Ecommerce Fraud Prevention: Best Practices & Trends in 2025

Ecommerce is growing fast and so is fraud. Online stores now face smarter scams, more complex attacks, and bigger losses. According to Juniper Research, global ecommerce fraud could reach $54 billion by 2025. And it’s not just big brands anymore but small and mid-sized stores are being targeted too.

Imagine you launch an online store selling luxury products. Orders come in quickly, and things look great. But soon, you notice something odd, multiple big orders from different names, but with similar email styles and shipping addresses. A week later, chargebacks start coming in and soon you realise all purchases were done through stolen cards. You lose the products, pay chargeback fees, and risk getting flagged by your payment processor. 

This is a common example of card-not-present (CNP) fraud  and it’s just one type. Today, fraud also includes fake accounts, account takeovers, refund abuse, loyalty scams, and even real customers misusing return policies (friendly fraud).

With more online payments, mobile shopping, and Buy Now, Pay Later (BNPL) options, fraud is getting harder to catch. And beyond just money loss, it damages your customer’s trust and can increase your support and payment processing costs.

From Reactive Defense to Proactive Prevention 

Traditionally, many online businesses reacted to fraud after it happened. That approach is no longer enough where you are continuously: 

• Waiting for chargebacks to pile up
• Blocking entire IP ranges or countries
• Relying only on manual reviews

These methods are slow, expensive, and hurt real customers too.

Modern ecommerce fraud prevention is proactive. It uses:

• Machine learning to spot patterns early where the system learns from past fraud attempts and picks up on suspicious patterns before fraud happens.  A customer places an order using a new account, shipping to an unfamiliar country, and uses an email like “[email protected]”. Machine learning notices this matches patterns from past fraud cases — so it flags or blocks the order before you ship.

• Behavior analysis to flag risky activity in real time the system watches how people behave on your site  like how fast they click, scroll, or fill out forms and detects unnatural or suspicious actions.
• Device fingerprinting to detect bots and multi-account abuse where each device has a unique fingerprint (like browser type, screen size, settings). This helps spot if the same person is using multiple fake accounts or automating actions.  You notice 10 different “customers” creating accounts within 30 minutes but all from  the same device fingerprint. Even though the names and emails are different, it’s clearly one person trying to abuse your new user discount or loyalty program. The system blocks them automatically.

The goal is ecommerce fraud prevention before it impacts your revenue or reputation  without hurting the customer experience.

What will this guide cover?

This guide is made for ecommerce leaders, security teams, and decision-makers who want to level up the ecommerce fraud prevention strategy. 

It will help you to:

• Understand the real-world impact of modern ecommerce fraud
• Learn how to spot early warning signs in customer behavior
• Explore the latest fraud detection tools and how to evaluate them
• Build a future-ready fraud prevention strategy
  

We’ll break down fraud trends, tools, and use cases in simple terms, always focusing on what you need to know before investing in an ecommerce fraud prevention solution.

So, Let’s get started.

What Qualifies as Ecommerce Fraud Today?

Any time someone uses false information, steals payment details, or abuses store policies to get something for free or cheat the system it’s ecommerce fraud.

This type of fraud hurts your sales, increases chargebacks, and can damage your brand’s reputation.

Key Categories of Ecommerce Fraud:

1. Payment Fraud (Stolen Card Use)

Someone uses a stolen credit or debit card to make a purchase.
Example: A fraudster buys a ₹20,000 handbag in your store with a stolen card. Days later, the real cardholder files a dispute  and you lose the money and the product.

2. Account Takeover (ATO)

A scammer breaks into a real customer’s account using stolen login info.
Example: A fraudster logs into a customer’s account, changes the shipping address, and places an order using the saved payment method — all without the customer knowing.

3. Friendly Fraud / Chargebacks

A real customer makes a purchase  then falsely claims it was unauthorized to get a refund from their bank. Example: Someone buys headphones, receives them, and then files a chargeback claiming “I never got the product.” You lose the money and the item.

4. Loyalty & Promo Abuse

Scammers abuse reward programs, promo codes, or first-time user offers to get discounts or freebies multiple times. Example: One person creates 10 fake accounts to use your “20% off first purchase” coupon again and again costing your business real money.

5. Affiliate Fraud & Fake Traffic

A dishonest affiliate partner sends fake clicks, bots, or fake leads just to earn commission.
Example: You run an affiliate program. One partner uses bots to generate 5,000 fake clicks and demands payment for “leads” that never turned into real customers.

6. Refund & Return Abuse

Someone lies or bends return policies to get their money back unfairly.
Example: A customer orders a dress, wears it to an event, then returns it saying “it didn’t fit.” Or worse, they return an empty box and claim the item was defective.

Each of these types of fraud chips away at your profits, trust, and team’s time. That’s why understanding them is the first step toward prevention.

Why Ecommerce Fraud Is Harder to Stop Now

Fraud used to be easier to spot a strange IP address, a fake-looking email, or a bulk order from another country. But in 2025, fraudsters are smarter, faster, and harder to detect than ever before. Here’s why:

1. AI-Powered Fraudsters & Bots

Fraudsters now use AI tools and bots to carry out attacks that look almost human. These bots can fill out forms, mimic shopping behavior, and even bypass basic security checks.

Example:
A bot uses stolen credit cards to test small transactions on your store and all while pretending to be a normal customer, clicking and scrolling like a real person.

2.Omnichannel Surfaces: Web, Mobile, Marketplaces

Omnichannel ecommerce has become an essential part of the  Customers shop from multiple places — your website, mobile app, Instagram store, or marketplaces like Amazon. Fraud can happen on any of these channels, making it harder to monitor everything at once.

Example:
A fraudster creates a fake account on your mobile app to claim a discount, then uses a different email on your website to do the same abusing your promotion from two different channels.

 3. Growing Complexity in Payment Flows

With the rise of Buy Now, Pay Later (BNPL), wallets, international cards, and cryptocurrencies, there are now more payment methods and more places for fraud to sneak in.  A scammer uses a stolen card to make a BNPL purchase and  your store ships the item, but the payment provider later blocks the transaction. You lose both the item and the payment.

4. Blurring Lines Between Legitimate & Malicious Behavior

Not all fraud is black and white. Some users behave suspiciously, but not illegally  while others act like real customers but have bad intentions. For instance, a customer makes a large order using valid details, then claims the item never arrived and demands a refund even though it was delivered. It’s hard to tell whether they’re lying or there was a real issue.

In short,  today’s fraud is faster, more intelligent, and often looks “normal” which is why ecommerce businesses need smarter tools that can detect hidden risks without blocking good customers.

Core pillars of eCommerce fraud prevention

There are four core pillars of eCommerce fraud prevention that are:

1. Identity Intelligence
2. Transactional Risk scoring
3. Real-Time Decisioning
4. Feedback loops

1. Identity Intelligence

Identity intelligence helps you understand who is really behind a transaction, not just what email or card they used, but how they behave, what device they’re using, and whether their digital identity is trustworthy.

It’s like doing a background check in real-time, without slowing down the customer. 

Below are the important aspects of identity intelligence:

1.1 Behavioral Biometrics

This looks at how a user interacts with your site  like how they type, move the mouse, scroll, or tap. These tiny patterns are hard for bots or fraudsters to fake.

Example:
A real customer types smoothly and naturally. A bot, on the other hand, may paste info instantly or move the mouse in perfect lines. The system flags this as suspicious behavior.

 1.2. Device Fingerprinting

Every device (laptop, phone, tablet) has a unique “fingerprint” made up of settings like screen size, browser type, time zone, and plugins. Fraud detection tools track these to spot risky patterns.

Example:
Ten different accounts are created from devices with the same fingerprint  meaning it’s likely one person trying to game your new-user promo.

 1.3. Email, Phone, and IP Reputation

Fraud tools check whether the email, phone number, or IP address has a bad history such as being used in past scams, spam, or chargebacks.

Example:
A user signs up with an email that was used in a fraud attack last month. Even if everything else looks fine, the system can warn you or block the transaction based on reputation data.

Together, these tools give you a 360-degree view of the user’s identity helping you block bad actors while letting real customers shop smoothly.

2. Transactional Risk Scoring

Transactional risk scoring is like a fraud “credit score” for every purchase. It quickly checks dozens of signals to decide whether a transaction looks safe or suspicious  and assigns a risk score. Higher scores = higher fraud risk.

These scores help you:

• Automatically approve good orders
• Flag or block risky ones for manual review

2.1 Velocity Checks

Velocity checks plays an integral role in ecommerce fraud prevention where it monitors how quickly a user performs certain actions like signing up, placing orders, or entering payment details. For example, if your website uses a fraud prevention solution and a user creates three accounts and places five orders within just 10 minutes, all using different email addresses but the same shipping address, that behavior appears suspicious. It’s unusually fast and likely automated, so the system raises a red flag to alert you of potential fraud.

2.2 Geolocation Validation

This checks if the user’s physical location (based on IP address) makes sense compared to their shipping or billing info. For example, a customer claims to be in Mumbai, but their IP shows they’re logging in from Russia — and they’re shipping to Dubai. That mismatch could indicate a fraudster using stolen card details.

 2.3. Payment Method Anomalies

This looks for unusual behavior related to payment, like mismatched card data, failed attempts, or rare payment types.

Example:
A customer tries 4 different cards within a minute, all of which fail except the last one. This suggests someone is testing stolen cards so the final “successful” transaction may still be risky.

So, transactional risk scoring helps you catch shady transactions before they happen, without slowing down trusted customers. It works best when combined with identity intelligence.

3. Real-Time Decisioning

Once a fraud detection system gathers data about a user and a transaction, the next step is to decide: Is this transaction safe, suspicious, or clearly fraudulent?

 Here’s how different types of decisions are made:

Fraud prevention tools use two main approaches: rule-based and AI-based decisions.

3.1 Rule-based systems vs AI-based system

Rule-based systems for ecommerce fraud management follow fixed instructions like “block any order over ₹10,000 from outside India. These are simple and easy to set up, but can miss smarter fraud or block good customers by mistake.

AI-based systems, on the other hand, learn from patterns over time. They can spot more complex behavior  like someone using a new device, but with an old saved address, and making a large purchase late at night. While that may not break a specific rule, AI may still flag it as risky.

For instance, a fraudster may bypass basic rules by using a local IP and a real-looking email. A rule-based system might miss this — but AI can catch it by noticing subtle behavior patterns that don’t feel “normal.”

3.2. Threshold Tuning

Threshold tuning means adjusting the system’s sensitivity — deciding how risky a transaction must be before it’s flagged, reviewed, or blocked.

If your threshold is too strict, you might block real customers who are just doing things a bit differently. If it’s too loose, fraud might slip through.

Let’s say your system assigns risk scores from 0 to 100. You decide to automatically block anything over 80. But after reviewing your data, you notice that many genuine customers are scoring 85 just because they’re using international cards. In this case, you might raise the threshold to 90 to reduce false positives and improve the user experience.

3.3. Manual Review and Overrides

Not all decisions can be made by a system; some transactions land in a gray area. That’s where manual review comes in. Your team (or a fraud analyst) checks flagged transactions and decides whether to approve or decline them.

For example, a customer makes a high-value purchase from a new location but has a long order history with your store. The system flags it as risky  but your team manually checks and sees it’s likely the same person traveling. In this case, you can override the block and approve the order.

Manual review adds a human touch, especially for borderline cases where systems alone might be too cautious.

4. Feedback Loops

Fraud prevention isn’t a one-time setup; it gets smarter over time by learning from past events. This learning process is called a feedback loop. It helps your fraud detection system improve its accuracy by using real-world outcomes.

Here are three powerful sources of feedback:

 4.1. Chargeback Data

When a customer files a chargeback, it’s a strong signal that something went wrong — either the transaction was truly fraudulent, or the buyer had a bad experience.

Fraud systems can use chargeback data to update their rules or risk scoring. For example, if a specific pattern of behavior (like using certain email formats or payment methods) often leads to chargebacks, future transactions with similar traits can be flagged more aggressively.

Over time, this helps reduce repeat fraud from similar sources.

4.2. Customer Support Signals

Your support team talks directly to customers — which means they often spot fraud before the system does.

Imagine a support rep gets several complaints in one week from customers saying:
“I didn’t make that order,” or “My account was accessed without my permission.”

These support tickets are valuable signals. By feeding them into the fraud system, you can improve future detection — for example, by tightening account security or adding 2FA prompts in similar situations.

 4.3. CRM / Loyalty Triggers

Your CRM and loyalty programs also offer useful clues. When users suddenly change their behavior like redeeming all points at once, updating their email and shipping address, and placing a large order  that can be a sign of fraud.

Let’s say a loyal customer who usually spends ₹2,000 per month suddenly redeems ₹10,000 worth of rewards and ships the order to a different city. This unusual activity can be picked up by the system and flagged for review.

By integrating CRM and loyalty data with your fraud prevention tool, you add an extra layer of context that helps catch fraud without blocking good customers.

Choosing the Right Fraud Prevention Solution

With so many fraud tools available, it’s important to choose one that fits your business model, team size, and risk level, not just the most popular one.

1. Understand Your Business Model and Fraud Risk Profile

Every ecommerce store has different fraud risks. If you run a subscription box service, you’re more likely to face chargebacks. Selling luxury goods? Then payment fraud using stolen cards is a bigger threat.

Knowing your risk profile helps you choose the right fraud prevention for your ecommerce store that means one that targets your fraud types.

2. Map Tools to Your Operational Needs

Choose a tool that works with how your business runs. If you have a small team, go for a system that automates most decisions. But if you sell high-value items, like electronics or designer wear, you might want manual review options for flagged orders.

The goal is a tool that protects you without slowing operations.

3. Red Flags to Avoid in Vendor Claims

Beware of tools that promise “zero fraud” or “100% accuracy.” No system can block every attack and allow every good order. If a tool can’t explain why an order was blocked  like a mismatch between device and IP then it’s a sign to dig deeper.

 4. Questions to Ask Before Buying

Ask the right questions:

• Does it integrate with Shopify, WooCommerce, etc.?
• Do they have strong customer support?
• Are they addressing the challenges that your ecommerce store is likely to face?
• Can I adjust rules or thresholds myself?
• How are false positives handled?
• Do they offer real-time alerts?

A good tool will give clear answers, not just generic sales promises.

5. Considerations for Pricing, Integration, and Explainability

• Make sure the pricing works for your sales model per transaction or flat monthly fee.
• Integration should be easy, no long setup or coding help needed until and unless you have a strong in-house technical development team.

Also, avoid black-box tools which means that if a ₹10,000 order is blocked, you should know why and not be left guessing.

Case Studies: Fraud Prevention in Action

1. DTC Brand Scaling from ₹10 L to ₹1 Cr/mo GMV

MisFit Makeup (a direct-to-consumer beauty brand) used a layered fraud solution to manage growth. By combining AI rules, device fingerprinting, and manual review, they cut chargebacks to below 1% and slashed daily manual order checks by 70%. As they grew monthly revenue, their fraud system scaled to protect profits without slowing orders.

2.  Electronic goods store with fake registrations every minute

Our customer’s business was scaling rapidly  but with growth came risk. They began noticing a spike in suspicious signups. In just one hour, over 180 fake accounts were being created by an advanced bot  that’s three every minute.

This wasn’t a simple script. The bot was sophisticated enough to bypass standard bot protections, using realistic email addresses and human-like browsing behavior. It blended in with genuine users, making it nearly impossible to detect using traditional methods. 

Their system recorded over 81,000 bot visits and more than 38,000 fake email or username attempts. This not only messed up their data but also slowed down servers and wasted money on marketing to fake users. After switching to Sensfrx, the fake registrations stopped completely  thanks to its AI-powered real-time monitoring and strong fraud detection tools.

Future Trends in Ecommerce Fraud Defense

Fraud is changing fast. As scammers get smarter, fraud protection tools also need to improve.  Here are some upcoming trends that ecommerce store owners should know and how they could affect your business:

LLMs as Both Attacker and Defender

LLMs (Large Language Models) like ChatGPT can be used by both sides, good and bad.

Fraudsters can use these tools to write fake emails, trick your chatbots, or make their attacks more believable. But the same technology is also used to stop fraud  by spotting suspicious messages, fake user behavior, or unusual patterns faster than before.

Impact on store owners:
Scams may become harder to spot, but smart tools powered by AI can help block fraud automatically, so you don’t have to keep guessing what’s real.

Risk-Adjusted UX — Adaptive Friction

Not every customer should go through the same checkout process. Fraud tools can now adjust the level of security based on risk.

A returning customer may get a smooth, fast checkout. A new customer with suspicious behavior may be asked to enter an OTP or verify their identity.

Impact on your business:
You get less fraud, and good customers still enjoy a fast, easy shopping experience with  no need to slow down everyone just to stop a few bad actors.

Collaborative Detection (Network Graph-Based)

Fraud detection tools are starting to work together across companies sharing information about known fraudsters and fake accounts.

If someone uses the same fake email or device on multiple stores, the system can recognize it and block them early even if it’s their first time on your site.

Impact on your business:
You benefit from collective intelligence  stopping fraud faster by learning from what others have already seen.

Linking Fraud Ops to Growth & Customer Experience (CX)

Ecommerce Fraud prevention is no longer just about stopping bad orders, it’s also about supporting your business goals.

Smart fraud systems now help improve your customer experience by reducing false declines, offering smoother checkout, and protecting your reputation.

Impact on your business:
Better fraud tools can actually help you grow faster, keep real customers happy, and spend less time dealing with chargebacks or fake signups.

Conclusion

Prevention Is No Longer Optional

Ecommerce fraud is no longer just a risk, it’s a reality. Whether you’re a growing DTC brand or an established marketplace, fraud attacks are getting more frequent, smarter, and costlier. Waiting until something breaks can result in lost revenue, chargebacks, and damage to your reputation. Proactive prevention isn’t optional anymore, it’s part of doing business online.

Build a Layered, Feedback-Driven Fraud Stack

The most effective fraud prevention strategy doesn’t rely on one tool or rule; it’s layered. That means combining identity intelligence, transactional risk scoring, behavior analysis, and feedback loops. When your system learns from chargebacks, customer complaints, and loyalty activity, it becomes stronger with every transaction.

Align Your Fraud Strategy With Business Goals

Fraud prevention should support your growth, not slow it down. A good solution helps you block bad actors without blocking good customers. It should align with your team’s size, your platform, and your risk tolerance whether that means reducing COD abuse, stopping promo fraud, or keeping your ATO rates low.

As you scale, your fraud strategy should scale with you  flexible, data-informed, and always customer-friendly.