Customer Story

How National Debt Relief achieved 99%+ accuracy while processing debt settlement letters with Docsumo

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Docsumo is your go-to solution if you need a flexible solution to capture data from unstructured documents

“Docsumo does a very good job when it comes to our specific use-case. Debt settlement letters vary a lot from each other, but Docsumo manages to capture data accurately almost every single time at the processing speed which is unprecedented. We’re witnessing a straight through processing rate of over 95% with Docsumo.”

Daniel Tilipman

Co-Founder & Executive Board Member, National Debt Relief

About the customer

National Debt Relief is one of the largest debt settlement companies in the United States. The company negotiates on behalf of consumers to lower their debt amounts with creditors.
Financial services
Company size
780+ Employees
Portfolio Units
Document Processed
$10 Billion+

The case study: In a nutshell


Manually scanning debt settlement letters
50 agents scanned 350,000 debt settlement letters annually
Varying vendor formats was challenging for the team to parse
Little to no validation done on captured data
All documents had to undergo double manual entry


Using intelligent document processing software to extract data
Employees review only exceptions
All letters are now digitized with key data extracted from them
Docsumo's algorithms auto-classify letters and validate data with custom rules in real-time
95%+ straight through processing

The Challenge

Process large amounts of debt settlement letters

  • National Debt Relief (NDR) needed to process 350K debt settlement letters received from creditors annually.
  • The team of 50 agents were stretched as they tried to manually reconcile the letters with the negotiated deal.

Extract accurate data from letters

  • NDR had to scan and extract accurate data from unstructured settlement letters and feed it into Salesforce.
  • Data included names, account numbers, settlement amounts, payment terms etc.

Letters had varying structures and mostly had running text

  • Not only did the structures vary for different debt collectors, but the payment schedule was often written as running text.
  • Some of them were in tabular formats.

No in-built validation procedures

  • The manual extraction lacked a logical validation of debt amounts or instalments.

The Docsumo Solution

Ingesting debt settlement letters

  • API-based direct integration that seamlessly ingests debt settlement letters onto Docsumo.

Pre-processing and getting ready for data extraction

  • Inbuilt document pre-processors identified the letter formats (JPG, PDF, PNG etc.) and queued them up for data extraction.

Data extraction from unstructured text

  • Docsumo's OCR module used the vectorized position reference in a letter to extract data.
  • The OCR not only parsed through letters with varying fonts, layouts, image quality, and resolution; it even extracted data from the tables with 95%+ accuracy.

Intelligent categorization of key value pairs

  • Our proprietary NLP-based classification framework started rapidly learning from the debt settlement letter templates. It was trained to categorize key value pairs and line items.
  • Another algorithm started making intelligent predictions to identify the data within a letter.

Rule-based data validation

  • Once the data was extracted, a rule-based validation engine applied contextual data validation and correction algorithms.
  • For example, the validation ensured 12 instalments of $50 each amounted to $600 within the letter.

Integration with Salesforce

  • The data was extracted in a JSON format that was easily integrated into NDR's Salesforce instance via APIs and iframe.

Result: 99%+ Data extraction accuracy

Faster processing of unstructured data
Touchless processing using smart validation rules
Data accuracy with intelligent automation
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