The year 2022 has seen a significant increase in financial scams, resulting in losses amounting to $8.8 billion. The United States Federal Trade Commission has reported a 30% rise in losses from financial fraud compared to the previous year, highlighting the dark side of technological progress. Scammers have now turned to generative artificial intelligence (AI) to create sophisticated and realistic scams.
Reports indicate that scammers are using AI-powered chatbots that can mimic human conversation, request personal financial information, create malware, write convincing phishing emails, and even imitate human voices. The speed and convenience of instant digital payments provide fraudsters with an opportunity to deceive users into transferring money instantly, leaving victims with little chance of recovering their funds.
In the ongoing battle against financial crime, risk management platforms like Feedzai play a crucial role. Powered by machine learning and big data, these platforms offer advanced technology and high-level security to combat complex financial scams.
So, what exactly is Feedzai and how does it work? Feedzai is a risk operations (RiskOps) platform that utilizes machine learning and AI to provide fraud prevention solutions to retailers, banks, and payment providers. With a global reach and services available in 190 countries, Feedzai aims to protect individuals from the risks associated with e-commerce and banking.
Originally founded in Portugal in 2011, Feedzai is now headquartered in California and has established itself as a market leader in its field. The company was initially developed by Nuno Sebastião, Paulo Marques, and Pedro Bizarro to offer operational intelligence and fraud detection solutions. Today, Feedzai has transformed into a suite of AI-based solutions specifically designed to detect fraud and prevent financial crime. Its main clients include prominent banks and financial institutions such as Citibank, Standard Chartered, and Lloyds Banking Group.
Feedzai is built on the concept of RiskOps, which operationalizes risk through fair and customer-centric approaches. By empowering financial institutions to detect suspicious behaviors, identify scammers, and combat fraud, RiskOps helps manage identity, data, and fosters collaboration across various systems. This enables institutions to provide their customers with superior and reliable services.
In technical terms, RiskOps platforms like Feedzai provide financial institutions with a framework for more effective financial risk management. By standardizing the risk management and fraud prevention approach, these platforms make it easier to assess abstract and difficult-to-define concepts like risk. Consequently, institutions can confidently measure and analyze risk, leading to smarter decision-making.
Feedzai’s platform leverages machine learning to process events and transactions rapidly while presenting easily understandable results through a human-readable semantic layer. Its learning model processes multiple data streams and insights from various sources to create detailed customer profiles, making it easier to identify fraudulent activities and potential victims.
To minimize the risk of fraud and money laundering, Feedzai collects data from diverse sources, including cross-channel, cross-product, and third-party data. This enables the platform to differentiate between authentic and fraudulent transactions and provides a comprehensive view of each individual’s interaction with the bank. These profiles also help identify customers who are more likely to fall victim to scams, even before they are targeted.
Feedzai’s platform detects fraud quickly and in real-time for various payment types such as cards, instant transfers, digital wallets, withdrawals, and deposits. It also offers production-ready application programming interfaces (APIs) for different payment methods, providing real-time transaction recommendations, such as whether to approve or decline them.
Feedzai addresses several threats and weaknesses, including the limitations of legacy solutions. Traditional approaches to fraud detection often rely on outdated point solutions that use rules-based methods and fail to specifically target scams. These solutions are restricted to individual channels, making them vulnerable to fraud schemes that span multiple banking products or payment platforms. Moreover, they analyze either behavioral or financial activity separately, hindering the ability to quickly identify ongoing scams. Additionally, traditional fraud protection measures struggle to adapt quickly to new tactics employed by scammers. Machine learning fills this gap by assimilating new data and providing real-time insights into customer behavior.
Another area where Feedzai excels is in combatting the creation of fake accounts to exploit rewards systems. With the rise of digital transactions and increased gamification, fraudsters create fake accounts to collect rewards. Feedzai specializes in analyzing network transactions made by account holders to identify hidden fraudulent payment networks, detecting patterns that may not be immediately obvious.
Feedzai also helps detect SIM swaps, a type of fraud where perpetrators pose as the owners of phone numbers to gain control. By analyzing transactional data, Feedzai can detect suspicious behavior such as multiple transactions attempted from different devices in quick succession, alerting financial institutions to potential scams.
Feedzai’s key features for fraud prevention include a comprehensive RiskOps architecture that operates in real-time, early intervention and education capabilities, human-centered AI that prioritizes customers, inbound payment monitoring to prevent scams, triage behavior alerts for quick identification of fraudulent behavior, and customization options to adapt to changing fraudulent schemes.
Looking ahead, AI-powered risk operations are expected to undergo transformative growth. Cutting-edge machine learning algorithms and predictive analytics will revolutionize risk assessment, detection, and mitigation across sectors. AI’s ability to rapidly analyze large datasets will uncover complex patterns and anomalies, enabling proactive risk management. Real-time monitoring and adaptive algorithms will enhance response agility, reducing vulnerabilities. Sentiment analysis and natural language processing will improve understanding of risk, including social and reputational factors.
Furthermore, collaborative AI-human workflows will optimize decision-making, and AI’s self-learning capabilities will enable continuous adaptation to evolving risks. Ultimately, AI-powered risk operations will usher in an era of precision, efficiency, and resilience, mitigating threats and creating safer environments.