Fraud Prevention RFP Questions Every Enterprise Buyer Must Ask

Ask the right questions now, so you don’t pay for the wrong answers later.

Issuing an RFP for e-commerce fraud detection software is not a procurement exercise — it’s a strategic decision that will shape your company’s risk posture, customer experience, and profitability for years. Yet most organizations rush into the process without first asking the right internal questions. The result is misalignment: solutions that over-decline legitimate transactions, partners whose incentives conflict with your revenue goals, and platforms that address checkout fraud while leaving the rest of the customer lifecycle exposed.

Before you draft your RFP, answer these six questions. They define what success looks like and ensure the partner you select delivers measurable outcomes: higher approval rates, lower fraud losses, and a fraud strategy built for growth.

1. Does Your Fraud Strategy Support Revenue?

Fraud prevention is a revenue function. A fraud strategy aligned to revenue minimizes false positives, scales for growth and peak selling periods, and optimizes payment authorization — rather than reducing fraud losses by over-declining legitimate transactions.

• Minimize false positives. Legitimate transactions flagged as fraudulent are lost sales — full stop. False positives are the enemy of growth, and any partner that reduces fraud by over-declining transactions is solving the wrong problem.
• Scale for growth and peak periods. Black Friday and Cyber Monday are global events now. Your fraud partner needs documented uptime guarantees and performance benchmarks at high volume, not just average-day SLAs.
• Optimize payment authorization. Strong partners apply proven techniques — intelligent routing, best-practice 3-D Secure (3DS) implementation — to maximize approval rates without adding friction for legitimate customers.

What to require in your RFP:

• A detailed explanation of how the partner minimizes false positives and how those metrics are calculated (clarify whether multiple attempts count as a single transaction)
• Evidence of performance during high-volume events, including uptime strategy and system availability guarantees
• Details on payment optimization approaches, specifically intelligent routing and 3DS implementation
• Transaction-level insight access: scores, decline reasons, and historical linkages

2. Are You Addressing Fraud Across the Entire Customer Lifecycle?

Fraud doesn’t start and end at checkout. Third-party fraud — stolen credentials purchased on the dark web — often targets account creation. First-party or friendly fraud — returns abuse, refund manipulation — happens entirely post-purchase. A checkout-only approach leaves both ends of the lifecycle unprotected.

When fraud signals are captured across account creation, login, checkout, and post-purchase, merchants gain strategic flexibility in how and when to act. A merchant might detect elevated risk at login but choose to intervene at purchase — this unpredictability makes it harder for fraudsters to identify which signal triggered scrutiny and adapt around it.

Unified lifecycle data also produces stronger machine learning models. Multi-touchpoint signals are more resilient to evolving fraud patterns than checkout-only data. Fragmented solutions — separate tools for account protection, transaction screening, and returns abuse — create operational inefficiency, incomplete risk views, and multiple integration points that increase your attack surface. The CFO and CISO should be aligned on fraud strategy, not operating in separate silos.

What to require in your RFP:

• Capabilities covering account creation, login, checkout, and post-purchase returns, refunds, and adjustments
• An integrated platform that consolidates all customer events in a single solution
• Evidence of unified data and workflows that support predictive models across the full lifecycle
• Alignment between financial risk and cybersecurity functions

3. What Fraud Model Aligns with Your Business Goals?

Not all fraud vendor models are created equal — and choosing the wrong one creates incentive misalignment that costs you revenue. There are three primary approaches.

The Fraud Liability Shift (FLS) Model

You pay a portion of revenue to the vendor, who assumes total liability for fraud losses. Because 90%+ of transactions are legitimate, you’re effectively paying an insurance premium on good transactions, making FLS disproportionately expensive for most merchants. It also misaligns incentives: the vendor is motivated to minimize chargebacks, and the easiest way to do that is to decline more transactions, including legitimate ones. FLS has a place, but only on your highest-risk transactions.

The Black Box Model

A black box vendor uses machine learning to generate a fraud score but provides little or no visibility into why a decision was made. You can’t adjust inputs or challenge decisions, you typically cannot add custom rules or thresholds, and without integrated chargeback data, the model never receives outcome feedback — it cannot learn from its own errors.

The SLA-Based Partner Model

This model provides minimum acceptance and chargeback rate guarantees aligned to your business goals. If the partner performs better than those guarantees, they earn a profit share; if they underperform, they absorb the loss. Their incentives are directly tied to your outcomes, not to minimizing their own liability.

What to require in your RFP:

• Whether the partner offers models beyond fraud liability shift that align incentives with revenue maximization
• Transparency in decisioning logic, with examples of transaction-level supporting detail
• The ability to configure custom rules and thresholds alongside the ML model
• The extent to which human fraud review is available for borderline (“grey area”) transactions

4. Does Your RFP Integrate Chargeback Management?

Chargebacks are the most direct feedback signal available to your fraud models — which makes chargeback management inseparable from fraud detection strategy. Partners that handle both on the same platform create a closed-loop system that improves performance continuously:

• Accelerated model improvement: Dispute outcomes feed directly into fraud models, enabling real-time updates that make detection more accurate and adaptive
• Stronger fraud signals: Fraud insights inform dispute strategies, and dispute outcomes sharpen fraud signals — the loop compounds over time
• Faster identification of friendly fraud: Integrated chargeback data makes first-party abuse patterns easier to detect and address, reducing unnecessary disputes and improving win rates
• Better overall metrics: This learning cycle reduces false positives, improves customer experience, and keeps fraud strategy ahead of evolving tactics

A strong fraud strategy also reduces fraud-related chargebacks (which are nearly impossible to win), shrinking the denominator and boosting your overall chargeback win rate.

What to require in your RFP:

• Dispute win rate — overall and specifically for clients using combined fraud detection + chargeback management
• Chargeback ratio
• Recovery timelines
• Automated representment workflows
• How dispute outcomes feed back into fraud detection models

5. Does Your Partner Understand Your Vertical?

Fraud patterns vary significantly by industry. A retail fraud model needs to understand gift card abuse and returns fraud. A travel model needs to handle last-minute booking patterns without over-declining legitimate urgent purchases. A gaming or digital goods model faces entirely different velocity and account behavior patterns. A one-size-fits-all approach consistently underperforms on both detection accuracy and false positive rates.

Vertical expertise has two components that must work together: technology — models trained on industry-specific data outperform horizontal models — and people — analysts and fraud practitioners who have worked inside your industry and understand seasonal fraud trends, regulatory nuances, and operational realities that no algorithm derives on its own.

What to require in your RFP:

• Evidence of vertical-specific ML models and how they differ from horizontal models
• Examples of operational insights specific to your industry — seasonality, regulatory compliance, common fraud schemes
• Proof of people expertise: team members with prior experience in your vertical or specialized fraud practitioners
• Case studies or performance benchmarks from clients in your industry
• Reference conversations: ask to speak directly with clients in your vertical about their experience working with the vendor

6. How Strong Is the Partner’s Data Network?

Machine learning performance depends on the size, diversity, and accuracy of the dataset it’s trained on. Partners with narrow datasets struggle to detect emerging fraud patterns; partners with global, multi-vertical consortium networks adapt faster and deliver materially better protection.

The network effect is the mechanism: when one bad actor is detected for one merchant in the network, every other merchant benefits from that signal immediately. This collective intelligence accelerates detection and reduces exposure across the community — the fraud equivalent of herd immunity.

What to require in your RFP:

• Evidence of consortium data sharing and how intelligence flows across merchants in the network
• Breadth and diversity of the data network: geographic coverage, verticals represented, transaction types
• Refresh frequency: how frequently are fraud models updated with new data?
• Partner disclosure questions: How large is your network? How frequently do you refresh data? Do you share intelligence across clients, and if so, how?

The Bottom Line

Selecting a fraud detection partner is a business-critical decision that shapes revenue, risk posture, and customer experience for years. Issuing an RFP without addressing these six questions first risks adopting the wrong strategy — or settling for results that underperform what is possible.

A well-constructed fraud prevention RFP should:

• Align fraud strategy with revenue goals by minimizing false positives, scaling for growth, and optimizing payment authorization
• Require lifecycle coverage that addresses fraud at account creation, login, checkout, and post-purchase — not just at the point of payment
• Integrate chargeback management to create the real-time feedback loop that makes fraud models continuously more accurate
• Demand vertical-specific expertise in both technology and people, so your partner understands the nuances of your industry from day one
• Require a true consortium approach — where fraud spotted at one merchant is blocked across all clients, so you benefit from collective intelligence at scale

When these elements come together, your RFP does more than solicit bids. It sets the foundation for measurable outcomes: higher approval rates, lower fraud losses, improved profitability, and a fraud strategy that is resilient, adaptive, and built for growth.

Accertify helps enterprise merchants across retail/e-commerce, travel, ticketing, hospitality, entertainment & media, quick service restaurants, grocery, and online marketplaces build fraud strategies that say yes to more revenue, more good customers, and more growth without getting burned. Read our white paper.