Automating advanced doc processing: How Onity Group constructed an clever resolution utilizing Amazon Bedrock

Within the mortgage servicing business, environment friendly doc processing can imply the distinction between enterprise development and missed alternatives. This publish explores how Onity Group, a monetary providers firm specializing in mortgage servicing and origination, used Amazon Bedrock and different AWS providers to rework their doc processing capabilities.

Onity Group, based in 1988, is headquartered in West Palm Seaside, Florida. By means of its major working subsidiary, PHH Mortgage Company, and Liberty Reverse Mortgage model, the corporate offers mortgage servicing and origination options to owners, enterprise shoppers, buyers, and others.

Onity processes hundreds of thousands of pages throughout a whole lot of doc varieties yearly, together with authorized paperwork resembling deeds of belief the place vital info is usually contained inside dense textual content. The corporate additionally needed to handle inconsistent handwritten entries and the necessity to confirm notarization and authorized seals—duties that conventional optical character recognition (OCR) and AI and machine studying (AI/ML) options struggled to deal with successfully. By utilizing basis fashions (FMs) offered by Amazon Bedrock, Onity achieved a 50% discount in doc extraction prices whereas enhancing total accuracy by 20% in comparison with their earlier OCR and AI/ML resolution.

Onity’s clever doc processing (IDP) resolution dynamically routes extraction duties based mostly on content material complexity, utilizing the strengths of each its customized AI fashions and generative AI capabilities offered by Amazon Net Providers (AWS) via Amazon Bedrock. This dual-model strategy enabled Onity to handle the dimensions and variety of its mortgage servicing paperwork extra effectively, driving important enhancements in each value and accuracy.

“We would have liked an answer that might evolve as rapidly as our doc processing wants,” says Raghavendra (Raghu) Chinhalli, VP of Digital Transformation at Onity Group.

“By combining AWS AI/ML and generative AI providers, we achieved the proper stability of value, efficiency, accuracy, and velocity to market,” provides Priyatham Minnamareddy, Director of Digital Transformation & Clever Automation.

Why conventional OCR and ML fashions fall brief

Conventional doc processing introduced a number of elementary challenges that drove Onity’s seek for a extra refined resolution. The next are key examples:

• Verbose paperwork with knowledge parts not clearly recognized
  • Concern – Key paperwork in mortgage servicing include verbose textual content with vital knowledge parts embedded with out clear identifiers or construction
  • Instance – Figuring out the precise authorized description from a deed of belief, which could be buried inside paragraphs of legalese
• Inconsistent handwritten textual content
  • Concern – Paperwork include handwritten parts that change considerably in high quality, fashion, and legibility
  • Instance – Easy variations in writing codecs—resembling state names (GA and Georgia) or financial values (200K or 200,000)—create important extraction challenges
• Notarization and authorized seal detection
  • Concern – Figuring out whether or not a doc is notarized, detecting authorized court docket stamps, verifying if a notary’s fee has expired, or extracting knowledge from authorized seals, which are available a number of shapes, requires a deeper understanding of visible and textual cues that conventional strategies would possibly miss
• Restricted contextual understanding
  • Concern – Conventional OCR fashions, though adept at digitizing textual content, typically lack the capability to interpret the semantic context inside a doc, hindering a real understanding of the knowledge contained

These complexities in mortgage servicing paperwork—starting from verbose textual content to inconsistent handwriting and the necessity for specialised seal detection—proved to be important limitations for conventional OCR and ML fashions. This drove Onity to hunt a extra refined resolution to handle these elementary challenges.

Answer overview

To handle these doc processing challenges, Onity constructed an clever resolution combining AWS AI/ML and generative AI providers.

Amazon Textract is a ML service that automates the extraction of textual content, knowledge, and insights from paperwork and pictures. By utilizing Amazon Textract, organizations can streamline doc processing workflows and unlock invaluable knowledge to energy clever purposes.

Amazon Bedrock is a completely managed service that provides a alternative of high-performing FMs from main AI firms. By means of a single API, Amazon Bedrock offers entry to fashions from suppliers resembling AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon, together with a broad set of capabilities to construct safe, personal, and accountable generative AI purposes.

Amazon Bedrock provides you the pliability to decide on the FM that most closely fits your wants. For IDP, widespread options use textual content and imaginative and prescient fashions resembling Amazon Nova Professional or Anthropic’s Claude Sonnet. Past mannequin entry, Amazon Bedrock offers enterprise-grade safety with knowledge processing inside your Amazon digital personal cloud (VPC), built-in guardrails for accountable AI use, and complete knowledge safety capabilities which are important for dealing with delicate monetary paperwork. You possibly can choose the mannequin that strikes the correct stability of accuracy, efficiency, and price effectivity to your particular utility.

The next determine exhibits how the answer works.

1. Doc ingestion – Paperwork are uploaded to Amazon Easy Storage Service (Amazon S3). Importing triggers automated processing workflows.
2. Preprocessing – Earlier than evaluation, paperwork bear optimization via picture enhancement, noise discount, and format evaluation. These preprocessing steps assist facilitate most accuracy for subsequent OCR processing.
3. Classification – Classification happens via a three-step clever workflow orchestrated by Onity’s doc classification utility. The method outputs every web page’s doc kind and web page quantity in JSON format:
   1. The applying makes use of Amazon Textract to extract doc contents.
   2. Extracted content material is processed by Onity’s customized AI mannequin. If the mannequin’s confidence rating meets the predetermined threshold, classification is full.
   3. If the doc isn’t acknowledged as a result of the mannequin isn’t skilled with that doc kind, the applying robotically routes the doc to Anthropic’s Claude Sonnet in Amazon Bedrock. This basis mannequin, together with different textual content and imaginative and prescient fashions resembling Anthropic’s Claude and Amazon Nova, can classify paperwork with out further coaching, analyzing each textual content and pictures. This dual-model strategy, utilizing each Onity’s customized mannequin and the generative AI capabilities of Amazon, helps to optimally stability value effectivity with velocity to market.
4. Extraction – Onity’s doc extraction utility employs an algorithm-driven strategy that queries an inside database to retrieve particular extraction guidelines for every doc kind and knowledge aspect. It then dynamically routes extraction duties between Amazon Textract and Amazon Bedrock FMs based mostly on the complexity of the content material.
   For instance, verifying notarization requires advanced visible and textual evaluation. In these instances, the applying makes use of the capabilities of Amazon Bedrock superior textual content and imaginative and prescient fashions. The answer is constructed on the Amazon Bedrock API, which permits Onity to make use of completely different FMs that present the optimum stability of value and accuracy for every doc kind. This dynamic routing of extraction duties permits Onity to optimize the stability between value, efficiency, and accuracy.
5. Persistence – The extracted info is saved in a structured format in Onity’s operational databases and in a semi-structured format in Amazon S3 for additional downstream processing.

Safety overview

When processing delicate monetary paperwork, Onity implements sturdy knowledge safety measures. Knowledge is encrypted at relaxation utilizing AWS Key Administration Service (AWS KMS) and in transit utilizing TLS protocols. Entry to knowledge is strictly managed utilizing AWS Id and Entry Administration (IAM) insurance policies. For architectural finest practices constructing monetary providers Business (FSI) purposes in AWS, seek advice from AWS Monetary Providers Business Lens. This resolution is applied utilizing AWS Safety finest follow steerage utilizing Safety Pillar – AWS Nicely-Architected Framework. For AWS safety and compliance finest practices, seek advice from Finest Practices for Safety, Id, & Compliance.

Reworking doc processing with Amazon Bedrock: Pattern use instances

This part demonstrates how Onity makes use of Amazon Bedrock to automate the extraction of vital info from advanced mortgage servicing paperwork.

Deed of belief knowledge extraction

A deed of belief is a vital authorized doc that creates a safety curiosity in actual property. These paperwork are usually verbose, containing a number of pages of authorized textual content with vital info together with notarization particulars, authorized stamps, property descriptions, and rider attachments. The clever extraction resolution has diminished knowledge extraction prices by 50% whereas enhancing total accuracy by 20% in comparison with the earlier OCR and AI/ML resolution.

Notarization info extraction

The next is a pattern of a notarized doc that mixes printed and handwritten textual content and a notary seal. The doc picture is handed to the applying with a immediate to extract the next info: state, county, notary date, notary expiry date, presence of notary seal, individual signed earlier than notary, and notary public identify. The immediate additionally instructs that if a subject is manually crossed out or modified, the manually written or modified textual content must be used for that subject within the output.

Instance output:

{
    "state": "Indiana",
    "county": "Monroe",
    "notary_date": "8/13/2024",
    "notary_expiry_date": "8/24/25",
    "notary_seal": "Current",
    "person_signed": "[Redacted]",
    "notary_public": "[Redacted]"
}

Extract rider info

The next picture is of a rider that features textual content and a collection of verify containers (chosen and unselected). The doc picture is handed to the applying with a immediate to extract each checked riders and different riders listed on the doc in a offered JSON format.

Instance output:

{
"riders_checked": [],
"Others_listed": ["Manufactured Home Rider", "Manufactured Home Affidavit of Affixation"]
}

Automation of the guidelines assessment of residence appraisal paperwork

Residence appraisal stories include detailed property comparisons and valuations that require cautious assessment of a number of knowledge factors, together with room counts, sq. footage, and property options. Historically, this assessment course of required guide verification and cross-referencing, making it time-consuming and vulnerable to errors. The automated resolution now validates property comparisons and identifies potential discrepancies, considerably lowering assessment occasions whereas enhancing accuracy by 65% over the guide course of.

The next instance exhibits a doc in a grid format with rows and columns of data. The doc picture is handed to the applying with a immediate to confirm if the room counts are similar throughout the topic and comparables within the appraisal report and if sq. footages are inside a specified proportion of the topic property’s sq. footage. The immediate additionally requests a proof of the evaluation outcomes. The applying then extracts the required info and offers detailed justification for its findings.

Instance output:

{
    "Consequence": "Sure",
    "Rationalization": "Each situations are met. Room counts match at 4-2-2.0 (total-bedrooms-baths) throughout all properties. Topic property is 884 sq ft, and all comparable (884 sq ft, 884 sq ft, and 1000 sq ft) fall inside 15% variance vary (751.4-1016.6 sq ft). Comparable #3 at 1000 sq ft is inside acceptable 15% vary."
}

Automated credit score report evaluation

Credit score stories are important paperwork in mortgage servicing that include vital borrower info from a number of credit score bureaus. These stories arrive in various codecs with scattered info, making guide knowledge extraction time-consuming and error-prone. The answer robotically extracts and standardizes credit score scores and scoring fashions throughout completely different report codecs, attaining roughly 85% accuracy.

The next picture exhibits a credit score report that mixes rows and columns with quantity and textual content values. The doc picture is handed to the applying utilizing a immediate instructing it to extract the required info.

Instance output:

 {
    "EFX": {
        "Rating": 683,
        "ScoreModel": "Equifax Beacon 5.0"
    },
    "XPN": {
        "Rating": 688,
        "ScoreModel": "Experian Honest Isaac V2"
    },
    "TRU": {
        "Rating": 691,
        "ScoreModel": "FICO Threat Rating Traditional 04"
    }
}

Conclusion

Onity’s implementation of clever doc processing, powered by AWS generative AI providers, demonstrates how organizations can rework advanced doc dealing with challenges into strategic benefits. By utilizing the generative AI capabilities of Amazon Bedrock, Onity achieved a outstanding 50% discount in doc extraction prices whereas enhancing total accuracy by 20% in comparison with their earlier OCR and AI/ML resolution. The influence was much more dramatic in particular use instances—their credit score report processing achieved accuracy charges of as much as 85%—demonstrating the answer’s distinctive functionality in dealing with advanced, multiformat paperwork.

The versatile FM choice offered by Amazon Bedrock permits organizations to decide on and evolve their AI capabilities over time, serving to to strike the optimum stability between efficiency, accuracy, and price for every particular use case. The answer’s skill to deal with advanced paperwork, together with verbose authorized paperwork, handwritten textual content, and notarized supplies, showcases the transformative potential of recent AI applied sciences in monetary providers. Past the fast advantages of value financial savings and improved accuracy, this implementation offers a blueprint for organizations looking for to modernize their doc processing operations whereas sustaining the agility to adapt to evolving enterprise wants. The success of this resolution proves that considerate utility of AWS AI/ML and generative AI providers can ship tangible enterprise outcomes whereas positioning organizations for continued innovation in doc processing capabilities.

If in case you have related doc processing challenges, we advocate beginning with Amazon Textract to guage if its core OCR and knowledge extraction capabilities meet your wants. For extra advanced use instances requiring superior contextual understanding and visible evaluation, use Amazon Bedrock textual content and imaginative and prescient basis fashions, resembling Amazon Nova Lite, Nova Professional, Anthropic’s Claude Sonnet, and Anthropic’s Claude. Utilizing an Amazon Bedrock mannequin playground, you may rapidly experiment with these multimodal fashions after which evaluate the perfect basis fashions throughout completely different metrics resembling accuracy, robustness, and price utilizing Amazon Bedrock mannequin analysis. By means of this course of, you may make knowledgeable selections about which mannequin offers the perfect stability of efficiency and cost-effectiveness to your particular use case.

In regards to the writer

Ramesh Eega is a World Accounts Options Architect based mostly out of Atlanta, GA. He’s obsessed with serving to clients all through their cloud journey.