Top Reads of July

We're delving into the latest developments in the financial world, focusing on how emerging technologies and trends are shaping the industry. From the rise of credit card usage in India and its associated challenges to the power of machine learning in loan underwriting and the growing threat of AI-driven fraud, we have it all covered. Let’s dive in!

1. Best Practices to Prevent Card Chargebacks

As of March 2024, India has surpassed 100 million credit cards issued, highlighting the nation's growing embrace of digital payments. However, this surge in credit card usage also brings challenges, particularly for merchants who face increasing credit card chargebacks. Payment fraud and disputes are major contributors to these chargebacks, with estimated fraud losses reaching INR 2604 crore in just six months.

To combat chargebacks, merchants should adopt several best practices:

• Clear Customer Communication: Ensuring customers understand charges to prevent disputes.
• Data Analysis: Analyzing chargeback data to identify and eliminate root causes.
• Resilient Payment Systems: Reducing transaction errors through robust systems.
• Anti-fraud Measures: Implementing measures to protect against fraudulent charges.
• Detailed Records: Maintaining comprehensive transaction records for dispute resolution.

For more detailed strategies to prevent card chargebacks, read our blog: https://verinite.com/best-practices-to-prevent-card-chargebacks/

2. Using Machine Learning to Predict and Reduce Delinquency for Card/Loan Portfolios

Providing loans is fundamental for financial institutions, but traditional underwriting methods often fail to accurately assess applicant risk. This can lead to numerous loans being issued to individuals with poor repayment capacity, contributing to delinquency issues. In the US, 2.68 percent of all consumer loans in the first quarter of 2024 turned delinquent, with credit card debt being a significant contributor.

Machine learning offers a solution by enabling more accurate and granular risk assessments. It analyzes data from various sources to create realistic risk profiles, ensuring stricter yet fairer underwriting. This technology supports continuous learning, improving loan availability over time, and provides bias-free credit provisioning, reducing corruption risks.

By adopting machine learning, lenders can enhance risk assessment, streamline processes, and improve customer experiences. For more insights, read our blog: https://verinite.com/using-machine-learning-to-predict-and-reduce-delinquency-for-card-loan-portfolios/

3. Should the Payment Industry Be Worried About FraudGPT?

In the digital age, the financial world faces a growing threat from AI-powered fraud, referred to as "FraudGPT." The 2024 AFP? Survey Report highlights a significant increase in payment fraud attempts, with 80% of companies targeted in 2023. FraudGPT, while hypothetical, symbolizes the potential misuse of advanced AI for fraudulent activities, posing risks to credit cards, online wallets, and bank transfers.

To combat this, financial institutions must implement:

• Multi-factor Authentication (MFA): Adding extra security layers.
• Biometric Security: Using facial recognition or iris scans for high-value transactions.
• AI-powered Fraud Detection: Leveraging AI to detect and prevent fraud.
• Consumer Education: Raising awareness about social engineering scams.
• Collaboration: Fostering cooperation between financial institutions and law enforcement.

Staying vigilant against AI-driven fraud is crucial. For more insights on safeguarding the payment industry, visit our blog: https://verinite.com/should-the-payment-industry-be-worried-about-fraudgpt/

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