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Check out our reservoir of information related to check recognition and healthcare payment technologies. We frequently update this section with the latest news, trends, and analysis of the banking and healthcare industries.

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May 20-21, 2019
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Platform Modernization

Both the financial and healthcare industries are undergoing modernization initiatives in check payments and remittance.  See how OrboGraph is using AI, self-learning and deep learning models to drive innovation in these industries to deliver workflow automation.

Platform Modernization

Modernizing payments in the banking and healthcare industries

AI, Self Learning & Deep Learning Technologies

Optimized AI and deep learning models for the automation of check processing and healthcare posting

Operationalizing AI & Self Learning in Checks

Revolutionizing check processing and fraud prevention for the banking industry

Delivering Healthcare Payment Electronification

Increased accuracy levels, decreased error rate for healthcare payments posting

Product Videos

See how each product/service module of OrboAnywhere and OrboAccess delivers value from our check and healthcare payment platforms

Corporate Video & Podcast

See and hear how OrboGraph incorporates AI, deep learning and self learning technologies into its product suite

Healthcare Payments

OrboAccess automates remittance and payment posting as well as enables full research and business intelligence analysis for RCM companies, clearinghouses, billers, and providers.

Access EOB Conversion

Delivers EOB/EOP electronification with information intelligence via AI and deep learning technologies

Access Correspondence Letters

Extracts posting data and tracks reimbursement progress via workflow management

Access Payment Reconciliation

Streamlines the reconciliation process of ERA, ACH, EOB and checks

Access Patient Payments

Automates patient payments for posting

Access Denial Intelligence

Spotlights trends in denials to reduce receivables via prevention

Healthcare Payments Automation Center

Scalable, reliable, flexible cloud-based hosted data center on Amazon Web Services (AWS)

Modernizing RCM with AI

An informative resource to assist RCM companies in understanding how to solve today's problems with the help of AI.

Check Processing

OrboAnywhere automates paper originated payments (i.e. checks, money orders, drafts) and remittances for balancing and posting while reducing risk and losses in the areas of check fraud, payment negotiability and compliance.

Anywhere Fraud

Transaction and image analysis for on-us and deposit fraud detection of counterfeits, forgeries, and alterations.

Anywhere Recognition

Divergent multi-engine CAR/LAR, ICR, OCR & AI check recognition for the Omnichannel

Anywhere Validate

Validate payment negotiability of paper originated items

Anywhere Payee

Match, read, and validate payees for risk and operational workflows

Anywhere Positive Pay

Payee name verification of business checks using issue files

Anywhere Compliance

Mitigate risk in check payments for OFAC, BSA/AML, UCC, Reg CC, and KYC

Restrictive Endorsement

Automatic validation of restrictive, mobile and non-restrictive endorsements

Traditional Products

Based on the Accura XV platform

Modernizing Omnichannel Check Fraud Detection

An informative blog series exploring payments fraud and image technologies used to fight financial crimes.

Modernizing Omnichannel Check Fraud Detection

An informative blog series exploring payments fraud and image technologies used to fight financial crimes.

Nice Actimize & OrboGraph Webinar

Integration of Machine Learning and Image Analysis is Vital

  • Checks remain a ripe target for fraudsters
  • So far, we're winning the battle
  • It will require collaboration between image analysis and machine learning to remain successful

OrboGraph recently co-hosted with Nice Actimize a dynamic webinar entitled Change is Inevitable - How Will You Respond? Along with OrboGraph's Joe Gregory, Allegra Angus and Nithin Mangalore of Nice Actimize discussed with attendees different aspects of check fraud trends, best practices for combating check fraud, including incorporating machine learning and image analysis.

Key findings and discussion points include:

  • According to the 2019 AFP Survey, 70% of attempted and/or actual payments frauds were perpetrated via check -- far ahead of second-in-line Wire Fraud, which stood at 45%
  • Fraud against bank deposits accounts amounted to $25.1B in 2018, up from $19.1B in 2016 according to ABA estimates
  • The nation's banks stopped $22.3B in fraudulent transaction (almost $9 out of 10$) according to the ABA
  • Why banks are deploying tools and collaborative measures available to identify check fraud attempts before they become liabilities to banks or their customers

Consensus: Check Fraud Attempts Will Increase

The responses from webinar attendees were also very interesting when they were asked whether they expected to see increases in fraud losses:

  • 21 of 32 respondents stated that they thought check fraud attempts would increase in 2020
  • 11 of 29 thought their organization would see increases
  • 10 of 29 thought would stay the same
  • 8 of 29 thought the would see loss declines

With AFP reporting that 70% of payment methods that were targets of attempted and/or actual payments fraud in 2018 and the ABA Deposit Account Survey noting that, in 2018, check fraud accounted for 47% ($1.3 billion) of industry deposit account losses, a rise in check fraud attempts and losses would have tremendous impact on banks.

Looking Forward

The bottom line as explicated by the webinar's participants and presenters seemed to be that check fraud in its myriad forms is a force to be reckoned with, and it's more important than ever for financial institutions to keep their tools up to date. Joe Gregory pointed to trends in the recent past that showed periods where legacy systems simply could not keep up, leading to spikes in successful fraud transactions.

Collaborative efforts via machine learning and image analysis appear to be the key to winning the fight against fraud. Image analysis represents the visual factor, while machine learning represents the behavioral component. When combined via collaborative systems, financial institutions have a potent defense against all.

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