Accertify Machine Learning
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Accertify Machine Learning
|Company size (employees)||500 to 999|
|Type of solution||Service|
Accertify’s Industry Models with Dynamic Risk Vector Community data capability enables clients to leverage a world class Machine Learning model, tailored specifically to their industry + Dynamic Risk Vectors Global Community data capability.
Clients that opt into safely sharing their data are eligible to take advantage of the Industry specific Machine Learning model and community data. The Machine Learning model is designed to:
(a.) Dynamically update as fraud trends change – through use of the Dynamic Risk Vector Community Data Capability. Risk factors in the model will fluctuate up and down based upon the current risk of factors in the transaction – enabling the model to leverage the latest trends to make the optimal risk assessment.
(b.) Provide visibility into the reason why the model calculated the model probability per transaction. This enables visibility to Merchant Analysts who may review the transaction and why the model assessed the transaction risk as it did. Each transaction decisioned by the model shows a panel demonstrating the risk factors most significant in the model risk assessment.
(c.) Be flexibly implemented into client decisioning so that clients can still respect policies they want to follow. Clients can calculate the model probability on every transaction, but select to override the model risk in situations where they always want to approve or decline a transaction. For example, in cases of product limits – where merchants may want to only allow X purchases of a specific item, clients can leverage the model probability and respect the product limit in their decision.
Accertify’s Dynamic Risk Vector Community Data Capability is designed to be a safe manner that Clients can share and gain the value of community data.
Value to merchants:
• Less fraud incurred both by count and amount
• More transactions approved without Merchant Analyst review – yielding lower operational costs and increased sales
• Dynamic decisioning requires less time invested to change decisioning strategies to constantly stay on top of latest fraud trends
Value to stakeholders:
• Consumers who shop at client ecommerce sites are better protected from fraudulent purchases on their payment cards – enabling them with improved confidence in ecommerce and fewer hassles with having to process fraud claims on their payment cards
• Banks and card issuers incur fewer fraud claims on their cards issued – enabling fewer operational costs and improved customer service experience with their merchant relationship
Accertify is a leading provider of fraud prevention, chargeback management, digital identity and payment gateway solutions to customers spanning ecommerce, financial services, and other diverse industries worldwide. The Machine Learning model provides:
(a.) Speed of execution – Enabling this solution required significant investment in hardware and software to be able to aggregate, accumulate and calculate statistics on massive volumes of inbound transactions – and make the calculated statistics available for use in the current transaction in milliseconds of time.
(b.) Vast network of ecommerce community participants – Accertify’s leading ecommerce clients across industries participate in this capability – enabling a wide network of cross industry global intelligence
(c.) Dynamic Nature of solution - Industry Machine Learning models are tuned to take advantage of the Dynamic Risk Vectors to enable clients to dynamically adjust decisioning as risk patterns fluctuate – identifying risk as the fraud trends rise and relaxing controls as the fraud trends subside.