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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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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)

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

Post Summary

  • Specific problematic use cases
  • Check fraud has evolved
  • Using independent data sources improves fraud predictions

Multivariate analysis addresses gaps in legacy, transactional analytics

As a payment professional, you know the nuances differentiating a check and an ACH or Zelle payment. When considering fraud detection and prevention on these payments, it makes sense to optimize an approach for each payment type.

Or does it?

Checks have a unique set of fraud scenarios which need to be considered. For example:

  1. There are several main check fraud categories:
    Counterfeits: These are fake checks and there are many ways to create them.
    Forgeries: Unauthorized or forged signatures occur on stolen or modified checks.
    Alteration: Changing the dollar amount or payee or other fields of an item.
    Kiting: Still used with limited “float”, fraudsters take different approaches now, often incorporating electronic transactions into the mix.
  2. Banks must factor the above categories with loss management. Are you the Bank of First Deposit (BoFD) or the paying bank? A fraud liability can rest on whether you have strong controls on the right workflow.
  3. Fraud return windows: Time zones and regulatory processing time-frames dictate where the risk of fraud loss falls.
  4. Unlike electronic payments, fields like payee, payor, date, etc. are printed or written on the check, whereas electronic payments are inherently “electronic.”

Because of the unique attributes of checks, there are many fraudulent use cases which are difficult to detect. Let’s look at how banks are trying to address these problems.

Transaction Analysis Evolution in Check

Check fraud detection started with the manual review of items which were out-sorted from reader sorters (remember reader sorters?). Then, basic analytics were developed to analyze MICR data (amounts, serial numbers, velocity) for transactional analysis. Does ASI-16 and ASI-19 ring a bell? Although it’s 20-30 year old technology, many banks still use “legacy analytics” within their enterprise fraud solutions.

Today, fraud detection and prevention is heading toward real-time. This has brought a new wave of vendors to the table. The legacy transaction analysis fraud vendors are trying to adapt, and while they do provide a baseline of protection, these solutions cannot stay ahead of fraudsters. This results in detection gaps.

These use case examples illustrate new potential gaps to close in detection:

  • Mobile RDC has revolutionized capture. This is now a new entry point for fraud.
  • EMV tightened up protections to credit card. Fraudsters returned to find weak points in legacy check protection systems.
  • Customer convenience and improved funds availability is both a blessing and a curse. Omnichannel capture/deposit of checks means that fraudsters can deposit money and have access by next morning.

To address these cases, many banks developed their own transactional analysis and even kicked off AI and machine learning initiatives. The Hewlett Packard Enterprise report shows strategically how AI and ML fit into the picture.

In fact, here’s how AI is being deployed:

In December of 2018, Citi announced plans to integrate Feedzai’s ML-driven transaction management monitoring system into its own proprietary services and platforms, with the aim of providing business customers with enhanced risk management for payment transactions.

Image Analysis: True Multivariate Analysis

So, with all these improvements going on in the industry, why are 20+ banks issuing RFPs and requesting information for modernized image analysis technologies?

  • Layman’s answer: The strength of image analysis resides in the ability to “see the attributes of an image” and compare that to previously cleared checks for match purposes. This can only be done via image processing software or by the human eye.
  • Technical answer: Multivariate analysis measures two or more independent variables (Xi) simultaneously to predict a value of a dependent variable (Y) for each subject. Images are ignored in basic transaction analytics and machine learning.

For example, many times the perpetrator is using a different check stock for a counterfeit, forgers use unauthorized signatures, and alterations can only be seen on the image. Transactional-only systems will analyze data fields and are unable to defects in the image. The image analysis tests (check stock validation, amount verification, automated signature verification, and check style validation) are the independent variables combined with the transactional data which drives up detection and reduces false positives. The potential loss savings is in the millions of dollars for many banks.

For more details on image analysis analyzers, review the Anywhere Fraud feature page.

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