Using Fraud Analytics to Stay Ahead of Criminals

Banks suffered an astounding $485.6 billion loss to fraud and scams last year, highlighting the urgent need for them to outpace criminals. Fraud analytics plays a crucial role in enabling banks to transition from merely reacting to fraud to proactively preventing it.

Explore how fraud analytics helps detect and prevent various types of fraud, minimizing financial losses and improving customer trust and satisfaction.

What is Fraud Analytics?

Fraud analytics combines artificial intelligence (AI), machine learning, and predictive analytics to enable advanced data analysis. By leveraging these technologies, banks can quickly analyze and gain insights from vast amounts of data.

The integration of technological analytics with human expertise provides numerous benefits, including identifying fraud, uncovering hidden patterns, and predicting future threats. Most importantly, it allows banks to respond to suspicious activities in real time.

Why Banks Need Fraud Analytics

The rise of digital banking has been exponential, particularly during the pandemic, when in-person banking became less accessible. This shift has generated massive volumes of digital data, creating new opportunities for fraudsters to exploit vulnerabilities in banking systems.

Each new digital banking channel introduces a wave of fraud tactics. Traditional rules-based systems often fail to keep up, as fraudsters quickly adapt to and bypass established rules. This leaves banks in a continuous cycle of reacting to new schemes.

Fraud analytics breaks this cycle by proactively analyzing large data sets in real time, identifying unusual patterns that traditional systems might miss. This enables banks to calculate accurate transaction risk scores and make informed decisions before approving transactions.

Key Benefits of Fraud Analytics

Predict Future Fraud Risks

Fraud analytics shifts banks from reactive to proactive strategies by using machine learning models to analyze historical data and predict potential fraud patterns. This forward-looking approach helps banks stay ahead of fraudsters and prevent issues before they escalate.

Real-Time Fraud Detection to Minimize Losses

Fraud analytics systems use pattern recognition and real-time monitoring to swiftly detect anomalies, reducing the impact of fraudulent activities. Early detection protects both the bank and its customers from significant financial losses.

Enhanced Customer Trust and Satisfaction

Proactive fraud prevention reassures customers that their funds and sensitive information are secure. This fosters greater trust and satisfaction, strengthening the relationship between banks and their clients.

To Know More, Read Full Article @ https://ai-techpark.com/fraud-analytics-powered-by-ai/

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Feedzai’s AI Technology Earns Industry Recognition by Chartis

Feedzai, the world’s first RiskOps platform, has achieved two significant accolades from Chartis Research. We are proud to be recognized as the leading AI-driven anti-fraud platform and to rank among the top 5 overall in the prestigious RiskTech AI 50 2024 rankings.

These achievements underscore Feedzai’s pioneering role in leveraging artificial intelligence and machine learning to advance financial risk management. With an AI-first approach, our technology is designed to swiftly adapt to emerging fraud and scam patterns.

Feedzai Among Top 5 in RiskTech AI 50 2024 Rankings

It is a privilege to be named the top AI-driven anti-fraud platform in Chartis Research’s RiskTech AI 50 2024 report. This recognition reflects our unwavering commitment to empowering the financial sector with real-time fraud detection and prevention, delivering unmatched precision through state-of-the-art AI and machine learning.

We are equally honored to place #4 overall in AI, a notable achievement in a highly competitive industry. This ranking highlights Feedzai’s innovative approach to combating fraud and financial crime.

Driving Financial Services with an AI-First Approach

Feedzai’s recognition stems from its AI-first foundation. From the very beginning, we have built our platform with AI at its core, ensuring our models are flexible, responsible, and well-governed—key differentiators in fraud and financial crime prevention.

Unlike many legacy systems developed before the rise of AI, Feedzai’s technology was purpose-built for modern challenges. Traditional systems often rely solely on rules-based models, which, while effective against established fraud patterns like account takeovers or card-not-present fraud, struggle with evolving threats such as authorized push payment scams.

Understanding customer behavior is critical in addressing complex, individualized fraud scenarios like elder fraud or purchase scams. Rules-based systems alone often fall short in these nuanced cases.

Feedzai’s patented technologies are tailored exclusively for fraud and financial crime prevention. At the heart of our platform is AutoML, which accelerates the deployment of machine learning models from weeks or months to mere days, streamlining the fight against fraud.

Continued Recognition for Feedzai’s Innovative AI Technology

The acknowledgment from Chartis Research reinforces Feedzai’s dedication to safeguarding commerce and financial services through advanced AI and machine learning. This honor adds to a series of accolades affirming our leadership in fraud prevention.

Recently, Feedzai was named a Leader in the 2024 IDC Worldwide Enterprise Fraud Solutions Vendor Assessment. The IDC MarketScape report highlighted our omnichannel capabilities, enabling real-time monitoring of customer interactions and transactions to enhance accuracy and improve customer experiences.

Additionally, Feedzai was recognized as a Leading Contender in Datos Insights’ Behavioral Biometrics Market Analysis for innovations in behavioral biometrics and device fingerprinting. Our biometrics solution also earned recognition in Quadrant Solutions’ SPARK Matrix™: Behavioral Biometrics, 2023 report.

To Know More, Read Full Article @ https://ai-techpark.com/feedzai-ranks-top-5-in-risktech-ai-50-2024/

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Dynamic Risk Assessment for Smarter Merchant Monitoring

Acquiring banks often face the challenge of balancing merchant satisfaction with risk management. On one hand, they aim to keep merchants happy by enabling quick payouts. On the other, they must protect themselves from financial losses if a merchant's risk profile unexpectedly shifts. Dynamic risk assessment plays a crucial role in safeguarding acquirers while supporting businesses with the liquidity they need to operate smoothly.

Here’s how Feedzai’s Dynamic Risk Assessment, available as an add-on to its Merchant Monitoring solution, helps acquirers mitigate risk while ensuring merchants maintain access to vital cash flow.

The Cash Flow Challenge for Merchants

Cash flow is essential for merchants, especially small businesses, to sustain operations. Access to funds allows merchants to replenish inventory, pay employees, settle utility bills, and maintain vendor relationships.

However, studies reveal that nearly 75% of merchants frequently experience delayed payouts. Without timely access to their earnings, many businesses face severe cash shortages, threatening their survival.

To address this issue, some acquirers are adopting same-day payouts for merchants in good standing. In markets like Australia and Brazil, payouts are even offered intra-day or on-demand. While these measures benefit merchants, they also increase financial exposure for acquirers if a merchant’s risk level is underestimated.

Current Merchant Risk Management Approaches

Acquirers typically employ several strategies to balance risk management and merchant needs. Each has its advantages and limitations:

Increased Merchant Deposit Requirements

Pros: Helps acquirers offset liability risks by requiring merchants to maintain larger reserves.

Cons: Reduces merchants' available cash flow, making it harder to cover operational expenses.

Faster Settlements for Merchants in Good Standing

Pros: Rewards merchants with a strong track record, offering quicker access to funds.

Cons: Excludes newer merchants who lack a year’s history, even if they meet other risk criteria.

Transaction Data Analysis

Pros: Provides insights into individual transaction risks.

Cons: Focuses on transaction-level data, often missing aggregated risk signals and relying on manual processes prone to human error.

To Know More, Read Full Article @ https://ai-techpark.com/smart-merchant-risk-strategies/

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