Describe Fraud Protection
Fraud Protection introduces the functionality of cloud-based fraud protection to help organizations protect both revenue and reputation by providing the tools and capabilities needed to:
Decrease fraud and abuse.
Reduce operational expenses.
Increase acceptance rates.
Safeguard user accounts from fraud exposure.
Fraud Protection is a cloud-based solution designed to help merchants decrease fraud costs, improve the customer experience, and streamline operational efficiency. Fraud Protection uses AI to understand patterns about fraudulent transactions and benefit from connected knowledge generated from using the service.
Evaluate purchase transactions
By implementing Fraud Protection, it's possible to scrutinize the purchase transactions and increase the bank acceptance rate. This process eventually reduces the fraud loss of the organization.
For instance, a legitimate transaction is flagged as fraudulent or suspicious and triggers a bank’s fraud detection system. This response is known as a false positive. An individual reports a legitimate transaction as unauthorized, which is known as friendly fraud. Dynamics 365 has built-in insights and tools that help recognize these patterns and balance revenue opportunity versus fraud loss and checkout friction.
Fraud Protection can be paired with an acceptance booster that shares transactional trust knowledge with issuing banks to help boost your bank acceptance rates. An acceptance booster gives broad awareness of fraud activity across the globe, while keeping the security of your confidential information and shoppers’ privacy as a priority.
Fraud Protection learns and adapts continuously from patterns and equips you with tools to:
Optimize fraud controls.
Minimize false positive transactions and friendly fraud.
Improve the acceptance rate of e-commerce.
Prevent account-related fraud
Fraud and abuse in the digital world can come in many forms. Account access by someone who uses excessive forceful attempts (brute-force) to try to "force" their way into your private account(s) is an old, less sophisticated attack method used by fraudsters.
In today’s digital world, fraudsters use more sophisticated technology, such as deceptive email messages or copycat URL addresses, to trick internet users into revealing personal or confidential information (phishing).
Both forms of attack can lead to accounts being accessed by someone who isn't authorized or given permission to use the account (account compromise), with the intent to commit fraudulent activity.
Internet users who decide to commit fraud and abuse against a business may also:
Create fake accounts to obtain free or promotional benefits as a new user.
Create an account a year or two in advance, leave it in good standing by not using the account, and then randomly use it to commit fraud (sleeper accounts).
Negatively affect legitimate customers by forcing banks to implement a less seamless sign-up experience.
Fraud Protection provides merchants with the capability to assess whether attempts to create new accounts and attempts to log in on to a merchant’s ecosystem are fraudulent. Risk assessment in Fraud Protection is used by the customer to block or challenge suspicious attempts to create new fake accounts or to compromise existing accounts.
Account protection includes APIs for real-time risk assessment and rule and list experience to optimize risk strategy as per your business needs, and a scorecard to monitor fraud protection effectiveness and trends in your ecosystem.
Prevent fraudulent transactions
Fraud Protection allows you to combat fraud associated with:
Returns
Improper discounts
Merchandise mishandling
Inventory turnover
You can upload historical data into the system so the adaptive AI models can look for anomalies. The AI intelligence technology works with a collection of software services, apps, and connectors to turn your unrelated sources of data into coherent, visually immersive, and interactive insights using Power BI.
The historical data must be imported from four entities:
Transactions
Sales
Payments
Payment methods
If outliers are detected when the reports generate, the adaptive AI models assign scores to rank them. These scores are based on their deviation from the average value in the overall data that was analyzed. A higher score indicates a higher probability that a meaningful anomaly is present. A store manager can consult these scores as part of an investigation. The reports provide an aggregated view of the anomalies.