To Block or not to Block- AI led risk assessment & other strategies to improve customer experience and reduce reissuance cost

To Block or not to Block- AI led risk assessment & other strategies to improve customer experience and reduce reissuance cost

Banks face a significant dilemma when it comes to reissuing cards to prevent fraud. This dilemma centers around balancing the need to protect customers from fraud with the potential disruptions and costs associated with card reissuance.

Banks can reduce the reissuance cost of cards with compromised information by using AI and implementing targeted measures to determine when reissuance is necessary. Here are some strategies:?


AI led Risk-Based Assessment One of the most important strategies for minimizing unnecessary card reissuance is leveraging risk-based assessment. Artificial intelligence (AI) and machine learning play a crucial role in enhancing the accuracy of risk-based reissuance. Banks can analyze individual cardholder behavior, detecting anomalies and unusual transaction patterns. AI-driven models can improve over time, learning from historical data to better predict when a card is at risk of fraudulent use. This ensures that only truly at-risk customers are reissued new cards, leading to more precise and efficient management of fraud risks. Also, by analyzing factors such as the nature of a data breach—whether it compromised cardholder data, CVV codes, or account details—banks can make informed decisions. Instead of reissuing cards across the board, they can determine which customers are at actual risk of fraud and need new cards, thereby reducing unnecessary reissuance.


?Customer Segmentation Following the risk assessment, banks can categorize cardholders into different risk levels, such as high, medium, or low. High-risk customers, whose cards are more likely to be exposed to fraud, may receive immediate reissuance. Medium-risk cardholders can be monitored more closely for suspicious activity, with reissuance only if needed. Low-risk customers, meanwhile, may not require new cards at all, allowing banks to further reduce the volume of reissued cards and the associated costs.


?Enhanced Monitoring for Low-Risk Customers For customers identified as low-risk, banks can implement additional layers of monitoring to ensure their cards remain secure. Real-time transaction monitoring systems can flag any suspicious activity, and cardholders can receive alerts if unusual spending patterns are detected. In cases where a breach is detected but no immediate risk is evident, this proactive monitoring reduces the need to reissue cards while ensuring customer accounts remain protected.

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Effective Communication and Customer Trust Banks must also maintain transparent communication with their customers, explaining why their card has not been reissued and assuring them of the protective measures in place. When cardholders understand that their bank is using advanced tools to monitor their transactions in real time, they are less likely to feel anxious about not receiving a new card. This open communication fosters trust and enhances the customer experience while maintaining operational efficiency.

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Cost Efficiency and Fraud Intelligence By reducing unnecessary reissuance, banks save significantly on production, distribution, and operational costs. Additionally, collaborating with global fraud networks allows banks to stay updated on the latest threats and tailor their reissuance policies accordingly. As more customers adopt digital wallets and tokenized payments, the exposure of physical card numbers to breaches is minimized, reducing the need for reissuance even further.

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The dilemma for banks in reissuing cards is a balancing act between security and service continuity. Banks must decide whether the potential benefits of protecting customers from fraud outweigh the operational challenges and customer inconvenience. By making informed, strategic decisions, banks can navigate this complex landscape effectively, ensuring both security and customer satisfaction.

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Disclaimer:?The postings on this site are the authors’ personal opinions. This content is not read or approved by their current or former employer before it is posted and does not necessarily represent their positions, strategies or opinions

Anupam Chatterjee

Experienced Consumer Risk professional

1 个月

Agree. Very informative. Maybe you could add a section on the extent by which AI models are more effective in these use cases compared to more traditional ones. Very well written.

Very informative Puneet

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