Feedzai is an advanced artificial intelligence-powered risk operations platform that assists in the detection of fraud and scams at an institutional level
Financial threats and scams are increasing, with Losses exceeding $8.8 billion in 2022. Based on reports by the United States Federal Trade Commission, losses accrued from financial crimes have increased by 30% between 2021 and 2023. This paints a terrible picture of the dark underside of digital and technological advancements, with scammers now using generative artificial intelligence (AI) to set up highly complex and realistic-looking scams.
Many reports indicate that scammers can develop chatbots that mimic the targeted human conversation, create malware, ask for personal financial details, write complex phishing emails, and mimic human voices. The urgency of instant payments through digital banking also offers a chance for fraudsters to deceive the users and convince them to transfer money immediately, leaving the defrauded users with little to no hope of recovering their money.
Risk management platforms such as Feedzai have joined the financial space to fight against these scams. Powered by big data and machine learning, such platforms utilize advanced technology and high-level security to fight all kinds of complex financial crimes.
What Is Feedzai And How Does It Operate?
By description, Feedzai is a risk operations (RiskOps) platform that utilizes artificial intelligence and machine learning technology to offer banks, retailers, and payment providers fraud prevention solutions. This platform has a global reach that strives to protect users from the risks that come with banking and e-commerce.
Launched in 2011 in Portugal, Feedzai later relocated to California and it offers services in 190 nations. Considered a market leader in the sector, the firm was originally set up by its founders Paulo Marques, Nuno Sebastião, and Pedro Bizarro, to provide fraud detection solutions and operational intelligence.
Currently, Feedzai has changed into a suite of AI-based solutions mainly targeting the detection of fraud and preventing financial crime. Feedzai’s primary clients are established financial institutions and banks such as Standard Chartered, Citibank, and Lloyds Banking Group.
Feedzai Uses Machine Learning
Feedzai was developed using the concept of RiskOps, a method that mainly operationalizes risk through fair and customer-centric strategies. RiskOps also empowers financial institutions that help detect any suspicious behaviors, identify scammers before they strike, and combat fraud.
RiskOps assists financial institutions in managing their identity, and data, and enhance collaboration across different platforms more efficiently. This enables institutions to offer their users superior and reliable services.
Technically, what these RiskOps platforms do is offer financial institutions an infrastructure for highly effective financial risk management services. Standardizing the risk management and fraud prevention approach makes it easy to evaluate abstract and difficult-to-define concepts such as risk. On that note, the institutions can confidently measure and analyze risk and make smart decisions based on the findings.
On its part, Feedzai’s platform utilizes machine learning to process transactions and events rapidly while offering easily understandable results via an added human-readable semantic layer. Notably, this learning model processes and transforms many data streams and insights from different sources to develop highly detailed customer profiles, which makes it easy to identify fraudulent activities and users facing potential threats.
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Feedzai reduces the risk of fraud and money laundering for financial institutions by collecting data from different sources, including cross-product, cross-channel, and third-party data.
This assists in distinguishing between fraudulent and authentic transactions, and it offers an extensive view of the way every user interacts with the bank. All these profiles also make it easier to identify clients who are highly likely to fall victim to scammers, even before a scam targets them.
The platform also detects fraud rapidly and in real-time for various types of payment, including instant transfers, cards, digital wallets, deposits, and withdrawals. This solution also provides production-ready application programming interfaces (APIs) for different payments to offer real-time transaction recommendations, including whether to decline or approve them.
What Is Feedzai Used For?
There are multiple threats and weaknesses that Feedzai assists in addressing:
Limitations Of Legacy Solutions
Financial institutions mostly utilize many outdated point solutions that utilize rule-based strategies to detect fraud but do not particularly focus on scams. Traditional strategies have three main restrictions. First, they are limited to siloed channels, which makes them highly vulnerable to fraud schemes that spread across different banking products and payment platforms.
Secondly, legacy solutions help detect fraud by analyzing either financial activity such as transactional data across banking platforms, and behavioral activity such as device and app usage patterns, biometrics, malware incidence, and network activity. Nonetheless, their analysis does not consider both kinds of activity together, reducing the ability to rapidly identify a continuous scam as it happens.
Finally, the fraud protection measures do not adapt quickly enough to counter any new tactics utilized by scammers. Machine learning fills the gap by assimilating new data and offering real-time insights into customer behavior. Feedzai’s platform is well-designed to rapidly detect money laundering, financial fraud, and other illegal activities with AI-driven strategies customized for different payment mechanisms’ strategies.
Fighting Fake Accounts Creation To Increase Rewards
The increased rate of digital transactions, mainly for small and frequent purchases, has boosted the rewards for merchants and consumers. Nonetheless, the growth also offers a chance for scammers and criminals to exploit the reward system.
Fraudsters are now taking advantage of the shift toward cashless transactions and increased gamification by setting up fictitious accounts and sending money in circles to collect rewards.
Feedzai focuses on analyzing network transactions that are executed by account owners to determine hidden fraudulent payment networks. That means they can Detect fraudulent patterns that might not be instantly obvious.
Detecting SIM Swaps
SIM swapping is a form of fraud where the criminal poses as the owner of a phone number and then convinces a call center or branch employee to swap out the linked SIM card. That is done by offering the victim’s data to the carrier.
Criminals collect this data through hacks and data breaches, or they exploit information that users have already shared publicly on social media. They utilize that information to dupe carriers into allowing them to replace the SIM card that is linked to a phone number with a SIM card they own. By doing that, all incoming calls and text messages are re-directed to the fraudsters.
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Feedzai assists in fighting this occurrence by analyzing transactional data that can be utilized in the detection of a SIM swap. For example, when many transactions are attempted from multiple devices rapidly, Feedzai’s algorithms will flag them as suspicious incidents and alert the financial institution of a possible scam.
The Future Of AI-Powered Risk Operations
Risk operations that are enabled by AI are expected to see transformational growth in the future. Risk detection, evaluation, and mitigation across industries will be revolutionized by advanced machine learning algorithms and some predictive analytics.
A quick analysis of massive data sets by artificial intelligence will show complex patterns and any anomalies, supporting proactive risk management. Response agility will be enhanced, decreasing potential vulnerabilities, and utilizing real-time monitoring and adaptive algorithms. Sentiment evaluations and natural language processing (NLP) will increase knowledge of risk, including reputational and social factors.
Moreover, collaborative AI-human workflows will enhance decision-making, and artificial intelligence’s self-learning skills will let it constantly adapt to all forms of risks as they evolve. Eventually, AI-powered risk operations will introduce an era of precision, resilience, and efficiency, minimizing threats that may come and promoting secure environments.
