and Reliable Data Extraction for Student Loans

Summary 

This student loan underwriting company specializes in processing and approving educational loans based on comprehensive transcript analysis. Traditionally, their operations heavily relied on manually extracting critical data from student transcripts. 

This process was both time-consuming and susceptible to errors due to the lack of standardized formats across transcripts from various educational institutions. 

In response, they partnered with us to develop a customized AI solution that integrates advanced OCR technology, a vector database, a traditional database, and a Large Language Model (LLM) tailored to their specific needs. Our solution involved training the AI with over 250 diverse transcripts to handle a variety of document qualities and formats.

With the implementation of our AI Agent, the processing time was reduced from 5-10 minutes to just 45 seconds, achieving over 90% accuracy and speeding up the loan decision-making process. This led to a streamlined loan underwriting workflow and enhanced decision accuracy.

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Challenges with Academic Transcript Processing in Student Loan Underwriting 

The loan underwriting industry demands high precision, as the extracted data significantly influences loan decision-making. The manual extraction process is lengthy and error-prone due to the detailed and varied nature of the transcripts. 

As part of their application, students would submit their academic transcripts and financial data. Like other loan companies, our client used this data to assess each student’s loan repayment capacity. However, in order to do so, the client first needed to manually extract crucial data points from submitted documents – including GPA, total credits, highest education level, worst grade, and total years of education. Automating the process with conventional solutions also proved to be nearly impossible. 

Key challenges the client faced included:

• Lack of standardization in the document’s structure
  

• Time-consuming manual processes
  

• Error-prone data handling
  

Lack of standardization in the document’s structure:

Transcripts vary widely across different educational institutions, each presenting information differently. For example, some transcripts might list multiple GPAs (cumulative, per term, weighted, unweighted). This was complicating the extraction of the correct figures needed for loan decisions.

Time-consuming manual processes:

Analysts spent extensive time locating specific data (e.g., determining the worst grade among diverse grading systems) and other necessary information. Each transcript took 5-10 minutes to process, delaying decision timelines.

Error-prone data handling:

The detailed manual review required sifting through dense academic information. That task was demanding and exhausting, therefore increasing the risk of inaccuracies. Minor errors in data extraction could impact the accuracy of a loan decision, potentially leading to unfavorable outcomes for both the lender and the borrower.

Faced with these issues, the client partnered with us to seek an automated solution that could handle transcript diversity efficiently and accurately. They aimed to replace the slow and error-prone manual process with a more reliable, AI-driven approach, ensuring faster and more precise underwriting decisions.

Custom AI Solutions Streamline Data Extraction for Student Loans

Our AI-driven solution involves several steps to automate data extraction while maintaining high accuracy. As students submit their academic transcripts, our AI Agent processes these documents and extracts the aforementioned critical information (overall GPA, total credits, highest education level, etc.).

For this project, we utilized both a vector database and a traditional SQL database to enhance the performance of our chosen LLM. We used the vector database to quickly find and retrieve similar student transcripts by comparing numerical embeddings, which helped provide relevant context to the LLM for more accurate data extraction. 

Meanwhile, to store and manage larger volumes of structured data efficiently we used the traditional (SQL) database. These were the detailed extraction results. 

Another challenge we faced was the poor quality of some images, such as cell phone photos of crumpled transcripts. To extract text from these non-machine readable documents relatively quickly, we did intensive work on the OCR. We had to find the correct parameters to produce the best results. 

Together, these technologies allowed us to streamline the student loan underwriting process by combining fast, context-aware processing with robust data management.

We designed each step in our solution to directly counter the specific issues identified during the client’s manual processing phase. 

Here's how we approached it:

1. Training on diverse data
   

We trained our model on over 250 diverse student transcripts. This comprehensive dataset ensured that our AI system learned to handle the wide variety of formats and data presentations found in different educational institutions' transcripts.

2. Manual annotation
   

Initially, we manually annotated several documents to extract key data points accurately. This step created a benchmark for the AI Agent to understand what critical information looks like and where it typically resides within the transcripts.

3. Embedding calculation and storage
   

We calculated and stored embeddings of these example documents in a vector database, allowing our AI Agent to reference the context and specifics of various data points quickly.

4. Creating a selection pipeline
   

We did this to identify and select the most similar examples from our database when a new document is processed. This step ensures consistency in data extraction from transcripts, even when the structure varies.

5. In-context examples for LLM
   

We presented these similar documents as in-context examples to an LLM. This step provided the LLM with relevant, real-world examples to improve its understanding and accuracy.

6. Custom instructions for LLM
   

We developed specific instructions for the LLM, guiding it on how to extract necessary data based on the client's requirements and the nuances observed in different transcript types.

This way we tailored the AI’s approach to each transcript, ensuring that all client-specific data points were accurately captured.

7. Result generation and post-processing
   

After the LLM generated the extraction results, we moved into a post-processing phase where we validated and refined the data. This last step ensured that the data met the client's specific needs and maintained the highest level of accuracy for informed loan decisions.

Through these targeted and automated solutions, we effectively transformed the client's transcript analysis process, making it faster, more accurate, and less reliant on manual intervention. 

Enhanced Accuracy and Speed Accelerate Loan Decision Processes

The client has completely transformed their loan underwriting workflow with our custom generative AI solution. Instead of time-wasteful manual data extraction from diverse student transcripts, they now use an automated system to fetch necessary data. 

Initially, manually processing a single transcript took about 5-10 minutes. Our AI solution has reduced this to just 45 seconds per document, achieving over 90% reduction in processing time. We have also maintained an impressive 90% accuracy across various transcript formats.

This substantial decrease in processing time has increased operational efficiency and accelerated decision-making for student loans. As a result, our client can now handle a greater volume of applications with improved precision and reduced overhead.

These enhancements in processing speed and accuracy are setting new standards in the student loan industry. Motivated by these results, our client is moving forward with the production phase. They are also considering expanding other AI capabilities to automate their workflows further. 

Our vision at Multimodal is strategically deploying AI agents to gradually transform any enterprise into an AI-powered organization. Based on the successes we've seen with this and other clients, we anticipate that this will further enhance businesses’ productivity and increase customer satisfaction.