Friendly Fraud Prevention Guide for Merchants, Banks, and Fintechs

For merchants, banks, and fintechs, friendly fraud is no longer a marginal annoyance — it is one of the most persistent and costly risks in digital payments. It is now the second most common source of fraud attacks faced by merchants (Cybersource, 2024 Global Fraud Report).

Unlike traditional fraud, where stolen credentials are used by third parties, friendly fraud originates with the legitimate cardholder. A purchase is made – and later disputed, sometimes by mistake, sometimes deliberately. Rising chargeback ratios create a double burden: compliance pressure and revenue leakage that eats into margins.

Based on JuicyScore’s work with digital lenders across 40+ countries, we see friendly fraud evolving into a structural risk: it erodes repayment quality in BNPL and microfinance portfolios, drives up the cost of risk, and exposes institutions to tighter oversight from regulators and card schemes.

Industry estimates suggest that at least 75% of all chargebacks can be traced back to friendly fraud – representing billions in lost revenue, dispute fees, and compliance costs. And with the rise of BNPL, digital lending, and subscription models, the problem is accelerating.

This guide outlines what friendly fraud is, how it manifests across industries, and – most importantly for decision-makers – how it impacts profit and loss, compliance thresholds, and long-term business sustainability.

What Is Friendly Fraud?

Friendly fraud – also referred to as chargeback abuse, first-party fraud, or “cyber shoplifting” — occurs when a customer disputes a credit card transaction despite having authorized it.

The motivations vary: from confusion (a forgotten subscription, an unrecognized descriptor) to deliberate abuse (keeping goods without paying). Regardless of intent, the outcome for institutions is the same: revenue loss, higher chargeback ratios, and increased compliance exposure.

Why this matters for risk managers: traditional fraud detection tools were built to stop unauthorized use. They don’t address disputes raised by the customer themselves. Managing friendly fraud requires advanced, context-aware analytics that capture behavior beyond the transaction level.

Why it’s different from true fraud

• True fraud: A criminal uses stolen card data to make purchases without the cardholder’s consent.

• Friendly fraud: The cardholder themselves disputes a legitimate charge, exploiting the chargeback process.

This distinction matters: traditional fraud detection tools are designed to catch unauthorized use, not disputes by the cardholder themselves. Friendly fraud requires more advanced, context-aware solutions.

Examples of Friendly Fraud

Friendly fraud manifests in several patterns, each with measurable financial and compliance consequences for institutions:

Accidental disputes

• Forgotten subscriptions or missed BNPL installments that later trigger chargebacks.

• Misinterpretation of billing descriptors, where customers dispute transactions instead of contacting the merchant.

• In digital lending and microfinance, borrowers may deny receiving digitally disbursed loans, creating portfolio losses and operational risk.

Business impact: These cases inflate chargeback ratios and increase servicing costs, while also driving up non-performing loan metrics.

Deliberate abuse

• Customers intentionally dispute legitimate charges to secure goods or services without payment.

• Exploiting return/refund frameworks by filing chargebacks instead of following official processes.

Business impact: This creates direct revenue leakage, raises the cost of fraud, and erodes profitability as institutions absorb both lost goods and dispute fees.

Household and shared usage disputes

• Transactions made through shared cards or devices that are later denied by the primary account holder.

• In family contexts, unauthorized purchases (e.g., in-app or digital services) disputed after fulfillment.

Business impact: For banks, neobanks, and microfinance providers, these disputes complicate liability, increase operational workload, and undermine trust in digital channels.

Buyer’s remorse and dissatisfaction claims

• Instead of pursuing legitimate return or refund channels, some customers use chargebacks as a shortcut after regretting or being dissatisfied with a purchase.

Business impact: This directly impacts the P&L through lost revenue and dispute fees, skews CAC/LTV ratios as legitimate customers are misclassified as fraud risks, and increases the overall cost of fraud management across the portfolio.

When Chargebacks Escalate: From Isolated Losses to Systemic Risk

For merchants, banks, and fintechs, a chargeback is never just a transaction reversal. It represents lost product or service value, reversed payment, and added dispute fees. At scale, it becomes a structural risk that can threaten entire business models.

The costs compound across several dimensions:

• Direct losses – unrecoverable value of goods, shipping, and fulfillment.

• Chargeback fees – processors and banks levy penalties with every case.

• Ratio pressure – breaching Visa/Mastercard thresholds exposes institutions to monitoring, higher reserves, or even restrictions on payment processing.

• Operational burden – staff resources diverted from growth activities to evidence collection and dispute management.

• Hidden costs – acquiring banks may increase processing fees, tighten compliance controls, or downgrade risk categories.

For fintechs and microfinance organizations in particular, elevated chargeback ratios are not just losses – they are a direct threat to the sustainability of the business model. They undermine portfolio quality, inflate the cost of risk, and weaken profitability metrics such as CAC/LTV.

Why Friendly Fraud Persists

Several systemic and behavioral factors sustain its growth:

• Consumer-first rules – card schemes prioritize customer protection, often at the expense of merchants.

• Low awareness – many consumers do not recognize the downstream business impact of disputes.

• Ease of filing – chargebacks are faster than contacting a merchant.

• Weak issuer validation – many disputes are resolved in favor of the customer with minimal review.

• Behavioral blind spots – customers often do not view disputing their own transaction as fraud.

This mix of behavioral bias and structural rules ensures both accidental disputes and deliberate abuse continue to grow.

How to Prevent Friendly Fraud

Prevention requires a layered strategy that combines communication, education, operational improvements, and analytics.

1. Strengthen Communication

Use clear billing descriptors that reflect the customer’s brand experience.

Send purchase confirmations and real-time delivery updates.

Provide pre-billing reminders for BNPL installments or subscriptions.

2. Improve Transparency

Publish clear refund and return policies.

Offer self-service tools for customers to check transactions.

Provide order tracking to reduce “item not received” disputes.

3. Apply Advanced Analytics

Use machine learning to spot suspicious dispute patterns.

Deploy device intelligence to distinguish trusted vs. untrusted environments.

Track repeat offenders and maintain deny lists where allowed.

4. Educate Customers

BNPL providers can highlight repayment schedules during onboarding.

Banks can send reminders about responsibilities when sharing cards within families.

E-commerce platforms can provide FAQs on billing descriptors and charges.

5. Adopt Tiered Responses

First-time accidental disputes → resolve with clarification and education.

Repeat disputes → flag for closer scrutiny.

High-risk cases → escalate to advanced fraud-prevention systems.

Fighting Back: The Representment Process

When disputes happen, merchants can challenge them through chargeback representment – submitting evidence that the transaction was valid.

Evidence may include:

• Delivery confirmations and tracking numbers.

• Login or device fingerprints showing account holder access.

• Customer service transcripts or chat logs.

• Signed receipts or digital consent records.

Although win rates vary (often only 20–40%), representments recover revenue and discourage opportunistic abuse. Automated chargeback management platforms can streamline this process for larger merchants.

Industry-Specific Challenges

BNPL (Buy Now, Pay Later)

• Forgotten installment payments often result in accidental disputes.

• Higher adoption among younger consumers with limited repayment literacy increases default and dispute risk.

Business impact: elevated chargeback ratios can quickly erode unit economics for BNPL providers, pushing them closer to Visa/Mastercard compliance thresholds.

Digital Banking & Neobanks

• Shared family card usage and recurring subscription billing are frequent sources of disputes.

• Instant dispute processes, while designed for customer satisfaction, increase exposure by limiting investigation time.

Business impact: unresolved chargebacks can drive higher dispute ratios, increase operational overhead, and attract regulatory scrutiny.

Microfinance

• Shared devices and accounts blur transaction ownership, leading to disputes.

• Low digital literacy among borrowers can drive accidental chargebacks or denial of digitally disbursed loans.

Business impact: chargebacks worsen portfolio quality, distort repayment metrics, and inflate the cost of risk — critical in thin-margin microfinance operations.

E-commerce & Cross-Border

• Foreign billing descriptors or currency mismatches trigger suspicion among customers.

• Delivery delays or customs bottlenecks can drive premature “item not received” claims.

Business impact: lost revenue compounds with higher logistics and compliance costs, while reputational damage can limit growth in new markets.

Fintechs (Lenders and Payment Providers)

• Rapid scaling, high transaction volumes, and diverse customer segments expose fintechs to multiple forms of friendly fraud simultaneously.

• Digital-first business models amplify disputes tied to subscription services, instant payouts, or lending disbursements.

Business impact: Elevated dispute ratios directly threaten profitability, increase the cost of risk, undermine investor confidence, and limit access to payment networks. For early-stage fintechs, exceeding compliance thresholds with Visa/Mastercard can halt scaling entirely.

Balancing Fraud Prevention with Customer Experience

An overly aggressive stance risks alienating customers who make honest mistakes, while leniency invites abuse. The solution lies in:

• Clear communication and proactive education.

• Transparent return policies.

• Analytics that can differentiate accidental mistakes from systematic abuse.

Good customer service remains a cornerstone. Prompt responses and reasonable refund policies often prevent disputes from escalating into chargebacks.

Using JuicyScore for Friendly Fraud Prevention

Traditional fraud controls focus narrowly on transactions, often missing disputes that originate with the cardholder. JuicyScore extends protection further, equipping institutions with advanced tools to reduce friendly fraud at scale. Our technology:

• Applies device intelligence to separate trusted from suspicious environments.

• Combines behavioral analytics with scoring models to spot unusual patterns.

• Identifies household-level fraud by analyzing shared device usage and anomalies.

• Delivers accurate detection without reliance on personal data, aligning with global privacy standards.

Case Study: CashExpress Nigeria

CashExpress (operating as CashX in Nigeria) faces one of the toughest lending environments in the world – where smartphones are frequently shared among several users, making it difficult to separate legitimate borrowers from repeat applicants on the same device.

By integrating JuicyScore’s device fingerprinting and behavioral analytics, CashExpress filters out up to 25 percentage points of potential fraud risk in its highest-risk segment. The platform leverages unique device parameters, borrower activity signals, and JuicyScore’s default probability models to block suspicious applications before they reach the underwriting stage.

The impact is clear: when JuicyScore data was temporarily unavailable, default rates increased by 3–5 percentage points. With JuicyScore fully active, CashExpress not only reduces defaults but also strengthens scoring accuracy for credit-invisible clients, where up to half of model variables are powered by JuicyScore’s non-personal data.

As Temitope Adetunji, CEO of CashExpress Nigeria, explains:

👉 Book a demo with the JuicyScore team to learn how your institution can reduce friendly fraud exposure while safeguarding customer trust.

Conclusion: A Proactive and Continuous Strategy

Friendly fraud is growing, costly, and complex. It blurs the line between legitimate customer behavior and abuse, making it one of the hardest risks to manage.

The solution is not a single tool, but a continuous strategy combining:

• Customer communication and education.

• Transparent policies.

• Device and behavioral analytics.

• Tiered dispute handling.

• Collaboration across merchants, issuers, and processors.

Friendly fraud is not going away — but with the right tools and processes, institutions can protect revenue, strengthen compliance, and reduce the cost of risk.

Key Takeaways

• Friendly fraud is one of the costliest risks in digital payments, affecting merchants, banks, BNPL providers, and fintechs alike.

• It can be accidental or intentional – from forgotten subscriptions to deliberate abuse of the chargeback system.

• The financial impact extends beyond chargebacks, including higher processing fees, compliance risks, and reputational harm.

• Traditional fraud detection tools are not enough – distinguishing legitimate customers from abusers requires device intelligence, behavioral analytics, and contextual data.

• A layered strategy is essential: clear communication, transparent policies, advanced analytics, and proactive dispute handling.

• Advanced solutions can help institutions reduce friendly fraud by detecting suspicious device patterns, analyzing household-level activity, and improving overall fraud scoring without relying on personal data.

FAQs

What are the early warning signs of friendly fraud?

Typical red flags include sudden spikes in chargebacks from regular customers, vague or inconsistent dispute reasons, and “item not received” claims even when delivery is confirmed.

What costs can friendly fraud create for my business?

The losses extend beyond chargeback fees. Businesses also absorb the value of goods or services already delivered, shipping expenses, and staff time spent on disputes. In addition, if chargeback ratios exceed thresholds, merchants may face compliance issues, higher fees, or even lose the ability to process card payments.

Can friendly fraud be accidental?

Yes. Accidental friendly fraud happens when customers unintentionally dispute charges — for example, due to forgotten subscriptions, unclear billing, or family members using the same card.

Is friendly fraud illegal?

Yes. Knowingly disputing a legitimate charge is considered fraud and can have legal consequences, even if it is sometimes overlooked or downplayed.

How do you fight friendly fraud chargebacks?

The process is called representment: merchants submit proof such as delivery confirmations, login records, or customer communications to show the transaction was valid. Win rates vary, but it can recover lost revenue and discourage repeat abuse.

Can merchants fully prevent friendly fraud?

Not entirely. Some disputes will always slip through, but merchants can limit exposure with clearer billing practices, proactive customer education, and layered fraud-prevention systems.

How do banks and fintechs detect friendly fraud?

They monitor dispute patterns, apply device intelligence to detect shared or suspicious environments, and use behavioral analytics to spot unusual repayment or purchase activity. Collaboration with merchants strengthens this detection.

How can clear communication and billing transparency reduce friendly fraud?

Clear billing descriptors, pre-billing reminders for subscriptions or installments, and real-time delivery updates reduce confusion. Transparent refund and return policies also encourage customers to resolve issues directly instead of filing chargebacks.

What technology helps reduce friendly fraud across industries like BNPL and microfinance?

Modern solutions combine device and browser analytics, behavioral scoring, and machine learning. These tools highlight suspicious activity, detect shared-device anomalies, and improve fraud scoring accuracy without relying on personal data.