Our modular solution consists of a variety of complementary components that can be combined to address your unique challenges
a custom, robust and accurate model designed for online scoring.
to guarantee specific business requirements
to detect and alert about attacks and anomalies
enabling consumption of new data sources when required
We offer a unique solution for every specific problem your platform may face
Fraudsters obtain stolen credit cards data and try to monetize them in online and instore payments, generating chargeback losses for merchants
Our transactional score runs in less than 200 millisecons evaluating multiple data points to indicate chargeback risk in a transaction, enabling online fraud prevention.
Among other data points, it evaluates payments history and relational networks of creditcards, BINs, users, merchants, devices, IP, geolocation and shipping.
Users fall victim of phishing and social engineering attempts by hackers, which in turn take control of their accounts and whether profit their account balance or use their reputation to scam other users, generating significant losses and unpleasant user experiences across the platform
Our ATO model detects user behavior anomalies and evaluates similarity to other hacker attacks to assess ATO risk online. It can be integrated in any part of the user journey such as login, publications, purchases and money transfers.
Some inputs include account information changes, navigation, interactions, device, geolocation, IP and account relations network.”
Fraudsters take possession of an individual’s personal information and then use it for fraudulent purposes, such as opening new accounts, making purchases, or accesssing sensitive financial or personal information.
Our model elaborates a fingerprint of the users’ Proof of Life, and match it to the account data, relational network and behavior within the platform to prevent Identity Theft risk before fraudsters further exploit the stolen data.
A significant share of the marketing budget can be miss-spent rewarding fraudulent schemes that profit campaigns blind spots and vulnerabilities. These gangs create multiple accounts and benefit from campaigns but don’t generate real user base growth.
Our model enables smart marketing budget investment by identifying and penalizing these users based on their relationship with fraudulent networks, which typically perform money triangulations and share data related to device, creditcard, account data, IP and geolocation”
Some users apply to online credits with the intention of defaulting on their credits obligations, generating default losses for platforms.
Our model runs in real-time analyzing multiple data points about the user, as credit and purchasing history, navigation and behavior, device, IP and geolocation.
Fraudsters try to scam genuine buyers by creating a merchant profile with convenient prices and selling conditions. Sales are finalized but buyers do not receive the products they were expecting, or do not receive anything at all.
Our model detects merchant fraud online by analyzing the merchants’ behavior, publications, reputation, selling prices, peak in sales, interaction with buyers and shipping information.
Fraud not only occurs on merchant or buyer side. Some fraudsters generate a carrier profile to appear legitimate and benefit from stealing some or all of the cargos assinged to them.
Our model evaluates the carriers profile and behavior to asses the risk of fraud in real-time, consuming features such as deliveries history, device, account relations network and account profile.
Fraudsters obtain stolen credit cards data and try to monetize them in online and instore payments, generating chargeback losses for merchants
Our transactional score runs in less than 200 millisecons evaluating multiple data points to indicate chargeback risk in a transaction, enabling online fraud prevention.
Among other data points, it evaluates payments history and relational networks of creditcards, BINs, users, merchants, devices, IP, geolocation and shipping.
Users fall victim of phishing and social engineering attempts by hackers, which in turn take control of their accounts and whether profit their account balance or use their reputation to scam other users, generating significant losses and unpleasant user experiences across the platform
Our ATO model detects user behavior anomalies and evaluates similarity to other hacker attacks to assess ATO risk online. It can be integrated in any part of the user journey such as login, publications, purchases and money transfers.
Some inputs include account information changes, navigation, interactions, device, geolocation, IP and account relations network.”
Fraudsters take possession of an individual’s personal information and then use it for fraudulent purposes, such as opening new accounts, making purchases, or accesssing sensitive financial or personal information.
Our model elaborates a fingerprint of the users’ Proof of Life, and match it to the account data, relational network and behavior within the platform to prevent Identity Theft risk before fraudsters further exploit the stolen data.
A significant share of the marketing budget can be miss-spent rewarding fraudulent schemes that profit campaigns blind spots and vulnerabilities. These gangs create multiple accounts and benefit from campaigns but don’t generate real user base growth.
Our model enables smart marketing budget investment by identifying and penalizing these users based on their relationship with fraudulent networks, which typically perform money triangulations and share data related to device, creditcard, account data, IP and geolocation”
Some users apply to online credits with the intention of defaulting on their credits obligations, generating default losses for platforms.
Our model runs in real-time analyzing multiple data points about the user, as credit and purchasing history, navigation and behavior, device, IP and geolocation.
Fraudsters try to scam genuine buyers by creating a merchant profile with convenient prices and selling conditions. Sales are finalized but buyers do not receive the products they were expecting, or do not receive anything at all.
Our model detects merchant fraud online by analyzing the merchants’ behavior, publications, reputation, selling prices, peak in sales, interaction with buyers and shipping information.
Fraud not only occurs on merchant or buyer side. Some fraudsters generate a carrier profile to appear legitimate and benefit from stealing some or all of the cargos assinged to them.
Our model evaluates the carriers profile and behavior to asses the risk of fraud in real-time, consuming features such as deliveries history, device, account relations network and account profile.