The diversity and thriving evolution of e-commerce entails high risks: cyber-attacks, compromised information and malware creation, among others.
The increasingly lower profit margins derived from the competitive framework must be protected efficiently through fraud prevention, with versatile tools that deal with different business cornerstones, maintaining high approval levels in the authorization systems, with lower rejections for transactional risk and fraudulent operations.
Sentinel monitors different channels: POS, Mobile POS, and e-Commerce, allowing the Acquirer to configure multiple analytical modules that detect unusual transactions and reduce fraud losses.
It grants the Acquirer the capabilities required to manage merchant risk, generating actions like point of sale blockage, payment blockage to the merchant or transaction blockage.
It helps the business by increasing the approval indices and chargeback reduction.
Sentinel supports management of risks associated with the merchant onboarding process.
Restrictive Lists: It helps determine if those associated with the merchant (representatives, shareholders, employees, among others) are in black lists, PEP lists, negative news, the OFAC list, and 450 other global lists.
Merchant Risk: It helps determine the risk entailed by the merchant to establish a due diligence or an enhanced due diligence process, and to manage the control parameters from a fraud perspective.
Relationships Analysis: Through this analysis, it is possible to determine undisclosed relationships between the merchant in process of formalization and the entire base of Acquirers of the Institution, as well as with merchants, representatives, shareholders, and elements that characterize it (directors, telephones, others) that were considered to be undesirable in the past.
With the beginning of merchant operations and transactions towards the authorization system, Sentinel establishes a new prevention approach that focuses more on transactions, response times, and preventive actions, completely on line. The system has several features in this area:
Rule Models and Statistical Behavior: They help to analyze unusual merchant behaviors to generate alerts and trigger automatic processes to mitigate risk. The analysts have a consultation feature or viewer to see the unusual activity with multiple inputs useful for the decision-making process:
Predictive Models: They add an additional sophistication level to the analysis of unusual merchant behavior, which includes technologies like neural networks, deep learning, decision trees, and others.
Expert Risk Score: It allows including in the system a risk matrix to assign a score to each transaction, based on the information contained, such as the MCC, amount, time, country of origin, etc.
The prevention and detection models generate alerts of unusual transactions that can be managed by the user of the system:
Investigation of cases makes it possible to track the entire process of unusual activity management of the merchant:
It offers a control chart of pending investigations:
It is a powerful tool for data discovery and information analysis. Its main characteristic is that it is oriented to end users without significant technical knowledge, who can design their own reports and control charts, schedule their execution, and automaticaly send to different recipients, if desired.