TrustDecision | Credit Card Fraud Detection System: A Comparative Approach

Key Takeaways

Credit card fraud is rising globally, with payment card fraud losses hitting about USD 33.83 billion in 2023, putting sustained pressure on issuers, acquirers, and merchants.
Traditional, rules-only credit card fraud detection systems struggle with card-not-present (CNP) and ecommerce attacks, which leads to high false positives and heavy manual review workloads.
Modern fraud detection in credit card environments relies on machine learning, behavioral analytics, and device intelligence to detect subtle, real-time anomalies across channels.
Globally, card-not-present and ecommerce credit card fraud already account for roughly 70–74% of card fraud losses, making multi-layered defenses essential for regional merchants.
TrustDecision’s AI-based credit card fraud detection solution combines ML, device fingerprinting, and network analysis to deliver real-time risk scoring and flexible integration for banks, fintechs, and ecommerce platforms.

Why Is Credit Card Fraud Rising?

The digital shift in commerce and financial services has transformed how consumers pay — and how fraudsters attack.

Key drivers include:

Explosive digital growth
- Digital banks, super apps, and ecommerce platforms are onboarding millions of new users who may be less familiar with digital fraud risks.
Card-not-present (CNP) dominance
- Online and in-app payments have overtaken point-of-sale (POS) in many markets, giving fraudsters more opportunities to abuse stolen card data without needing physical cards.
Cross-border payments and marketplaces
- Merchants in Southeast Asia and Latin America increasingly sell into US and EU markets (and vice versa), exposing them to new fraud typologies and regulatory environments.
Organised fraud rings and automation
- Criminal groups use bots, emulators, and device farms to test stolen cards, run credential-stuffing attacks, and scale social engineering scams.

For institutions operating in Asia Pacific (APAC), Europe, Middle East, and Africa (EMEA), and Latin America (LATAM), this means credit card fraud prevention can’t rely on rules alone. It requires data-driven, adaptive defence.

How Do Fraudsters Get Credit Card Numbers?

Understanding how card details are stolen is the first step in designing effective credit card fraud protection and detection strategies.

Phishing

Phishing uses fake emails, SMS, calls, or social media messages that impersonate:

Banks and card issuers
Delivery companies
Ecommerce marketplaces or government agencies

Victims are tricked into entering card details, passwords, or one-time passwords (OTPs) on fake websites, or sharing them directly with scammers.

In APAC and LATAM, phishing is often blended with local messaging apps and social platforms; in EMEA, fraudsters frequently exploit official-looking government or bank branding.

Skimming

Skimming involves placing covert devices on:

ATMs
Fuel pumps
Unattended POS terminals

These devices capture card data (magstripe or chip) and sometimes PINs. Fraudsters then:

Clone cards for in-person fraud, or
Use card numbers for CNP attacks and online purchases.

Skimming remains a risk in regions where magstripe cards and older terminals are still in use.

Data Breaches

Large-scale breaches at:

Retailers
Payment processors
Loyalty programmes
Third-party service providers

can expose millions of card numbers in one event. Stolen data is sold on criminal marketplaces and combined with identity data (names, addresses, phone numbers) to execute:

High-speed CNP fraud runs
Synthetic identity fraud
Account takeover attempts

Because businesses can’t fully control third-party breaches, credit card fraud detection systems must assume card data may already be compromised and focus on transaction- and behaviour-based risk signals.

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Credit Card Fraud Detection Methods Compare: Traditional vs Modern

Businesses typically start with simple controls and progressively move towards layered, data-driven fraud detection credit card strategies.

Traditional Methods

In the early days of credit card fraud detection, two methods dominated:

Rule-based systems – Predefined rules flag transactions, for example:
- Amount thresholds (e.g., > RM5,000)
- High-risk countries
- Velocity rules (e.g., too many transactions in a short period)
Manual reviews – Human analysts inspect flagged transactions and decide whether to approve, decline, or request more information.

Strengths

Transparent and easy to explain
Quick to implement for basic scenarios
Useful as baseline controls and compliance checks

Limitations

High false positives, especially with changing customer behaviour (travel, seasonal campaigns, new product launches)
High labour cost for manual reviews
Poor adaptability to new fraud patterns, bots, and organised rings
Static rules are easy for fraudsters to reverse-engineer
Slow response, which can be critical in high-speed fraud attacks

Manual reviews add human judgment but:

Are slow and resource-intensive
Delay decisions and frustrate genuine customers
Become unmanageable during peak periods (e.g., Singles’ Day, Black Friday sales)

Traditional approaches are essential building blocks, but modern credit card fraud prevention for businesses requires more intelligent, adaptive methods.

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Modern credit card fraud detection systems complement rules and human review with stronger, layered controls.

Biometric Verification

How does it help detect card fraud?

Biometric verification uses fingerprints, facial recognition, and iris scans to confirm that the person using the card or logging in matches the genuine account holder.

Benefits:

Much harder to steal or replicate than passwords or PINs
Useful for preventing account takeover and high-risk transactions
Supports mobile and in-app payment flows common in emerging markets

Biometrics are increasingly adopted in super apps, Strong Customer Authentication journeys, and mobile-first banks.

Tokenization

What is tokenization in credit card fraud protection?

Tokenization replaces the real card number with a one-time or limited-use token during transactions:

The token is useless outside its specific context
Card details are never exposed to merchants in clear form
Reduces the impact of data breaches and malware on POS devices

Tokenization is a core layer in credit card protection fraud strategies for ecommerce, in-app payments, and digital wallets.

Multi-Factor Authentication (MFA)

How does multi-factor authentication (MFA) reduce card fraud?

Multi-factor authentication — sometimes referred to as strong customer authentication — requires:

Something you know (password or PIN)
Something you have (device, app, security key)
Something you are (biometrics)

Risk-based MFA protects:

New device logins
High-value or unusual transactions
Sensitive account changes

When combined with intelligent risk scoring, MFA supports fraud protection credit card journeys without overwhelming genuine customers with friction.

How Does TrustDecision Detect and Prevent Credit Card Fraud?

TrustDecision provides an AI-driven credit card fraud detection system designed for banks, fintechs, payment processors, and ecommerce businesses.

AI and Machine Learning at the Core

TrustDecision’s Fraud Management solution and ARGUS® Fraud Management Platform:

Score every transaction in real time using machine learning
Ingest identity, device, behavioural, and network signals into a unified risk engine
Continuously learn from new fraud cases and chargebacks
Offer flexible rule and strategy configuration on top of ML scores

This gives organisations a central brain for fraud detection in credit card payments, instead of fragmented tools and manual spreadsheets.

Biometric and Device Intelligence Integration

TrustDecision supports:

Biometric checks as part of step-up authentication flows
Device fingerprinting to create persistent, tamper-resistant device identities
Risk-based authentication (only adding friction when risk is high)

The device intelligence layer helps detect:

Emulators and device farms
Multiple accounts or cards using the same risky device
Cross-merchant or cross-product abuse patterns

This is especially effective in mobile-first markets in Southeast Asia and Latin America.

Flexible Integration Options

TrustDecision offers flexible deployment and integration patterns:

APIs and SDKs for web, app, and backend systems
Cloud or hybrid deployment models
Gradual rollout by product, geography, or portfolio segment

This enables issuers, acquirers, and merchants to adopt advanced credit card fraud prevention without replacing their entire technology stack.

Regional Relevance

TrustDecision’s models and rules are informed by live deployments:

APAC (e.g., Indonesia, Malaysia, Philippines): high mobile usage, strong growth in digital lending and BNPL, and promotion abuse across super apps and ecommerce platforms.
EMEA: stronger regulatory frameworks (e.g., PSD2/SCA) and high card penetration; emphasis on balancing strong authentication with conversion.
LATAM (e.g., Mexico, Brazil): rapid rise of digital banks and fintechs; organised fraud rings that exploit both identity gaps and device manipulation.

TrustDecision’s case studies highlight how this regional insight translates into measurable reductions in fraud losses and manual review workloads.

How Can Businesses Strengthen Credit Card Fraud Detection Today?

A practical how to detect credit card fraud strategy blends policy, people, and technology.

For Issuers and Acquirers

Upgrade to ML-based detection
Add machine learning and behavioural analytics on top of existing rules to better identify high-risk patterns.
Integrate device and network intelligence
Use device fingerprinting and graph analysis to uncover fraud rings and mule networks.
Adopt risk-based MFA
Use step-up authentication only when risk scores justify it, preserving user experience.
Continuously tune models
Collaborate with risk and data teams to review model performance by region and product type.

For Merchants and Payment Gateways

Use a dedicated ecommerce credit card fraud prevention layer
Score every order using transactional, device, and behavioural features, not just basic AVS/CVV checks.
Apply 3-D Secure intelligently
Reserve strong step-up flows for high-risk transactions to avoid unnecessary checkout friction.
Monitor cross-channel behaviour
Link web, app, and marketplace channels to spot multi-channel fraud patterns.
Review disputes and chargebacks systematically
Feed confirmed fraud and friendly fraud outcomes back into models and rules.

For deeper tactics focused specifically on layered controls, see: Credit Card Fraud Detection Multi-Layered Security Strategies.

Conclusion

In a world of fast-growing digital payments, credit card fraud detection has to be proactive, not reactive. The strongest programmes blend clear policies, skilled teams, and an adaptive fraud engine that learns from every transaction.

By layering rules, machine learning, behavioural analytics, device intelligence, and network analysis, you can cut fraud losses, reduce false positives, and protect genuine customers without adding unnecessary friction.

Next steps with TrustDecision:
Explore Fraud Management and the ARGUS® Fraud Management Platform, or speak with TrustDecision about a deployment plan tailored to your portfolios and regions.

You can also explore related guidance such as:

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