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Ethical Considerations in Credit Card Fraud Detection: Balancing Security and Privacy

May 28, 2024

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Security, Privacy & Credit Card Fraud Prevention

Using customer data for security analytics can jeopardize user privacy. Here’s how to balance security and privacy in credit card fraud prevention.

Did you know that many merchants are wary of customers who use credit cards of smaller community banks and neobanks? While frictionless signup and convenience attract customers, these FIs are the easiest targets of fraud. Consider the digital bank Chime, which attracted a huge number of users due to the flexibility and convenience it offered. But it was also plagued by “first-party fraud” and denial of card transactions.

Global losses to card fraud are expected to reach $397.4 billion by 2032.

A San Fransico-based fraud expert says, “Companies used to build financial products starting with the risk. Everything today is built starting with marketing, and risk oftentimes comes way further down the funnel.” While the opinion is personal, it highlights why emerging financial services providers are shunned by merchants, forcing customers to opt for products and services from bigger industry participants.

Your bank or credit union may choose to store and analyze massive amounts of user data to develop sophisticated techniques to bolster security. However, the primary tradeoff here is user privacy. While enhancing security requires monitoring all transactions, identifying patterns, and assessing deviations from those patterns, privacy demands that data be used only for “required and pre-specified” purposes. This may even hinder innovation. Due to the sensitive nature of data available with financial institutions, it is critical to balance the tradeoff between privacy and security. This blog looks at navigating the ethical considerations of ensuring top-notch security while protecting user privacy.

Security: A Financial Services Imperative

Comprehensive security comprises vendor management, vulnerability assessment, transaction monitoring, and regular audits. Credit card scams consistently circumvent traditional security measures due to their rule-based nature. Machine learning is key to minimizing credit card fraud by scrutinizing data in real time. Take a look at these statistics:

Ethics of AI Decision-Making

Surveillance is key to mitigating fraud as payments become instantaneous. And AI monitoring systems are the most powerful tools to bolster security across touchpoints and interfaces. Banks must assess the behavior and risk of credit for each transaction, evaluate any divergence from the consistency of user behavior, and make decisions about approving transaction. Here’s a look at ethical considerations you need to manage while using AI to make credit decisions.

Fairness

UNESCO highlights how limitations of the training data can result in biased AI models. AI models that learn with experience get biased toward the most popular categories. However, this is against the principles of fair access to those from less populated categories, despite being genuine. These could generate false positives and miss out on actual red flags. Use data normalization and dynamic algorithms to prevent algorithmic bias from denying services to any section of your customers. Ensure that AI-powered decisions are not discriminating against a group or an individual. Train data models with a variety of data sets. Leverage simulations to improve the accuracy of AI decisions

Citigroup was charged $25.9 million for discriminating against individuals it perceived as “Armenian-American based on their last names,” for their likelihood of committing fraud.

Explainability

Algorithmically logical decisions may often be ethically wrong. For instance, the absence of a credit score may prevent a student from getting an education loan. The decision-making process and decisions made by AI must be explainable. Additionally, users must be able to challenge automated decisions. Enable the raising and handling of human oversight requests for critical decisions, user concerns, and anomalies. The AI system administrator and human reviewer are accountable for unintended decisions and managing the consequences.

Adaptability

As user requirements change, their spending behaviors and transaction sizes also change. Choose adaptive learning models that evolve with user expectations to calculate risk. Augment your analytics with third-party data to detect application fraud via fuzzy mapping and ID profiling.

Ethics of Data Use

Argus Information & Advisory Services charged $37 million for improperly accessing,  using, and retaining anonymized credit card data.

Banks and credit unions must utilize credit card data optimally in the age of datafication. You can use it to discover fraudulent transactions or money laundering attempts and provide personalized recommendations. Adopt cutting-edge technologies, such as behavioral analytics and neural networks, to identify fraudulent behavior. FIs must leverage internal and third-party data to train AI models. These models can assist you in anomaly detection and provide real-time notifications. However, a breach of privacy regulations, even if the data is anonymized, can amount to penalties and reputational damage. Follow best practices to ensure ethical data use

Maintain Transparency

Get user consent for all purposes their data is being used. Clearly specify usage duration, intent, and the rights of data owners on their data in the privacy contract. An important aspect is to mention the types of data that will and will not be encrypted.

Adhere to Regulations

Comply with state, national, and cross-border laws of data protection. Follow the highest data encryption standards for storing or sharing data with third parties. Employ internal policies to uphold users’ rights to access, modify, or deny data access to any third party. Communicate the conditions where these may change.

Best Practices to Achieve a Balance Between Security and Privacy

While security is paramount to prevent unauthorized transactions, data modification, or malicious disclosure, privacy focuses on preventing intrusion, use, or transmission to an individual’s data.

  • Take a risk-based approach to prioritize critical and urgent security concerns while upholding privacy principles by preventing over or under-protection.
  • Employ data minimization to reduce the volume and scope of data to lower risk exposure and risk of intrusion due to excessive data collection or use.
  • Use encryption for data protection and adopt multi-factor authentication for transaction initiation.
  • Collaborate with stakeholders, regulators, and compliant third parties to stay on top of data privacy guidelines.
  • Rely on experienced technology partners to build and implement fraud management initiatives with superior technologies and ethical approaches.

Opus Technologies has nearly 3 decades of experience in digital payments, with expertise in credit management, risk assessment, and fraud mitigation. We offer exceptional credit card fraud detection, mitigation, and incident management technology solutions in collaboration with Featuresapce. Learn more about financial fraud management from our experts.

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