What is Fraud Transaction Detection AI Agent in eCommerce Payments & Risk?

A Fraud Transaction Detection AI Agent in eCommerce is a specialized system that evaluates every online transaction in real time to detect and prevent fraud while preserving customer approvals. It combines machine learning, graph analytics, rules, device intelligence, and workflow automation to decide whether to approve, decline, or challenge a transaction. In Payments & Risk, it orchestrates fraud prevention across checkout, post-authorization monitoring, chargebacks, and compliance.

1. Core definition and scope

The Fraud Transaction Detection AI Agent is an intelligent decisioning layer embedded in the payments stack that scores risk and triggers the right action per transaction. Its scope spans pre-authorization risk checks, post-authorization monitoring, account protection, and dispute management. It unifies data across identity, device, behavioral signals, payment credentials, and historical patterns to make context-aware decisions with millisecond latency.

2. Key AI components inside the agent

The agent blends supervised models (e.g., gradient boosting, deep learning), graph-based anomaly detection, and rules calibrated to business policies. Ensemble techniques allow it to capture both known fraud patterns and emergent signals. A policy engine maps risk scores to actions, while an explainability module translates complex model outputs into human-readable reasons that auditors, insurers, and analysts can trust.

3. Data inputs the agent consumes

It ingests device fingerprints, IP reputation, geolocation, behavioral biometrics (typing, swiping), payment details, historical orders, issuer response codes, and network signals like 3-D Secure and Riskified/Network tokens. It also pulls merchant- and marketplace-level signals, including seller behavior and inventory anomalies, and optionally third-party data like consortium risk lists. The breadth and freshness of data are critical to accuracy.

4. Decision outputs and actions

For each transaction, the agent produces a risk score and recommended action: approve, decline, route to manual review, or step up authentication (e.g., 3DS challenge, OTP, document verification). It can also adapt checkout flows by offering lower-risk payment methods or deferring shipment for suspicious orders. These actions are logged with reason codes to enable dispute defense and compliance reporting.

5. Governance, transparency, and auditability

The agent maintains decision logs, model versions, feature lineage, and policy changes to support audits and regulatory inquiries. Built-in explainability provides feature importance and reason codes that are comprehensible to fraud ops teams and insurers underwriting payment risk. Strong governance ensures models meet PCI DSS, AML/KYC expectations where relevant, and jurisdictional rules like GDPR and CCPA.

6. Alignment with insurance-grade risk concepts

The agent expresses risk in calibrated terms that connect to insurance metrics, such as expected loss, frequency, and severity. It supports risk transfer programs like chargeback guarantees or merchant insurance by producing consistent, explainable risk scores and outcomes. This enables insurers and reinsurers to price coverage accurately, manage loss ratios, and set clear conditions for indemnification.

7. Deployment models and performance envelope

The agent can be deployed as a SaaS API, private cloud container, or hybrid edge/cloud setup where device checks run at the edge and model scoring occurs in the cloud. Typical real-time decisions target p95 latencies of 100–250 ms, with asynchronous enrichment and learning jobs running out of band. High availability, failover logic, and graceful degradation are designed to avoid conversion-impacting downtime.

Why is Fraud Transaction Detection AI Agent important for eCommerce organizations?

It is critical because it protects revenue by blocking bad transactions while raising legitimate approvals, which directly improves authorization and conversion rates. It also reduces chargebacks, operational costs, and regulatory exposure, enabling trust across customers, payment networks, and insurers. In a competitive market, it differentiates by delivering frictionless yet secure checkout experiences globally.

1. Revenue protection and approval uplift

By accurately distinguishing good from risky transactions, the agent safely approves more legitimate orders, raising bank authorization rates and top-line sales. Rich risk context can justify soft declines resubmission strategies and adaptive authentication to salvage borderline approvals. Even a 1–2% approval uplift can translate into millions in incremental revenue at scale.

2. Chargeback loss and fee reduction

Detecting fraud upstream reduces direct product loss, chargeback fees, and representment costs. The agent can also lower false alarms, which prevents costly cancellations and customer dissatisfaction. For card-not-present merchants, moving chargeback rates below 0.9% and ideally below 0.5% is often the difference between healthy acquirer relationships and monitoring programs.

3. Customer experience and loyalty gains

Frictionless approvals and fewer false declines drive higher customer lifetime value and repeat purchase rates. Smart step-up only when needed minimizes friction for good customers while satisfying compliance requirements like SCA. A strong fraud posture also reassures shoppers their data is safe, boosting trust and brand equity.

4. Compliance, liability shift, and brand protection

The agent supports PSD2/3DS2 orchestration, PCI DSS-aligned data practices, and audit trails for regulator or network inquiries. Where available, exemptions (e.g., low-risk TRA) are applied safely to deliver frictionless flows. Reduced fraud also protects brand reputation and keeps merchants out of card network monitoring programs.

5. Insurance and risk transfer synergy

By making risk measurable and explainable, the agent enables third-party chargeback guarantees and merchant insurance programs. Insurer-grade decision logs and performance consistency allow the risk to be priced, underwritten, and potentially reinsured. This turns volatile fraud losses into manageable, predictable costs.

6. Marketplace, BNPL, and omnichannel needs

Marketplaces face seller fraud, buyer fraud, and collusion; the agent spans onboarding, payouts, and order-level checks. BNPL and wallets introduce new data points and identity risk patterns the agent can leverage. Omnichannel flows like BOPIS (buy online, pick up in-store) benefit from device and identity continuity.

7. Support for cross-border expansion

Cross-border orders carry higher fraud risk due to issuer unfamiliarity and variable data quality. The agent localizes features and thresholds by country, currency, and network behaviors to keep approvals high across regions. This allows merchants to expand internationally without disproportionate fraud pain.

8. Competitive differentiation and margin impact

Merchants that combine high approvals with low fraud rates maintain superior unit economics. The agent’s automation reduces manual review headcount and accelerates order processing. Collectively, these effects strengthen contribution margins and customer acquisition efficiency.

How does Fraud Transaction Detection AI Agent work within eCommerce workflows?

It operates as a real-time decisioning layer within the checkout and payments flow, scoring transactions pre-authorization and orchestrating authentication, approvals, or declines. It also monitors post-authorization activity, triages chargebacks, and continuously retrains models from outcomes. Human-in-the-loop review and policy management ensure oversight and adaptability.

1. Real-time scoring at checkout

At checkout, the agent receives transaction, identity, device, and behavioral signals and returns a score and action in milliseconds. Feature engineering transforms raw data into high-signal attributes like velocity, graph linkages, and behavioral anomalies. The result is a risk-informed decision that balances acceptance and protection.

2. Adaptive authentication orchestration

When risk is borderline, the agent triggers step-up such as 3DS challenge, OTP, document or selfie verification, or out-of-band issuer checks. It selects the lightest-weight control likely to convert, meeting SCA requirements with minimal friction. This reduces declines while satisfying regulatory rules.

3. Post-authorization monitoring and velocity controls

Fraud patterns can emerge after authorization through escalated behavior or coordinated attacks. The agent runs velocity checks across accounts, devices, BINs, and IPs to spot and stop mule activity and triangulation scams. Suspicious orders can be flagged for manual review or shipment holds.

4. Chargeback and dispute workflow integration

When chargebacks occur, the agent’s decision and context logs feed representment packages with evidence and reason codes. It automates case assignment, evidence compilation, and submission deadlines. Insights from outcomes train models to reduce similar future losses.

5. Continuous learning and model retraining

The agent retrains models on fresh labels from approvals, declines, and chargeback results, using drift detection to schedule updates. Offline A/B evaluation and shadow mode protect against regressions before production promotion. Feature stores ensure consistent training-serving parity.

6. Human-in-the-loop review and playbooks

High-risk or high-value orders can be routed to analysts with pre-populated evidence and explainability. Playbooks guide analysts on when to approve, decline, or request more information, ensuring consistent decisions. Analyst feedback loops improve model performance and policy tuning.

7. Marketplace onboarding and payout checks

For marketplaces, the agent extends to seller onboarding, KYC/KYB verification, and payout risk control. It detects synthetic identities, shell companies, and collusive behaviors using graph link analysis. Payout holds and step-up verification protect the platform’s financial ecosystem.

8. Collaboration with issuers, acquirers, and networks

The agent leverages network tokens, issuer response insights, and directory server data to improve decision quality. It can signal issuer risk context to support approvals on the banking side. This two-way intelligence reduces soft declines and reattempts.

What benefits does Fraud Transaction Detection AI Agent deliver to businesses and end users?

It delivers higher approval rates, lower fraud losses, reduced operational costs, and better customer experiences. It also improves compliance readiness and enables insurance-backed guarantees, making risk more predictable. End users enjoy fast, secure checkouts with fewer false declines.

1. Approval rate uplift and conversion gain

Approval rates rise as the agent reduces issuer-initiated soft declines via better risk evidence and routing. Merchants often see 1–5% uplift depending on baseline, geography, and category. Conversion gains compound across marketing channels, lowering acquisition costs.

2. Fraud loss reduction and stability

Supervised learning and graph analytics detect stolen credentials, triangulation fraud, and mule networks that rules alone miss. This reduces direct product loss and downstream chargebacks, stabilizing P&L. Predictable risk fosters aggressive yet safe growth strategies.

3. Operational efficiency and scalability

Automating low-value reviews and prioritizing complex cases enables smaller teams to manage larger order volumes. The agent’s dashboards and alerts streamline workflows and SLA compliance. Efficiency gains translate into lower cost per order reviewed.

4. Better customer experience and loyalty

Good customers pass with minimal friction; risky customers encounter targeted step-up instead of blanket friction. Lower false decline rates reduce frustration and abandonment. Trust built at checkout supports lasting relationships and higher average order value.

5. Risk financing and insurance enablement

Explainable, consistent decisions make it feasible to secure chargeback guarantees or merchant insurance. Insurer alignment reduces capital reserves needed for fraud losses and smooths earnings. This is especially valuable for high-growth or seasonal merchants.

6. Data network effects and continuous improvement

As volumes grow, the agent learns from more patterns and enriches features with network intelligence. Feedback cycles from disputes and issuer responses sharpen precision. Performance improves over time without proportional increases in headcount.

7. Regulatory readiness and audit confidence

Comprehensive logging, explainability, and policy governance simplify audits and regulator queries. The agent enforces SCA logic and applies exemptions safely, reducing noncompliance risk. Cross-border compliance support accelerates international expansion.

8. Ecosystem trust across partners

Payment service providers, acquirers, and issuers favor merchants with low fraud and robust evidence. Greater trust can mean better pricing, priority routing, and collaborative problem solving. The benefits cascade through the entire payment value chain.

How does Fraud Transaction Detection AI Agent integrate with existing eCommerce systems and processes?

It integrates via APIs, SDKs, and webhooks with payment gateways, processors, order management systems, CDPs, and identity vendors. A phased rollout starting in shadow mode minimizes risk, while connectors accelerate deployment. Data governance and model ops practices ensure reliability and compliance.

1. Payment gateways, processors, and orchestration layers

The agent sits before or within payment orchestration, scoring transactions before authorizations are submitted. It supports multi-gateway strategies and can advise on routing to the acquirer most likely to approve. Webhooks and callbacks update order states after issuer responses.

2. Order management systems, carts, and OMS/WMS

Cart and OMS integrations pass order, item, and fulfillment data to the agent, including shipping speed and pickup options. The agent can place holds on suspicious orders or request fulfillment delays. Warehouse systems receive clear signals to release or pause shipments.

3. Device intelligence and identity verification vendors

SDKs capture device fingerprints and behavioral biometrics at checkout, enhancing risk models. The agent orchestrates third-party identity checks such as document verification or databases for KYB/KYC. Vendor results feed back into the risk score in real time.

4. Data lakes, CDPs, and analytics platforms

The agent pushes decisions, scores, and features to data lakes for analytics and BI. CDP integration supports personalization and remarketing strategies for recovered approvals. Unified data pipelines enable cross-functional visibility across risk, marketing, and finance.

5. SIEM, SOC, and security operations

Risk anomalies can be streamed to SIEM tools for correlation with broader security events like account credential stuffing. Joint playbooks align fraud ops and security teams on incident response. This reduces siloed blind spots across account and transaction layers.

6. Model operations, feature stores, and CI/CD

MLOps pipelines manage model training, validation, deployment, and rollback with guardrails. Feature stores standardize feature computation between training and serving to avoid skew. Canary releases and dark launches de-risk production changes.

7. Insurance partners and underwriters

Standardized decision logs and risk KPIs are shared with insurers for underwriting and claims adjudication. The agent tags transactions covered by guarantees and tracks performance to policy terms. This tightens alignment between operational risk and risk transfer.

8. Change management and rollout planning

Start in observe-only mode, then move to partial enforcement with manual review, and finally full enforcement once KPIs stabilize. Training for fraud ops and support teams ensures consistent handling of new workflows. Governance committees oversee policy updates and exception handling.

What measurable business outcomes can organizations expect from Fraud Transaction Detection AI Agent?

Organizations can expect higher approval rates, lower fraud and chargeback costs, reduced manual review, and faster order processing. These translate into improved revenue, margins, and customer satisfaction, with clear KPIs and ROI within quarters. Structured experimentation validates impact and guides optimization.

1. Core KPIs to track

Monitor approval rate, authorization rate, chargeback rate, fraud rate, false positive rate, review rate, and time to decision. Business KPIs like conversion, AOV, CAC/LTV, and contribution margin capture downstream effects. Audit KPIs include explainability coverage and decision log completeness.

2. Baseline, A/B testing, and shadow mode

Establish a clean baseline before deployment, then run A/B tests to isolate impact. Shadow mode allows scoring without enforcement to compare decisions safely. Statistical rigor avoids confounding factors like seasonality or channel shifts.

3. Financial impact modeling

Model the relationship between approval uplift, fraud reduction, and contribution margin. Include operational savings from reduced reviews and chargeback management. Scenario analysis accounts for regional mixes, product lines, and network behaviors.

4. Operational and SLA metrics

Track average handling time for reviews, case backlog, SLA adherence, and analyst accuracy. Measure p95/p99 latency and uptime to ensure checkout performance. Alerting on drift and anomaly detection prevents silent degradations.

5. Customer experience and satisfaction

Measure false decline rate, checkout abandonment, and post-purchase NPS/CSAT. Fewer manual interventions and step-ups improve overall satisfaction. Communication around security increases perceived trust without increasing friction.

6. Compliance and audit outcomes

Audit cycle time, exception rates, and regulator findings should improve with better governance. SCA compliance rates and exemption success rates indicate effective orchestration. Clean audit trails reduce costly remediation.

7. Insurance and risk transfer metrics

For merchants leveraging guarantees or insurance, track covered volume, claim rate, denial reasons, and loss ratio. Stable or improving loss ratios indicate sustainable programs. Data quality and explainability directly affect coverage terms and premiums.

8. Example ROI scenarios

A mid-market retailer could see a 2% approval uplift and 30% chargeback reduction, yielding 150–300 bps margin improvement. A marketplace may cut manual review by 50% and reduce payout fraud by 40%, lowering operational costs and partner disputes. Payback periods of 3–9 months are common when deployed at scale.

What are the most common use cases of Fraud Transaction Detection AI Agent in eCommerce Payments & Risk?

Common use cases span card-not-present checkout fraud prevention, account takeover, promotion abuse, marketplace onboarding and payouts, BNPL risk checks, cross-border fraud, first-party misuse, and returns/refund abuse. The agent adapts models and policies per use case while maintaining unified governance. This breadth allows a single platform to defend multiple attack surfaces.

1. Card-not-present checkout protection

The agent scores CNP transactions using device, behavioral, and payment features to block stolen card use. It applies adaptive authentication and issuer collaboration to recover soft declines. This is the cornerstone of eCommerce fraud control.

2. Account takeover (ATO) detection

By monitoring login behavior, device changes, and anomalous session flows, the agent flags likely ATOs. It can trigger step-up reauthentication or session termination. Preventing ATO protects stored payment methods and loyalty balances.

3. Promotion and loyalty abuse

The agent identifies coupon stacking, new-account farms, and resell arbitrage via velocity and graph analytics. It enforces policy limits without hurting legitimate promotional use. This preserves marketing ROI and customer fairness.

4. Marketplace seller vetting and payout risk

KYB/KYC checks, document verification, and graph link analysis detect synthetic sellers and collusive rings. Payout holds and incremental verification reduce exposure. Healthy seller ecosystems improve buyer trust and platform growth.

5. BNPL and alternative payment risk

BNPL introduces deferred payment and identity risk; the agent enriches decisions with credit-like features and soft checks. Wallets and A2A payments benefit from behavioral and device continuity. Tailored policies maintain acceptance while controlling loss.

6. Cross-border and high-risk geographies

Different device norms, IP ranges, and issuer behaviors drive regional risk variation. The agent localizes thresholds and model segments to maintain performance abroad. Cross-border acceptance increases with controlled exposure.

7. Friendly fraud and first-party misuse

The agent flags patterns of serial disputes, refund abuse, and post-delivery claims inconsistent with history. Evidence packs support representment and deter abuse. Policy guardrails reduce friction for genuine customer service issues.

8. Returns, refund, and inventory fraud

Link analysis uncovers fraud rings exploiting return policies or inventory substitution. Integration with WMS and logistics data identifies anomalies like address reuse and reshipment. Tighter controls improve net margin on free-returns programs.

How does Fraud Transaction Detection AI Agent improve decision-making in eCommerce?

It improves decision-making by fusing diverse signals into calibrated risk scores, providing transparent explanations, and recommending optimal next best actions. Continuous experimentation and human feedback refine policies. This results in consistent, faster, and more accurate risk decisions across the lifecycle.

1. Rich feature engineering and graph context

The agent constructs features such as device-identity consistency, time-of-day risk, BIN-country alignment, and shared entity graphs. Graph embeddings surface collusion and synthetic identity rings. These features boost recall without inflating false positives.

2. Ensemble modeling and AutoML

Combining gradient-boosting, neural nets, and graph models captures different fraud behaviors. AutoML searches hyperparameters and segments for the best fit per merchant or region. Ensembles are calibrated to produce reliable probabilities for policy thresholds.

3. Counterfactuals and policy optimization

Counterfactual analysis estimates outcome changes if different actions were taken (e.g., challenge vs. approve). This informs policy thresholds and step-up strategies to maximize approvals at fixed loss. Off-policy evaluation drives data-driven governance.

4. Explainable AI and analyst trust

SHAP-like explanations and reason codes clarify why a decision was taken. Analysts and auditors can see top features and evidence trails, improving trust and training quality. Transparent decisions shorten manual review times.

5. Human-in-the-loop learning

Analyst overrides and justifications feed back into training datasets. Disagreement analysis highlights where models need new features or segments. This tight loop improves both precision and recall.

6. Decision playbooks and orchestration

Codified playbooks tie risk bands to actions like 3DS routing, document checks, or shipping holds. Playbooks encode business risk appetite and regional constraints. Orchestration ensures consistency across channels.

7. Data quality monitoring and drift control

Automated checks catch missing features, schema changes, or delayed feeds. Drift monitors watch distributions and model performance, triggering retraining. Early detection prevents silent performance decay.

8. Continuous experimentation and governance

A/B frameworks test policy changes, new features, and vendor integrations. Governance councils approve changes and review KPI impacts. This disciplined approach sustains performance over time.

What limitations, risks, or considerations should organizations evaluate before adopting Fraud Transaction Detection AI Agent?

Key considerations include data privacy and consent, fairness and bias, false positive impacts, adversarial evasion, latency constraints, vendor lock-in, and global regulatory differences. Insurance programs carry terms and exclusions that must be understood. A thoughtful rollout plan and governance mitigate these risks.

Collecting device and behavioral data requires clear notice and appropriate legal basis under GDPR/CCPA. Data minimization and retention policies reduce exposure. Cross-border data transfers need safeguards like SCCs.

2. Fairness, bias, and disparate impact

Models can inadvertently disadvantage certain groups if proxies for protected attributes creep in. Fairness testing and bias mitigation are essential. Policy guardrails prevent overzealous declines on thin-file or cross-border shoppers.

3. False positives and revenue loss

Overly conservative thresholds hurt approvals and customer satisfaction. Measure and manage false positive rates explicitly, not just fraud catch. Provide appeal and remediation paths for good customers.

4. Adversarial attacks and model drift

Fraudsters probe systems with low-value transactions to learn thresholds and features. Rotate features, use randomized challenges, and monitor for probing patterns. Drift detection and rapid retraining keep models current.

5. Latency, resilience, and SLOs

Risk decisions must return within tight latency budgets; retries and fallbacks should not block checkout. Implement circuit breakers and cached fallbacks during vendor outages. Observability ensures rapid incident response.

6. Vendor lock-in and portability

Proprietary features and models can hinder migration. Favor open interfaces, exportable decision logs, and standardized data schemas. Avoid deep entanglement without clear switching strategies.

7. Regulatory fragmentation and compliance

PSD2/SCA in the EU, differing 3DS rules by network, and country-specific privacy laws demand localized policies. Keep current with network bulletins and acquirer requirements. Document decisions for audit readiness.

8. Insurance coverage caveats and claims handling

Chargeback guarantees and insurance come with exclusions, caps, and evidence requirements. Ensure operational capability to meet documentation and timing requirements. Misalignment can void coverage when it’s needed most.

What is the future outlook of Fraud Transaction Detection AI Agent in the eCommerce ecosystem?

The future includes consortium data sharing, privacy-preserving learning, real-time payments fraud defense, and autonomous agents that self-tune policies. GenAI will augment fraud ops, while embedded insurance and dynamic pricing align incentives. Standards in digital identity and responsible AI will shape adoption.

1. Consortium and network data models

Merchants and PSPs will share anonymized signals to detect cross-merchant fraud earlier. Network-led risk services will enrich merchant models. Collective intelligence raises the bar against organized rings.

2. Real-time and account-to-account payment defense

As RTP and A2A methods grow, irrevocable payment fraud risks rise. The agent will expand to pre-transaction identity proofing and payee verification. Rapid risk calls to banks and overlays will become standard.

3. Privacy-preserving ML and federated learning

Federated learning and secure multiparty computation will enable cross-entity learning without sharing raw data. Differential privacy helps meet regulatory expectations. This balances performance with compliance.

4. GenAI copilots for fraud analysts

Natural language interfaces will summarize cases, draft representments, and explain decisions on demand. GenAI will also simulate adversarial behaviors to harden defenses. Human analysts become supervisors of AI-first workflows.

5. Embedded insurance and dynamic risk pricing

Risk scores will directly inform premiums and coverage limits in real time. Merchants can select risk/acceptance trade-offs with transparent pricing. This tightens alignment among merchants, PSPs, and insurers.

6. Global digital identity standards

Adoption of verifiable credentials, passkeys, and reusable KYC reduces identity fraud at source. The agent will integrate these standards to streamline authentication. Customer experience improves as friction becomes smarter, not heavier.

7. Autonomous, self-tuning policy agents

Reinforcement learning and policy optimization will enable policies that adapt automatically to shifting fraud patterns. Guardrails ensure safety and compliance. Human oversight focuses on strategy and exception governance.

8. Responsible AI, ESG, and trust

Stakeholders will demand fairness audits, energy efficiency metrics, and transparent governance. Demonstrable responsibility becomes a competitive advantage. Trust, not just technology, differentiates leaders.

FAQs

1. What data does the Fraud Transaction Detection AI Agent need to score transactions?

It uses device fingerprints, IP/geolocation, behavioral biometrics, payment details, historical orders, issuer responses, and third-party risk signals. More diverse, high-quality data improves accuracy and reduces false positives.

2. How fast does the agent return a decision at checkout?

Typical production targets are 100–250 ms at p95, including feature computation and model scoring. Fallback logic ensures checkout resilience if external vendors slow down.

3. Will the agent increase declines and hurt conversion?

When configured correctly, it usually raises approvals by safely distinguishing good orders from risky ones. Adaptive authentication and issuer collaboration recover borderline approvals without excessive friction.

4. How does the agent support PSD2 SCA and 3-D Secure?

It orchestrates exemptions like TRA where eligible, and triggers 3DS challenges only when necessary. This meets regulatory requirements while minimizing customer friction and preserving conversions.

5. Can it handle BNPL, wallets, and account-to-account payments?

Yes. The agent adapts features and policies to each method, leveraging identity, device continuity, and behavioral context. It assesses deferred payment risk and payee verification for A2A flows.

6. What KPIs should we track to measure success?

Track approval rate, authorization rate, chargeback and fraud rates, false positive rate, review rate, latency, and uptime. Include CX metrics like abandonment and NPS, plus audit readiness and explainability coverage.

7. How do we start a low-risk pilot?

Begin in shadow mode to score without enforcement, validate accuracy, and calibrate policies. Move to A/B testing with partial enforcement, then full rollout once KPIs and error budgets are met.

8. How does the agent enable insurance or chargeback guarantees?

It produces consistent, explainable risk decisions and evidence logs that insurers require to underwrite and settle claims. This supports chargeback guarantees and merchant insurance with predictable loss ratios.

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