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features
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support

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Description

FraudShare is an innovative platform created by LIMRA aimed at tackling account takeover fraud within the financial services sector. It grants users real-time access to data on incidents and threat indicators linked to ATO attacks, allowing businesses to take proactive measures against fraudulent schemes. Users receive prompt email notifications and have the option to access data through export capabilities or an API, which streamlines the process of identifying and averting similar attacks. The platform's correlation analysis features enable organizations to identify and connect related incidents, revealing additional threat indicators that are essential for thorough investigations. Additionally, FraudShare provides valuable industry statistics and trending insights derived from verified fraud cases, helping companies grasp the dynamics and repercussions of ATO fraud. This wealth of information empowers organizations to make strategic choices to bolster their fraud prevention efforts and stay ahead of evolving threats in the financial landscape. Ultimately, FraudShare serves as an essential tool for enhancing collective defenses against increasingly sophisticated fraud tactics.

Description

Benford's law serves as a tool for uncovering patterns indicative of improper disbursements. It involves examining audit trail reports from QuickBooks or other bookkeeping software to pinpoint unusual activities like voids and deletions. Additionally, it entails identifying multiple payments made for identical amounts on the same day. A thorough review of payroll runs is conducted to detect any payments exceeding the established salary or hourly rates. Payments made on non-business days are also scrutinized. Statistical calculations help in identifying outliers that may suggest fraudulent activity, and duplicate payments are tested for validation. Vendor files in accounts payable are analyzed for names that may be suspiciously similar, and investigations are conducted to uncover fictitious vendors. Comparisons of vendor and payroll addresses are evaluated using Z-Scores and relative size factor tests. While data monitoring and surprise audits have shown to significantly reduce fraud losses, only 37% of organizations implement these critical controls. For businesses employing fewer than 100 individuals, the average loss due to fraud is estimated at $200,000, highlighting that smaller enterprises often lack the necessary resources to effectively detect and address fraudulent activities. Consequently, it is essential for small businesses to adopt more robust fraud detection mechanisms to safeguard their financial integrity.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$1,400 one-time payment
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

LIMRA

Founded

1916

Country

United States

Website

www.limra.com/en/solutions-and-services/regulatory-and-compliance/fraudshare/

Vendor Details

Company Name

MedCXO

Country

United States

Website

medcxo.com/fraud/

Product Features

Fraud Detection

Access Security Management No 
Check Fraud Monitoring No 
Custom Fraud Parameters No 
For Banking No 
For Crypto No 
For Insurance Industry No 
For eCommerce No 
Internal Fraud Monitoring No 
Investigator Notes No 
Pattern Recognition No 
Transaction Approval No 

Product Features

Fraud Detection

Access Security Management No 
Check Fraud Monitoring No 
Custom Fraud Parameters No 
For Banking No 
For Crypto No 
For Insurance Industry No 
For eCommerce No 
Internal Fraud Monitoring No 
Investigator Notes No 
Pattern Recognition No 
Transaction Approval No 

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