Building a fraud stack without killing conversion is the strategic implementation of layered security measures that detect and prevent fraudulent transactions while maintaining a seamless checkout experience for legitimate customers, typically achieved through a combination of machine learning models, risk-based authentication, and progressive data collection that reduces false positives from the industry average of 66% to under 10%.
TL;DR Summary: • Aggressive fraud prevention causes up to 66% false positive rates, with authentication issues driving 23% of cart abandonments—understanding these friction points helps you design better systems • Modern fraud stacks combine machine learning (achieving 90-98% accuracy) with rule-based systems and selective manual review to create adaptive, intelligent protection layers • Evaluate each layer using metrics like false positive rates, approval rates, and customer satisfaction scores to identify when security measures over-restrict legitimate buyers • Balance automation with human oversight by using ML for high-volume decisions while reserving manual review for complex cases, reducing review time by up to 98% • Implement low-friction controls like tokenization, progressive profiling, and adaptive authentication to maintain security while improving user experience • Track KPIs including fraud loss per dollar, conversion rates, and dispute win rates to measure the true business impact of your fraud controls • Satisfy compliance requirements (KYC, AML, PSD2) through risk-based approaches that apply stricter verification only to high-risk transactions • Partner with specialized providers like 2Accept to implement proven fraud prevention strategies that protect revenue while preserving customer experience
Quick Tip: Start with behavior-based risk scoring that adapts in real-time rather than static rules—this single change can reduce false positives by over 70% while maintaining the same level of fraud protection.
What Are the Main Challenges in Balancing Fraud Prevention and Conversion Rates?
The main challenges in balancing fraud prevention and conversion rates are false positives, customer friction, and the evolving nature of fraud threats. McKinsey reports that up to two-thirds of declined sales transactions are false positives, while authentication-related issues contribute to approximately 23% of cart abandonments according to MojoAuth. These challenges force businesses to navigate between protecting against fraud losses and maintaining customer satisfaction. Understanding how aggressive fraud prevention impacts conversion, which fraud types affect sales most, and where friction points damage revenue helps businesses optimize their fraud prevention strategy.Why Does Aggressive Fraud Prevention Hurt Conversion?
Aggressive fraud prevention hurts conversion by creating excessive false positives and authentication barriers. According to McKinsey, up to two-thirds of declined sales transactions are false positives. Authentication-related issues contribute to approximately 23% of cart abandonments, as reported by MojoAuth in their authentication studies.Conversion rates vary significantly across markets due to authentication requirements. MultiSafepay found that in some markets, conversion rates for 3DS challenge drop as low as 75%, while more authentication-friendly markets operate close to 90%. The global average acceptance rate of 87% means 13% of payments sent to 3DS are lost, according to Ravelin’s payment acceptance research.
Poor user experience drives abandonment at critical touchpoints. The LexisNexis True Cost of Fraud Study 2025 reveals poor user experience as the primary driver of abandonment at new account creation, affecting 36% of retail and 37% of ecommerce businesses. Sardine’s onboarding research shows 63% of potential new customers never finish signing up, with customers having only a 19-minute window to complete onboarding before abandonment.
Real business impacts demonstrate the cost of overly aggressive fraud prevention. Sift’s case study of Tutory revealed their previous provider flagged 26% of all transactions as fraudulent, resulting in $1 million in lost sales from over 3,000 legitimate purchases wrongly flagged.
What Types of Fraud Are Most Likely to Affect Conversion?
The types of fraud most likely to affect conversion are first-party fraud, real-time payment fraud, and account-based fraud schemes. First-party fraud now represents 36% of all reported fraud in 2024, up from 15% in 2023, according to the LexisNexis Cybercrime Report.The 2025 Global eCommerce Payments & Fraud Report identifies five major threats impacting one-third to half of all merchants:
- Real-time payment fraud
- Refund/policy abuse
- Phishing attacks
- First-party misuse
- Card testing
Alloy’s fraud analysis reveals the distribution of account-based fraud by case volume. Authorized Push Payment (APP) fraud accounts for 22% of cases, making it the most common. Bust-out fraud represents 21% of case volume, while account takeover fraud accounts for 13% of cases.
Nearly all businesses face fraud challenges. The 2025 Global eCommerce Payments & Fraud Report found 98% of merchants experienced one or more types of fraud in the past 12 months. Sift’s transaction analysis revealed loyalty points show a fraud rate of 6.7%, higher than credit and debit cards despite lower volumes. The LexisNexis True Cost of Fraud Study 2025 reports mobile transactions account for 33% of fraud expenses in the US and 41% in Canada.
How Do Customer Friction Points Impact Sales?
Customer friction points impact sales through reduced conversion rates, increased churn, and permanent customer loss. The LexisNexis True Cost of Fraud Study 2025 found 64% of respondents said fraud hurts customer conversion rates, while 63% reported fraud increases customer churn.Registration and authentication barriers drive immediate abandonment. Ping Identity research shows more than 90% of consumers have left a site rather than complete a traditional, cumbersome registration process. Trust issues compound the problem—Sardine found 75% of customers refuse to share data with a company they don’t know if they can trust.
Fraud experiences create lasting negative impacts on customer relationships. Sift’s consumer research reveals 76% of consumers would stop shopping on a site if they have been a victim of payment fraud. McKinsey’s customer experience analysis found 37% of “Detractors”—customers with bad fraud handling experiences—closed their account or significantly decreased usage.
Even fraud warnings create negative emotional responses. McKinsey’s banking research discovered 70% of banking customers who were fraud victims reported feeling anxious, stressed, displeased, or frustrated when warned about potential fraud. These friction points demonstrate how fraud prevention measures, even when successful, can damage customer relationships and reduce lifetime value through poor experience design.
What Elements Make Up an Effective Fraud Stack?
An effective fraud stack combines multiple technologies and approaches to detect and prevent fraud while minimizing impact on legitimate customers. The components range from identity verification and biometric authentication to machine learning models and manual review processes, each serving specific roles in the detection pipeline.Which Technologies Are Essential for a Modern Fraud Stack?
The essential technologies for a modern fraud stack include identity risk solutions, biometric authentication, document verification, tokenization, and adaptive authentication systems. According to an Alloy report, 75% of banks and fintechs plan to invest in identity risk solutions, while 55% are implementing voice, facial, and fingerprint recognition technologies.Tokenization has become particularly critical for payment security. The 2025 Global eCommerce Payments & Fraud Report reveals that 6 in 10 merchants use tokenization to reinforce security, boost authorization rates, and enable convenient experiences. Network tokens are gaining popularity as gateway tokens decline, signaling a shift in how merchants protect payment data.
Authentication technologies form another crucial layer. Strong Customer Authentication (SCA) is used by 40% of merchants according to the 2025 Global eCommerce Payments & Fraud Report. The adaptive authentication suite market demonstrates explosive growth, with LinkedIn data showing the market size estimated at USD 3.2 billion in 2024 and expected to reach USD 10 billion.
Document verification represents a growing priority, with 51% of institutions planning investment in verification software according to Alloy. Nearly 90% of merchants now use compelling evidence to block and reverse fraudulent disputes, up from 83% the previous year, as reported in the 2025 Global eCommerce Payments & Fraud Report.
How Do Machine Learning and Rule-Based Systems Work Together?
Machine learning and rule-based systems work together by combining the adaptability of ML with the predictability of rules to create comprehensive fraud detection. Financial institutions using machine learning report 92% accuracy rates in fraud identification according to Resolve, while AI-driven fraud detection prevented an estimated $25.5 billion in fraud losses worldwide with detection accuracy rates of 90-98% per HyperVerge data.The performance metrics demonstrate ML’s superiority in pattern recognition. Academic research shows Random Forest models achieved 100% accuracy for legitimate transactions and 95.79% accuracy for fraud detection. Artificial neural networks outperformed decision trees with 96.1% precision in fraud detection tasks. Ensemble models deploying Isolation Forests and XGBoost have reduced false positives by 72.6% while simultaneously increasing fraud detection rates.
Scale represents a key differentiator between the two approaches. Fraud.net reports that ML models scale effortlessly to handle millions of transactions per second with minimal manual input, while rules-based systems falter as data volumes grow. The combination works best for compliance checks, rapid alerts, and high-volume transaction environments like e-commerce.
Each system contributes unique strengths to the fraud stack. Rules offer structure and predictability for known fraud patterns and regulatory requirements. ML brings adaptability, speed, and advanced detection capabilities for emerging threats. Together, they create a robust defense that adapts to new threats while maintaining consistent protection against known risks.
What Role Does Manual Review Play in Fraud Prevention?
Manual review plays a critical but declining role in fraud prevention, serving as a final verification layer for high-risk transactions while organizations transition toward automation. The LexisNexis True Cost of Fraud Study 2025 reveals that 41% of North American merchants still depend on manual processes to prevent fraud, with 44% of financial institutions relying mostly or entirely on manual processes.Automation adoption remains surprisingly low across industries. Only 1 in 5 institutions use primarily automated fraud strategies according to the LexisNexis True Cost of Fraud Study 2025. Canadian ecommerce shows just 3% full automation, while US ecommerce reaches only 6% full automation rates.
The benefits of reducing manual review are substantial. Sift case studies demonstrate that Tutory reduced time spent on order reviews from entire days to 30 minutes daily for a single analyst. The same implementation achieved an 83% decrease in manual review efforts through the Sift platform.
Manual processes create significant bottlenecks in financial services. Sardine reports that 9 in 10 asset managers take over a month for manual onboarding, highlighting the operational burden of human-dependent verification. These statistics underscore the opportunity for efficiency gains through selective automation while maintaining manual review for complex or high-value cases.
The evolving fraud stack positions manual review as a specialized function rather than a default process, focusing human expertise on cases that require nuanced judgment while automating routine verifications.
How Can You Assess and Optimize Each Layer of the Fraud Stack?
Assessing and optimizing each layer of your fraud stack requires measuring specific metrics, detecting over-restriction patterns, and testing adjustments systematically. A 2025 LexisNexis study reveals US ecommerce merchants lose $4.61 per $1 of fraud, up 32% from $3.16 in 2022, highlighting the critical need for stack optimization.Financial services face even steeper losses at $5.75 per $1 of fraud according to the 2025 LexisNexis True Cost of Fraud Study. The impact extends beyond direct losses—57% of banks, fintechs, and credit unions lost over $500K in direct fraud losses in 2023, with over 25% losing more than $1 million over 12 months according to Alloy research. These metrics form the baseline for evaluating each layer’s effectiveness while 62% of respondents report increased fraud attempts in consumer accounts.
Which Metrics Help You Evaluate the Effectiveness of Each Layer?
The metrics for evaluating fraud stack effectiveness are fraud loss ratios, approval rates, and dispute win rates. According to the 2025 LexisNexis True Cost of Fraud Study, US ecommerce merchants experience $4.61 in total losses per $1 of fraud, while financial services lose $5.75 per dollar.Key performance indicators include:
- Direct fraud losses (57% of institutions lost over $500K in 2023 per Alloy)
- Order rejection rates (declined significantly per 2025 Global eCommerce Payments Report)
- Dispute win rates (higher for MRC members than non-MRC enterprises)
- Approval rates (85% for PSD2-compliant card transactions per Netcetera)
The 2025 Global eCommerce Payments Report shows order rejection rates declined significantly over the past year, indicating improved accuracy in fraud detection. MRC members achieve higher dispute win rates than non-members, demonstrating the value of standardized metrics. Tracking these indicators across each layer reveals optimization opportunities.
How Do You Detect When a Layer Over-Restricts Good Customers?
Detecting over-restriction requires monitoring false positive rates, customer satisfaction scores, and decision accuracy metrics. The 2025 Global eCommerce Payments Report shows merchants made significant progress cutting false positive rates, with fewer reporting rates above 10%.Customer satisfaction scores provide direct feedback on friction levels. McKinsey research shows scores range from 82 points for excellent fraud handling to -58 points for poor experiences. There are three primary detection methods: monitoring false positive trends, tracking customer satisfaction metrics, and measuring decision accuracy rates.
Success stories demonstrate achievable targets. TransmitSecurity reports a leading US bank achieved a 98% decrease in new account fraud while maintaining customer experience. Tutory achieved 99% decision accuracy with 0.17% chargeback rate by reducing false positives according to Sift case studies. Entrust notes real-time risk scoring ensures trusted users experience frictionless access while suspicious activity triggers stronger verification.
What Are Best Practices for Testing Stack Adjustments Without Risking Revenue?
Best practices for testing stack adjustments are implementing real-time detection capabilities, using champion-challenger testing, and leveraging proven machine learning methods. Experian Academy reports only 27% of businesses can detect fraud in real time, creating significant testing gaps.The 2025 Global eCommerce Payments Report indicates over 90% of merchants use various approaches to boost authorization rates. Academic research identifies XGBoost and Random Forest as top performing methods across 9 fraud detection methods evaluated over 9 datasets using 9 evaluation metrics.
Investment priorities reflect testing focus areas. Alloy reports 33% of institutions plan machine learning investments in the next 12 months, while 26% plan alternative data vendor investments. These technologies enable safer testing through better prediction accuracy and gradual rollouts that minimize revenue risk while optimizing fraud detection capabilities.
How Should You Balance Automation versus Human Oversight in a Fraud Stack?
Balancing automation versus human oversight in a fraud stack requires strategic decisions about when to deploy machine learning, where manual review adds value, and how to mitigate automation risks. Machine learning reduces review queues while adapting to new fraud patterns, but regulatory compliance and complex cases still demand human expertise.When Should Automation Take Precedence Over Manual Checks?
Automation should take precedence over manual checks when handling high-volume transactions, detecting evolving fraud patterns, and reducing review queues. According to Fraud.net research, ML-powered risk scoring greatly reduces review queues by achieving higher accuracy, enabling more good transactions to be auto-approved and more risky ones to be auto-cancelled. Machine learning dynamically adapts to new patterns and identifies risks never encountered before.A 2024 Sardine study reveals that automation reduced manual reviews by 98%. Major ecommerce platforms demonstrate similar success—Krazio Cloud reports a 65% fraud reduction using AI-powered cloud-based detection systems and real-time analytics. Financial institutions achieve comparable results. BioCatch and Alkami document a 100K+ member credit union reducing account takeover losses by $211K in six months using layered fraud detection tools.
Automation excels in scenarios requiring rapid pattern recognition across millions of transactions where human review would create bottlenecks.
What Risks Come with Over-Reliance on Automated Systems?
The risks of over-reliance on automated systems include regulatory non-compliance, AI-generated threats, and context-blind decision-making. A 2024 Sardine report found 60% of fintechs faced regulatory fines over $250,000 due to missed KYC checks while trying to expedite onboarding. The Experian Global Identity & Fraud Report 2024 notes GenAI has accelerated criminal activities while also helping improve fraud prevention strategies.The World Economic Forum cites AI-generated misinformation and disinformation as the second biggest global risk of 2024. Regulatory pressure intensifies as Sift reports 28 U.S. states enacted nearly 60 AI laws by the end of 2024. Businesses must ensure adherence to AI regulations and correct implementation.
Ping Identity research shows not every use case benefits from progressive profiling—compliance-heavy, one-time, or transactional workflows may still require full data collection upfront. These limitations highlight where human judgment remains essential.
How Can Human Analysts Add Value Without Slowing Conversion?
Human analysts add value without slowing conversion through strategic step-up authentication and risk-based tiering. Alloy data shows step-up authentication methods and their adoption rates: phone-centric verification (54%), selfie or liveness test (51%), Knowledge Based Answer (50%), and document verification (48%).Risk-based approaches optimize human resources. McKinsey estimates cited by Sardine indicate high-risk clients usually make up less than 5% of potential new clients. Yet Sardine finds only 29% of asset managers use tiering methodology for KYC as of 2024.
Human oversight prevents costly errors. Sardine documents 2-5% of form mistakes due to miscommunications between employees and new clients. Analysts excel at detecting nuanced fraud patterns automated systems miss while providing customer service during verification processes.
The optimal fraud stack combines automation’s speed with human judgment for edge cases, creating a system that maintains high conversion rates while protecting against sophisticated fraud attempts.
What Are the Most Effective Strategies to Minimize Customer Friction?
The most effective strategies to minimize customer friction combine adaptive authentication technologies with progressive data collection methods. Modern fraud prevention requires balancing security needs against user experience demands through intelligent risk-based approaches. LoginRadius data shows adaptive MFA reduces authentication challenges while maintaining protection levels, directly improving conversion rates.How Can You Streamline Verification While Maintaining Security?
Streamlining verification while maintaining security relies on adaptive multi-factor authentication and progressive profiling techniques. Adaptive MFA provides users fewer authentication challenges overall, improving UX and conversion rates without weakening protection according to LoginRadius research. Progressive profiling reduces friction by collecting only essential customer data upfront, then gathering additional information over time as noted by Ping Identity studies.Behavior-based data collection creates smoother user journeys. There are several effective approaches, such as requesting zip codes after users view location pages, timing document uploads after initial engagement, and deploying verification steps based on transaction risk levels. This method produces more accurate customer profiles while maintaining security standards.
Progressive profiling particularly excels at preserving conversion paths. Ping Identity research confirms delaying heavier data requests until customers fully engage with value propositions creates faster initial conversions. A 2024 Alloy report indicates 51% of organizations plan anti-scam education tool investments to complement these streamlined verification approaches.
What Are Examples of Low-Friction Fraud Controls?
Low-friction fraud controls include mobile wallets, P2P payment apps, and AI-powered risk scoring systems. The 2024 Experian Global Identity & Fraud Report reveals UK mobile wallet usage surged from 54% to 77% over two years. US mobile wallet adoption increased 12% to 73% during the same period.P2P payment applications demonstrate similar widespread adoption patterns. There are multiple indicators of mainstream acceptance: over 8 in 10 UK shoppers embrace P2P apps, matching US adoption rates exceeding 80%, and both markets show continued growth trajectories. These payment methods reduce friction through stored credentials and biometric authentication.
AI-powered risk scoring provides another low-friction control example. TickPick’s implementation with GeekyAnts reduced false declines while recovering significant revenue through intelligent transaction analysis. The system evaluates transactions in real-time without adding customer-facing steps.
How Do Progressive Profiling and Adaptive Authentication Improve Experiences?
Progressive profiling and adaptive authentication improve experiences through real-time risk assessment and contextual verification requirements. Entrust research confirms real-time risk scoring ensures trusted users experience frictionless access while suspicious activity triggers stronger verification measures. This dynamic approach eliminates unnecessary friction for legitimate customers.Customer perception data validates these improvements. McKinsey studies reveal customers perceive true fraud events as moments of truth, with satisfaction scores ranging from 82 points for well-handled incidents to -58 points for poor experiences. TransmitSecurity documented a leading US bank achieving 1300% ROI through improved fraud prevention implementation.
Organizations must consider authentication, fraud management, and customer experience simultaneously rather than individually according to McKinsey analysis. The new approach combines best-practice fraud models with customer experience considerations. This integration strikes optimal balance among loss prevention, customer protection, cost optimization, improved experiences, and new business value creation.
These strategies collectively demonstrate how modern fraud stacks can maintain security while eliminating unnecessary friction points that damage conversion rates.
How Can You Measure the Business Impact of Fraud Controls on Conversion?
Measuring the business impact of fraud controls on conversion requires tracking both fraud reduction metrics and sales performance indicators simultaneously. The balance between security and customer experience directly affects your bottom line through prevented losses and maintained revenue streams.Which KPIs Indicate Both Fraud Reduction and Conversion Success?
The KPIs that indicate both fraud reduction and conversion success are chargeback rates, false positive ratios, approval rates, customer lifetime value, and fraud loss multipliers. According to a 2021 McKinsey report, US fraud losses rose to $5.9 billion, representing a 436% increase compared with 2017. Internet crime losses soared to $6.9 billion in 2021, marking a 392% increase from 2017 levels.Key performance indicators must capture the full scope of fraud impact on your business. There are several critical metrics to monitor, such as fraud detection rate, false decline rate, average order value trends, and customer acquisition cost changes. The 2024 Clearly Payments study revealed US consumers suffered over $12.5 billion in fraud losses, a 25% increase from 2023. The AFP Payments Fraud and Control Survey found 79% of organizations faced fraud attempts in 2024.
Global fraud patterns show significant shifts requiring adjusted measurement approaches. Sift’s 2024 research documented $1 trillion lost globally to scams. The 2025 Global eCommerce Payments & Fraud Report noted fraud rates are down, reversing the multi-year trend of increasing incidence. There are significant declines in first-party misuse, card testing, and triangulation schemes according to the same report.
Success metrics must reflect both immediate wins and long-term business health through comprehensive tracking systems.
How Do You Establish an Ongoing Monitoring Process?
You establish an ongoing monitoring process by implementing real-time dashboards, setting automated alerts for threshold breaches, conducting regular performance reviews, and creating feedback loops between fraud and conversion teams. The 2024 Experian Global Identity & Fraud Report found 70% of UK businesses expect fraud prevention budgets to increase further.Investment priorities reveal monitoring focus areas across markets. According to Experian’s 2024 report, 62% of UK businesses plan to invest in synthetic identity fraud prevention and detection. The same study showed 56% of US businesses express intention to invest in synthetic identity fraud prevention capabilities.
Security leaders prioritize specific threat types in their monitoring frameworks. Sift research indicates 70% of security leaders view account takeover attacks as their greatest organizational concern. Post-purchase fraud impacts 38% of merchants’ ability to manage fraud effectively according to Sift data.
Continuous monitoring requires automated systems combined with human oversight to catch emerging patterns quickly.
What Tools Can Help Visualize the Tradeoff Between Fraud Losses and Lost Sales?
The tools that can help visualize the tradeoff between fraud losses and lost sales are risk-scoring dashboards, conversion funnel analytics, cohort analysis platforms, and ROI calculators. The 2025 LexisNexis True Cost of Fraud Study found 41% of US businesses identify identity verification as a major challenge at new account creation.| Organization | Risk Attribute | Reported Impact or Capability | Source |
| Financial institutions | Emerging identities challenge | 50% | LexisNexis 2025 |
| Large enterprises | Cyber-fraud fusion team adoption | 20% by 2028 | Gartner / Sift |
| ML systems | Anomaly detection capability | Complex patterns | Fraud.net |
| Behavioral analysis | Pattern recognition | Cross-account signals | Fraud.net |
Visualization tools must integrate predictive analytics with historical performance data. Gartner predicts by 2028, 20% of large enterprises will shift to cyber-fraud fusion teams, up from less than 5% today. The merging of fraud prevention and cybersecurity is expected to revolutionize fraud combat approaches according to Sift research.
Machine learning enhances visualization capabilities through pattern recognition. Fraud.net reports ML identifies complex anomalies and subtle signals suggesting fraudulent activity, including suspicious behavioral patterns across accounts.
Effective visualization transforms complex data relationships into actionable business insights that drive strategic decisions about fraud control investments and conversion optimization efforts.
How Are Regulatory and Compliance Requirements Addressed Without Stifling Sales?
Regulatory and compliance requirements create significant challenges for fraud prevention teams, requiring businesses to balance strict legal obligations with maintaining smooth customer experiences. Companies must navigate complex regulations while avoiding excessive friction that drives away legitimate customers.What Compliance Standards Commonly Affect Fraud Stacks?
Compliance standards affecting fraud stacks include Anti-Money Laundering (AML), Know Your Customer (KYC), Payment Services Directive 2 (PSD2), and Payment Card Industry Data Security Standard (PCI DSS). According to a 2023 FlagRight report, firms in EMEA spent $85 billion on AML efforts alone. Compliance costs average approximately 19% of financial firms’ annual revenue, based on FlagRight data. A 2024 TechUK study identified inadequate KYC procedures as the most frequently cited compliance failure. Silent Eight projects that approximately 15% of AML/KYC procedures will be conducted via blockchain-based systems in 2025.How Can You Satisfy KYC, AML, PSD2, or PCI DSS Without Blocking Legitimate Buyers?
Satisfying KYC, AML, PSD2, and PCI DSS requirements without blocking legitimate buyers requires implementing adaptive authentication and risk-based approaches. McKinsey research reveals a fundamental tension exists between controlling fraud and optimizing customer experience, as tighter fraud controls often add friction. Poorly designed authentication experiences have disproportionately negative impact on customer engagement, fraud mitigation, and operational efficiency. The 2025 LexisNexis True Cost of Fraud Study emphasizes that businesses must ensure security while maintaining speed and ease of use. Overly strict fraud prevention can frustrate customers and cause transaction abandonment. The solution involves implementing progressive verification that adjusts requirements based on transaction risk levels.What Role Does Data Privacy Play in the Stack Design?
Data privacy plays a central role in stack design by determining how customer information is collected, processed, and protected throughout the fraud prevention process. Tamas Kadar, CEO and Co-Founder of SEON, notes that businesses face escalating pressure to enhance their fraud protection measures. Businesses are increasingly burdened with the responsibility of protecting customers due to insufficient government support. Greater investment in advanced fraud prevention technologies and strategies is needed, with strong focus on maintaining customer trust and regulatory compliance. Privacy-preserving technologies enable fraud detection while minimizing data exposure and ensuring compliance with regulations such as GDPR and CCPA.How Should You Approach Building a Fraud Stack Without Killing Conversion with 2Accept?
Building a fraud stack without killing conversion requires balancing security with user experience. 2Accept provides merchants with adaptive fraud prevention tools that maintain high conversion rates while protecting against evolving threats. The approach combines machine learning detection, rule-based systems, and streamlined verification processes.Can 2Accept Help You Build a Secure Fraud Stack That Preserves Conversion?
2Accept helps merchants build secure fraud stacks that preserve conversion through intelligent risk assessment and minimal friction controls. The platform addresses the reality that US ecommerce faces 53% of fraud costs tied to online purchases and 30% to mobile channels, according to the LexisNexis True Cost of Fraud Study 2025. Canadian ecommerce merchants lose $4.52 per $1 of fraud based on the same 2025 study.First-party misuse presents unique challenges for conversion-focused fraud prevention. The 2025 Global eCommerce Payments & Fraud Report reveals that 6 in 10 merchants report increasing rates of first-party misuse but significantly fewer saw major spikes. This pattern requires nuanced detection methods that distinguish between legitimate customers and fraudulent behavior without creating unnecessary barriers.
Machine learning models form the core of 2Accept’s fraud detection capabilities. According to Fraud.net research, machine learning models analyze massive datasets to uncover patterns, anomalies, and relationships invisible to rules-based systems. These models adapt to new fraud patterns while maintaining low false positive rates that preserve legitimate transactions.
The platform integrates multiple verification layers that activate based on risk scores rather than applying blanket restrictions. This adaptive approach ensures high-risk transactions receive additional scrutiny while trusted customers experience seamless checkout flows.
What Are the Key Takeaways About Building a Fraud Stack Without Killing Conversion We Covered?
The key takeaways about building fraud stacks without killing conversion center on addressing authentication challenges and leveraging comprehensive data analysis. The LexisNexis True Cost of Fraud Study 2025 identifies that merchants struggle with onboarding processes and ineffective authentication methods. These struggles directly impact conversion rates when legitimate customers abandon transactions due to excessive verification requirements.Identity verification emerges as a critical challenge point. The LexisNexis True Cost of Fraud Study 2025 reports that up to 41% of US businesses identify identity verification as a major challenge at new account creation. This challenge requires solutions that verify identities accurately without creating friction that drives customers away.
Data utilization improvements show positive results across the industry. The 2025 Global eCommerce Payments & Fraud Report indicates more merchants are currently on the latest rules and using the full array of relevant data points. This comprehensive data approach enables better fraud detection accuracy while reducing false positives.
Real-world success demonstrates the effectiveness of balanced fraud prevention. The Sift case study documents how Tutory successfully mitigated an organized fraud attack by identifying common fraud indicators such as multiple emails and IP addresses. This targeted approach stopped fraudsters while maintaining conversion rates for legitimate customers, proving that effective fraud prevention and high conversion rates can coexist.