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© 2023, Inawisdom Ltd.
INAWISDOM INTRODUCTION
INTELLIGENT DOCUMENT
PROCESSING
FEBRUARY 2023
Intelligent Document
Processing
© 2023 Cognizant | Private
November 2023
2
Cognizant’s UK&I specialist AWS Data & AI Team
INAWISDOM
Found in 2016, an AWS Partner since 2017 and Premier
Partner since 2019. Inawisdom was acquired by Cognizant in
2020 and is part of Cognizant’s UK&I Consulting
Inawisdom lives and breath AWS including holding over 180
AWS certifications and accreditations. Inawisdom maintain a
close relationship with the AWS team, supporting and staying
up-to-date with all the latest developments.
Inawisdom has been awarded in the following
areas:
► ML Partner of the Year 2020
► Differentiation Partner of the Year 2019
► Global Launch Partner – CCI
► Launch Partner – AWS UAE Region
Inawisdom holds 9 competencies and service designations, reflecting business-
wide expertise in key areas:
Our Qualifications
All of our consultants hold at least 1
AWS certification. Including some
consultants with all certifications
Our CTO has been ranked #1 AWS
Ambassador in EMEA in 2021 and
2022
Your Data, AI and Machine Learning partner
WHY COGNIZANT & INAWISDOM
We offer a rapid,
proven path to
Machine Learning
excellence
We help customers in
a broad range of
industries achieve
their ML goals
We are recognised,
all-in AWS experts,
focused on customer
success
We offer full-stack
services, including AI /
ML, Data & Analytics,
BI and MLOps
Inawisdom’s full-stack capability
OUR SERVICES
Business
Differentiation / Value
Data Driven
Business Decisions
Cloud Transformation
Adoption and Scale
Digital
Enablement
AI and Machine Learning
Data & Analytics
Data Foundations
Cloud Infrastructure
Landing Zone, Control Tower, migration
Discover. Deliver. Productionise. Scale.
ACCELERATE AI/ML ADOPTION ON AWS
D
e
p
l
o
y
Scaled AI Productionise
Operationalise
Discovery
D
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f
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e
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P
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V
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b
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a
b
l
e
Data Science
AI/ML
Data
Engineering
& Platform
DevOps &
MLOps
Cloud/Data
Architecture
Think big, start small, prove value
THE INAWISDOM APPROACH

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Inawisdom IDP

  • 1. © 2023, Inawisdom Ltd. INAWISDOM INTRODUCTION INTELLIGENT DOCUMENT PROCESSING FEBRUARY 2023 Intelligent Document Processing © 2023 Cognizant | Private November 2023
  • 2. 2 Cognizant’s UK&I specialist AWS Data & AI Team INAWISDOM Found in 2016, an AWS Partner since 2017 and Premier Partner since 2019. Inawisdom was acquired by Cognizant in 2020 and is part of Cognizant’s UK&I Consulting Inawisdom lives and breath AWS including holding over 180 AWS certifications and accreditations. Inawisdom maintain a close relationship with the AWS team, supporting and staying up-to-date with all the latest developments. Inawisdom has been awarded in the following areas: ► ML Partner of the Year 2020 ► Differentiation Partner of the Year 2019 ► Global Launch Partner – CCI ► Launch Partner – AWS UAE Region Inawisdom holds 9 competencies and service designations, reflecting business- wide expertise in key areas: Our Qualifications All of our consultants hold at least 1 AWS certification. Including some consultants with all certifications Our CTO has been ranked #1 AWS Ambassador in EMEA in 2021 and 2022
  • 3. Your Data, AI and Machine Learning partner WHY COGNIZANT & INAWISDOM We offer a rapid, proven path to Machine Learning excellence We help customers in a broad range of industries achieve their ML goals We are recognised, all-in AWS experts, focused on customer success We offer full-stack services, including AI / ML, Data & Analytics, BI and MLOps
  • 4. Inawisdom’s full-stack capability OUR SERVICES Business Differentiation / Value Data Driven Business Decisions Cloud Transformation Adoption and Scale Digital Enablement AI and Machine Learning Data & Analytics Data Foundations Cloud Infrastructure Landing Zone, Control Tower, migration
  • 5. Discover. Deliver. Productionise. Scale. ACCELERATE AI/ML ADOPTION ON AWS D e p l o y Scaled AI Productionise Operationalise Discovery D i f f e r e n t i a t e I n n o v a t e P r o v e V a l u e B u s i n e s s C a s e D e p l o y E m b e d A u t o m a t e E n a b l e Data Science AI/ML Data Engineering & Platform DevOps & MLOps Cloud/Data Architecture
  • 6. Think big, start small, prove value THE INAWISDOM APPROACH
  • 7. Proprietary platform that enables secure and rapid data capture and analysis RAMP – INAWISDOM’S CUTTING-EDGE TECHNOLOGY Data Ingestion Service Layer Anomaly Detection Entity Extraction Natural Language Processing Name matching Exploitation Layer Prediction Classification Enterprise Security Streaming Batch Monitoring High Availability Automated Deployment Operational System Management Dashboard Standard BI Discovery Dashboard Human Structured e.g. SQL Unstructured e.g. documents Devices e.g. IoT Public open datasets Public APIs e.g. Twitter
  • 8. IMPROVING DATA EXTRACTION WITH INTELLIGENT DOCUMENT PROCESSING
  • 9. “Intelligent Document Processing provides decision support tools that make data extraction faster, less error-prone and less subjective, by reducing the amount of human intervention required. With the ability to automatically test results, the entire end-to-end process can be scaled up faster and in a more cost-effective way.” WHY SHOULD MY BUSINESS USE IDP?
  • 10. IDP – INAWISDOM APPROACH The Human in the Loop Human reviewers may sometimes want to override the extracted / interpreted data for perfectly valid business reasons — for example, the source document might be incorrect or out-of-date. Or they may simply need to correct an extraction error. Our process makes it easy for humans to review the extracted data as needed. This also helps improve the accuracy of the models over time — a key part of MLOps. Once the accuracy is sufficiently high, the review process will evolve to the point of “sampling”, where only a subset of the extracted data is checked to maintain ongoing business confidence. Having a robust MLOps pipeline in place allows this process to be highly automated.
  • 11. Inawisdom approach to IDP requires a range of capabilities IDP PLATFORM
  • 12. KEY BENEFITS Reviewing multiple lengthy documents, extracting relevant data and storing it in the right systems is a time-consuming process and prone to mistakes. With Intelligent Document Processing, this can be done much more efficiently. Not only does this result in cost-savings, it also speeds up the entire value chain — making data available for further processing or business analytics much earlier on. Drive Efficiency & Access Insights Faster Increase Accuracy & Improve Processes A second pair of eyes should ideally review the human decisions made when encoding the data, as mistakes can be expensive. IDP provides the second pair of eyes but flips the process around so that the encoder becomes the reviewer. Over time, the results of the IDP can be used to further increase efficiency. For example, standardising input across documents will increase the model accuracy, reducing human effort. The IDP process shines a light on data variability across sources and highlights outliers —insights that can improve the data collection process.
  • 13. INAWISDOM EXPERIENCE IN BANKING, FINANCIAL SERVICES & INSURANCE IDP Client Case Focus
  • 14. Acceleration in AI/ML adoption in the Insurance and Financial Services sector AI/ML IN INSURANCE AND FINANCIAL SERVICES Document Processing Personalised User Experience Chat Bots & Assistants Next Best Action Call Centre Optimisation Fraud Detection Risk Management Credit Scoring Customer Lifetime Value Smart Claim Management Churn Prediction Price Optimisation Regulation & Compliance Sentiment Analysis Debt Management & Prevention
  • 15. 15 CASE STUDY 1 The Customer: IDP - From Document-led to a Data-driven Market-place The Sector: Financial Services The Solution: The Result: The Requirement: Ø Established an automated, scalable underwriting process to improve underwriters’ day to day operations and drive business growth Ø Implemented E2E automated workflow solution that accepts documents via email or uploads, embedding a series of AI and gen AI ML models to extract key data points (pricing/policies) from broker documents held in multiple types (pdf, email, xls). Ø Trained and Deployed a series of fine-tuned ML models including Gen AI LLMs targeted at domain specific documents to improve extraction accuracy. Ø Use Case feasibility proven in 4 weeks (PoV), Production grade solution in 5 months Ø Average time to process c. 3 minutes, 540 times faster than previous manual process Ø Scale: 200,000+ documents processed per year Ø Improved quality for risk writing (90%+ extraction accuracy on key data points) Ø Productivity gains leading to multi-million operational cost reduction on yearly basis International insurance and reinsurance group Revolutionise the approach for underwriting risk in specialty insurance – leveraging AI & Gen AI The process from submission to quotation was largely manual –complexity of domain specific nomenclature and a huge volume of unstructured data was making automated data extraction difficult & slow
  • 16. 16 CASE STUDY 2 The Customer: Gen AI to support the Intake Process and CX The Result: The Solution: The Requirement: Ø Performed EDA on data received (25 000 job requests and set ot PPT documents) Ø Applied Generative AI models with request text and synthetic attachments, in particular to extract accurately key instructions embedded in the PPT and summarizing list of instructions. . Ø Trained a ML regression model to predict effort estimate for the job request Ø Discovery work completed - Data Readiness completed – presented to CEO. Ø Target State solution & path to Production defined Ø Next steps: Path to Production for this Use Case and define Gen AI roadmap for 2024-2025 (beyond this Use Case) The Sector: UC1: Client receives c. 20,000 job requests per year, mostly related to updating PPT documents. The Intake process (from receiving the request and assessing the complexity/effort of the task and allocating to the right resource) is manual. Can Generative AI be leveraged to automate the InTake process and eventually improve the Submission experience Business Services BPO Service provider to Investment Banking and Legal
  • 17. 17 CASE STUDY 3 The Customer: Audio Transcription and text summarization powered by AI/ML The Result: The Solution: The Requirement: Ø Implement and deploy a secure E2E cloud-based AWS production grade solution to support this Transcription Use Case Ø Solution include Transcription, Diarization and Dictation. Ø Bespoke (fine-tuned) ML models to Transcribe audio files including speaker detection Ø Gen AI model to deliver Text Summarization Ø 92.5 % accuracy (Word Error Rate, Diarization Error Rate) Ø 6 mins average Processing time (audio recording from 30 min up to 4 hrs) Ø $50 to $200 cost savings per hour of audio processed Ø Customer Experience improved with improved SLA. Revenue growth and margin uplift due to productivity gains The Sector: Automate the process to transcribe audio files at scale and with high accuracy to reduce process time and improve SLA. Include Text summarization features as new servie to end customers Business Services BPO Service provider to Investment Banking and Legal
  • 18. 18 CASE STUDY 4 The Customer: Emails triage – Accelerating lead processing in FSI The Result: The Solution: The Requirement: Ø Created a scalable document processing solution to extract key data from emails sent by brokers Ø Fine-tuned Large Language Models (LLMs / Gen AI) with Transfer Learning on AWS to extract and interpret industry-specific terminology Ø Developed a user interface to allow the underwriting team to review and correct the extracted data points as needed Ø Accuracy rates of 80-90%, Average processing time is 17 seconds Ø Auditable history of submissions including content, edits, agents, contract values, priorities and processing times Ø Provide full visibility to management on leads coming in helping better adjusting workforce allocation (Supply vs Demand) and target missed opportunities The Sector: Can IDP / AI-ML be used to extract email content from leads sent by broker and drive automation in the triage process Currently process is heavily manual and does not allow management to get comprehensive view on leads coming in and any missed opportunities. Financial Services Specialty insurer underwriting personal & commercial risk
  • 19. 19 CASE STUDY 5 The Customer: Policies admin automation powered by AI/ML in FSI The Result: The Solution: The Requirement: Ø Conduct remediation activities to improve existing IDP solution, implementing best practices for monitoring, scalability and integration Ø Develop new classification and data extraction models to handle a variety of structured and unstructured Retail Annuities documents, including free-form customer letters and application forms Ø Produce synthetic data using Generative AI to support training and testing of models, in place of sensitive customer data Ø Provide ongoing support and management of the solution Ø Solution scalability and reliability improved, Faster data extraction and improved accuracy, leading to a reduction in processing costs Ø Several new use cases being explored to expand current solution The Sector: Improve and expand the existing IDP (Intelligent Document Processing) solution, to enable key use cases including accelerated processing of insurance documents Financial Services Leading provider of asset management & life insurance
  • 20. 20 CASE STUDY 6 The Customer: Automating and improving invoicing process with AI/ML The Result: The Solution: The Requirement: Ø ML solution to (1) analyse documents, automate the extraction of business rules from OCG documents and (2) trained a classification model to detect potential errors in line items invoices and categorize them based on the primary reason for rejection. Ø Leveraged Generative AI (GPT-3) to generate synthetic data for improved training and testing Ø Built a robust QA process and audit trail to ensure consistency and transparency Ø Document extraction Accuracy rates of 75-97% across both use cases Ø 20% reduction in processing times Ø Yearly labour cost-savings of approximately $1.5m. Significant Revenue growth expected The Sector: The client needed a way to quickly analyse customer documents – namely Outside Counsel Guidelines and invoices – to extract key information, highlight line items for review and reduce the likelihood that invoice submissions were rejected (each invoice embedding thousands of line items, each of them to quality check. Business Services BPO Service provider to Investment Banking and Legal
  • 21. 21 CASE STUDY 7 The Customer: Audio processing - Intelligent Customer Call Analysis The Sector: Utilities The Result: Ø Improved Accuracy – better ability to identify specific issues and their root causes through full capture of customer call Ø Enhanced Customer Service and Improved Operations - enabled more accurate scheduling of on-site engineers to solve problems first time Ø Innovative Call Analysis - further business insight opportunities identified through intelligent ML modelling The Solution: The Requirement: Intelligent Customer Call Analysis to get more from the call recording information, helping to diagnose their customers' requirements more efficiently Ø Used the Discovery-as-a-Service method to rapidly progress from problem to solution Ø Deployed AWS's Contact Centre Innovation –creating a post-call analytics solution using Amazon Transcribe to detail thousands of customers call recordings from voice to text Ø Applied advanced ML models including deep learning/LLM models to identify root causes of caller enquiries A leading Utilities water company
  • 23. IDP combines the capabilities provided by AWS Services into a business outcome IDP SOLUTION ARCHITECTURE
  • 24. INGESTION AND PREPROCESSING AWS Lambda AWS Step Functions Amazon S3 Amazon EventBridge
  • 25. Capturing Meta Information and performing file conversion INGESTION AWS Lambda PACK FILENAME NAME VALUE 1 Slip 1 S3 Location S3://XXXXX 1 Slip 1 Date Time Captured XXXX/XX/XX XX:XX 1 Slip 1 Business Unit XXXXXX 1 Slip 1 Status PENDING 1 Slip 1 Email Address XXX@xx.com 1 Slip 1 Email Received XXXX/XX/XX XX:XX 1 Slip 1 Type Of Doc RENEWAL_SLIP Amazon DynamoDB Amazon S3 AWS Step Functions Amazon EventBridge
  • 27. Using custom classification to determine how to process a pack and the documents within it EXTRACTION AND CLASSIFICATION AWS Step Functions Amazon S3 Amazon Textract Amazon S3 Amazon Comprehend (Classification – Multi Label) Amazon S3 Amazon S3 rawText.txt Original PDF AWS Lambda AWS Lambda combinedText.txt Amazon DynamoDB output.tar.gz containing output.json Line of business + doc type I.e Slip Line per doc format allows for whole pack is processed in one comprehend job 10 examples needed per label/class Attention to metrics is required 1 2 3 4
  • 28. Advanced OCR capabilities AMAZON TEXTRACT Analyze Document: • Pretrained on millions on documents Analyze Expense: • Focused on invoices and receipts. Analyze ID: • Focused on identity documents such as U.S. passports and driver’s licenses Analyze Lending: • Focused on mortgages
  • 29. Multiple Extraction Types AMAZON TEXTRACT Queries Forms Table
  • 30. Analysis Document AMAZON TEXTRACT Output: • Tables as CSV • Raw as text • Key Values in Tables • JSON Output that is very verbose JSON Output needs post processing: • Document is split per page even when table spans more than 1 • Headers and Footer get in the way when tables are split over multiple pages • Output contains Bounding Boxes + X/Y locations
  • 31. Problem 1: Formatting of Data Points AMAZON TEXTRACT Query Form Queries Forms
  • 32. Solution 1: Formatting of Data Points AMAZON TEXTRACT PACK FILENAME NAME VALUE 1 Invoice 1 S3 Location S3://XXXXX 1 Invoice 1 Date Time Captured XXXX/XX/XX XX:XX 1 Invoice 1 Business Unit XXXXXX 1 Invoice 1 Status PENDING 1 Invoice 1 Email Address billing@amazon.com 1 Invoice 1 Email Received XXXX/XX/XXX XX:XX 1 Invoice 1 Type of Doc AWS INVOICE The classification is used to know what queries to ask and how understand the results i.e., we could infer that Amazon use MM-DD-YYYY for dates
  • 33. Problem 2: Context loss in Forms AMAZON TEXTRACT • Hugging Face and Amazon are partners • Multiple Amazon SageMaker GroundTruth jobs used for labelling and a Lambda used collated/merge the manifests • Model trained on labelled data is done in Amazon SageMaker and hosted via an endpoint • Hugging Face have approved Docker Images that do all the boilerplate work for you Textract Form Solution 2: LayoutLM via Hugging Face
  • 35. Textract Raw Name Entity Recognition AMAZON COMPREHEND Seattle WA 98109-5210 US Billing Period: Aug 1 . Aug 31, 2010 Amazon Simple Notification Service $ 0.00 Amazon Elastic Compute Cloud $ 85.13 Amazon Simple Queue Service
  • 36. Problem 3: Too generic! AMAZON COMPREHEND
  • 37. Solution 3: Customize Pre-Trained Large Language Models with Transfer Learning CUSTOM MACHINE LEARNING MODELS Wikipedia Books News Other Sources … … … Source Docs Named Entities AWS Comprehend DATE PERSON ORGANIZATION OTHER …
  • 38. Solution 3: Customize Pre-Trained Large Language Models with Transfer Learning CUSTOM MACHINE LEARNING MODELS Wikipedia Books News Other Sources … … … Source Docs Named Entities AWS Comprehend … … Customized AWS Comprehend DATE PERSON ORGANIZATION OTHER … Wikipedia Books News Other Sources … Source Docs … … Wikipedia Books News Other Sources … Source Docs
  • 39. Solution 3: Customize Pre-Trained Large Language Models with Transfer Learning CUSTOM MACHINE LEARNING MODELS … … Wikipedia Books News Other Sources … Source Docs Named Entities AWS Comprehend … … Customized AWS Comprehend New Connections are learned DATE PERSON ORGANIZATION LAYER OTHER … Wikipedia Books News Other Sources … Source Docs
  • 40. Solution 3: Customize Pre-Trained Large Language Models with Transfer Learning CUSTOM MACHINE LEARNING MODELS … … Layer Customized AWS Comprehend LAYER Wikipedia Books News Other Sources … Source Docs
  • 42. Problem 4: Data Points that are the same thing but called different things POST PROCESSING
  • 43. Problem 5: More than one Data Point to consider POST PROCESSING
  • 44. Problem 6: Missing Data Points to consider POST PROCESSING
  • 45. Solution 4 + 5 + 6: Precedence stored in DynamoDB and processed in Lambda POST PROCESSING AWS Lambda Amazon DynamoDB Rule Set Rule Slip Renewal Date {} Slip Insured Name {} Invoice Invoice Id {} Invoice Total Amount {} Invoice Supplier {} Amazon DynamoDB
  • 46. Problem 7: Different values in different documents for the same thing ENRICHMENT Mrs Elizabeth Bloggs Ms E Bloggs Lizz Bloggs Mr & Mrs J Bloggs Joe Bloggs Joseph Bloggs Mr J Bloggs Policy Number : 3355443333-wdd-7 Miss E Jones Solution 7: Using Existing Data Amazon RDS
  • 48. Columba House, Adastral Park, Martlesham Heath Ipswich, Suffolk, IP5 3RE www.inawisdom.com @inawisdom Inawisdom info@Inawisdom.com +44 20 8133 8349 Thank you © 2023 Cognizant | Private Phil Basford Senior Director – Consulting / Inawisdom CTO Philip.basford@cognizant.com 23