Veryfi is addressing Fraud for CPG and FinTech verticals
Fraud detection is vital for CPG and FinTech verticals to protect financial assets, preserve customer trust, reduce operational costs, ensure compliance, prevent brand damage, and promote fair competition. Veryfi is investing in robust fraud detection processes, to enhance customers security posture and maintain a competitive advantage in the market.
CPG – Brands, Loyalty Marketing
FinTech – Billpay
Fraud can also make it more difficult for legitimate customers to claim and redeem rewards points, which can lead to dissatisfaction and a decline in engagement with the program.
According to a study by the Collinson Group, a global loyalty and benefits company, fraud accounts for an average of 15% of loyalty program costs. This includes costs associated with rewards points that are fraudulently claimed or redeemed, as well as costs associated with fraud prevention and detection.
The impact of fraud on brand loyalty programs can be significant;
Fraudsters create counterfeit invoices or manipulate payment details to divert funds to their own accounts, leading to financial losses for bill pay providers and their customers.
Counterfeit Invoices: Fraudsters may create entirely fake invoices that appear legitimate, using the names, logos, and branding of reputable businesses or service providers. These invoices can be sent to unsuspecting individuals or businesses, who may unwittingly make payments for goods or services that were never delivered.
Altered Payment Details: Another form of fraud involves altering legitimate invoices or payment details. Fraudsters may intercept or modify invoices in transit, changing the payment beneficiary's bank account information to divert funds to their own accounts. This can lead to legitimate payments being misdirected and funds being lost.
According to our most recent Financial Professional Census, the average estimated cost of invoice fraud to middle markets businesses is an eye-watering $280,000 per year.
Finance teams are identifying, on average, one invoice fraud attack every month, notwithstanding those which slip through unnoticed. In total, the 2,750 businesses surveyed uncovered more than 34,000 cases of invoice fraud in the space of a year.
Veryfi offers a two stage approach to fraud
Veryfi Lens and Veryfi API
Lens Document Capture Framework
Veryfi Lens for Document Capture supports native mobile platforms, cross-platform SDKs and browser-based web applications.
Adding Veryfi Lens to your mobile solutions will delight your users with innovative camera features that increase document quality, accuracy and prevent fraudulent submissions.
If captured image drops below a Low quality threshold, a key “is_blurred” boolean value indicating this state is returned in the notification.
Helps you identify what type of document was captured into memory.
Computer Screen Detection
User tried to capture a receipt from their computer screen (LCD). This is typical of when user manipulates the receipt in photoshop. The key “lcd_screen” returns a list of probabilities that it was a screen for the last 30 frames.
What objects Lens saw during the last 30 frames. These objects are returned in the “detected_objects” along with the probability score of it being that object.
The angle in degrees the phone was in during capture of the document. See key “device_angle”. A capture process is typically 0-30 degrees and anything closer to 90 could signal the user it taking a photo of a screen.
Note: signal/s arrive on veryfi LensUpdate delegate.
"lcd_screen": [0.8, 0.7, 0.9, ...], // Last 30 frames
[ // Frame 1
], […] //Frame 2
Veryfi API fraud is an extension to Veryfi data extraction
Existing duplicate detection looks at: total, invoice #, date and vendor and some other assets etc.
User tries to submit a handwritten document or one created in MS Word.
"is_document": true, "document_type": "receipt",
Secondary (Lens was 1st) to check how many pages in the submitted document are blurred.
User has added a new line item by hand and/or modified the total by adding a 0 to the total to exponentially increase their spend claim.
Detects Moiré pattern due to interlaced scanning.