Unlock the Power of Handwriting Recognition to Optimize Your Business Processes
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While OCR technology has evolved substantially, it still struggles with handwriting recognition. However, modern machine learning offers new tools to solve long-standing challenges. New software solutions can now optimize complex business processes by turning even hand-filled paper documents into business-ready data automatically. Learn how leading insurance, financial services, and healthcare companies are leveraging new technology to automate processing of their paper-to-digital operations (including handwritten forms). You’ll hear how New York Life and five of the top 10 U.S. insurers have gained efficiencies and enabled critical business improvements with a 99.9% accuracy rate.
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Unlock the Power of Handwriting Recognition to Optimize Your Business Processes
- 1. Unlock the Power of Handwriting Recognition to Optimize Your Business Processes
- 2. Agenda Problems We Solve together Captricity Overview The State of Capture Within Enterprise Customer Use Cases
- 3. Agenda Integrations and Business Process Management Product Demo How the Technology Works Process Analytics Discussion
- 4. Problems We Solve Security of Extracted Content PII, Health, Financial Shred Handwriting on Structured, Semi- or Unstructured Content Accuracy Needed With OCR Extraction >95%
- 5. GovernmentHealth Insurance & Financial Services 12 of the Top 20 U.S. Insurers Non-Profit Customers
- 6. The State of Capture in Enterprise
- 7. The majority of critical business processes still rely on paper… It is still common practice to have people type HANDWRITTEN information from paper forms into downstream systems. Manual data entry processes are error-prone, time consuming, and expensive. Analytics teams are unable to access the most valuable legacy customer data from documents. Problems Organizations Face 01 02 03 04
- 8. Manual Data Entry • Prone to human error • Requires multiple reviews to avoid rework • Slow responses contribute to poor service • Expensive to train and hard to scale • Only works during regular business hours OCR • Requires >95% accuracy • Fails to effectively recognize handwriting • Expensive, license-based model (hard to scale) • Maintenance and updates require IT resources • Lacks end-to-end business/workflow logic The Challenges With Traditional Solutions
- 9. Captiva/Datacap/Kofax • Typically on premise • Complex deployment and configuration • Requires a team for Q/A due to 70-75% accuracy • High annual support costs • Costly and complex back-end integrations Captricity & Zia • Cloud based with high security standards • In production in weeks, not months • 99.5%+ accuracy on structured, semi- structured, and unstructured content • Straight-thru processing • Simple pricing model • Easy integrations How Do Our Solutions Compare? They Don’t. USE TO INDEX YOUR DOCUMENTS USE TO PROCESS YOUR DOCUMENTS
- 10. Use Cases: Operations and Analytics • Contract change requests • Collateral forms • Withdrawal forms • Death certificates • Applications/Enrollment forms (new business) • Change forms • Claims forms • Electronic payment authorizations Analytics use cases involve using Captricity on archived/historic documents to unlock missing or unavailable data. Operational use cases involve using Captricity day-to-day, eliminating manual entry. Familiar forms and workflows:
- 11. Value Delivered Same-day turnaround time; time-to-value <30 days Cost savings of 50–70%+; return on investment <3–8 months Enterprise-grade security; 100% HIPAA compliant Infinite, elastic platform scalability Significant data accuracy improvement (99.5%+, even on handwriting)
- 12. The True Value of Captricity Processing Work With Traditional Capture and Indexing Processing Work After Captricity Case Creation, Data Entry, & Clerical 38% Low Value 42.5% Moderate Value 19.5% High Value Utilization Analysis & Decision Making Information Compilation, Inventory, & Performance Management Analysis & Decision Making Information Compilation, Inventory, & Performance Management Case Creation, Data Entry, & Clerical 73% High Value Utilization 10% Low Value 17% Moderate Value
- 14. CHALLENGE: Digitize large amounts of life insurance application forms during peak business season when receiving thousands per month through scans and emailed PDFs. Processing teams are bogged down processing simplistic applications that don’t really need their review. SOLUTION: Provide straight-thru processing of applications allowing them to skip the queue and the processors to focus on more high-value tasks. Operational Efficiency: Streamlining the Onboarding Process
- 16. CHALLENGE: No easy access to cause-of-death data from death certificates which are submitted along with death claims but typically filed away without further analysis. (100s of potential templates to identify, sort, and capture) SOLUTION: Enabled big-data analytics for underwriting innovation to, specifically, improve fraud detection capabilities leveraging patterns found in historical customer data. Analytics Enablement: Extracting Data from 1M+ Death Claims
- 17. Captricity Provided Access to 20M+ New Data Points
- 18. How it Works
- 20. The Digitization Process 3.) Format & Deliver2.) Validate, Verify & Enrich1.) Capture, Sort & Shred
- 21. Shreddr™ Technology: Secure by Design
- 22. Enhance Data
- 23. Discover Data Remediation Opportunities: Addresses Example: Incorrect: 28263 HH Williams Rd, Angie, LA 70436 Correct: 28263 H H Williams Rd, Angie, LA 70426-1823 Address verification and correction via web API Able to improve: • Unstandardized addresses • Zip codes • Match street, city, and state data • Verify entire address is correct Key Insight: 41% of addresses were improvable, 12% were invalid Handwritten Address Data: ✘
- 24. Discover Data Quality Issues: Phone Phone Number Data: Key Insight: 91% of phone numbers listed above “home phone number” were not landlines. Phone number verification via web phone validation API Able to verify: • Mobile vs. landline • Active vs. inactive • Registered name • Carrier ✘
- 25. Legacy Systems and Integrations
- 26. What is Business Process Automation? Process model to orchestrate on-site patient visit designed with BPMN editor and powered by BPM engine Data Enrichment – Link input data to enrichment/validation actions
- 27. Where to from here….next steps.
- 28. A Model for Process Optimization: Process Analytics Identify Workflow Collect Inputs Run POC Analyze Data Customer selects a workflow to use as a starting point to build a clear business case around Customer provides batch of sample forms (1K+) Both parties determine critical data fields Discover basic business rules Sample forms run through Captricity’s crowd-guided digitization platform Captricity’s data science team performs analysis of sample data • Form design • Meta data analysis • Errored Submissions • Data validation • Data enrichment • Fraud detection • Intelligent delivery Step-by-Step Project: 1–2 Weeks
- 29. Questions?
Notas do Editor
- The problem Captricity found many organizations face are: The majority of critical business processes still rely on paper.. Common practice is (still) to have people type handwritten information from paper forms into downstream systems. Manual data entry process are error prone, time consuming and expensive. Analytics teams unable to access the most valuable legacy customer data from documents. JDF – in the second bullet can we emphasize HANDWRITEN as well as TYPED information in paper forms? MB - Addressed
- NIGO Problems Complex workflows What we hear in the market We give you great (and getting greater) data but it get ‘stuck” Need to be “automation ready”
- NIGO Problems Complex workflows What we hear in the market We give you great (and getting greater) data but it get ‘stuck” Need to be “automation ready”
- Compare to IBM, EMC, Kofax Up to 5k crowd sourced workers working for us. “Global elastic pool of workers” Same-day turnaround for any volume of documents As much as 50% less expensive than existing solutions 100% HIPAA compliant Infinite scalability for all document types
- Policy Change Forms
- SLF example. E-Apps aren’t always e-apps
- Here is how your data flows through Captricity. 1. Once document images (i.e., scans or photos) have been uploaded to Captricity, document images are captured and sorted via Captricity’s Intelligent Document ID capabilities and then the data is put through Shreddr™ to isolate individual pieces of information, or data fields (see: HIPAA compliance). We call this process “shredding” and the resulting small data pieces are referred to as “shreds”. 2. Next, shreds are sent through Captricity’s machine learning digitization engines to generate predicted values for each piece of data. To ensure 99.9 percent accuracy, the machine predicted values are then sent to crowdsourced workers to VALIDATE shreds have been digitized correctly. Once validated, the data can then be VERIFIED using the customer’s existing, internal systems of record (policy admin systems, claims management systems, CRMs, beneficiary databases, etc) or via 3rd party web services such as Lexis Nexis (address verification), Google (confirming obituary for life claims), etc. Data can also be “enriched” via 3rd party web services such as Lexis Nexis (latest personal contact information, for example) to ensure your customer data is business-ready for all lines of business in your organization. 3. Finally, business-ready data is formatted and delivered back to the customer - we’ve worked with nearly all common industry formats from basic CSV to ACORD.
- Copy: Captricity can ensure a secure environment for your data when submitting shreds to our crowd-sourced workers Each shred of data is pulled from our database via secure API commands and delivered to our crowdsource platform to be validated. The worker will be able to view a non-contextual image of the shred on their screen (delivered via HTML page), but no data will actually be stored on their computer or a server outside of the U.S. The data validation process is designed so that each worker is assigned to verify a given class or type of data, such as last name, from many forms rather than a group of complete forms. This ensures that every worker sees only one piece of data, or shred, from a single form. The worker receives no additional information along with the shred image that would correlate the image to a user, image file, organization or API token. Once the worker has completed their work on a single shred of data, the API terminates the connection and all data associated with that shred becomes permanently unavailable to the worker.
- Address verification & Correction via “Smarty Streets” “SmartyStreets” able to remediate 41% of incoming addresses that were incorrect
- Need to know whether phone numbers are landline vs. mobile Insight: 91.06% of phone numbers listed above “home” were not landlines Great way to stand-up a mobile (SMS) customer engagement program using data you already have or are collecting
- Should add the collect data section, maybe until Build Requirements, to talk about collecting not just forms to run the POC but also the many types of business rules (system, process human, etc.). Also add business rules analysis to the Analyze Data section. Great case for Hoover / micro services to grab and use business rules For Robotics….”we want to deliver automation ready data so that you can train the robots to hit the 80/20 rule. So we send you the enriched validated data, but also the meta data associated so that you can better train the bot” Should we include the type of training data into the “Intelligent Delivery” side of our analysis? (JDF – Can talk about this in terms of RPA and its use of the meta datawe provide as a part of Automation Ready Data to manage rules for intelligent delivery)