Fraud detection and prevention tools are an integral part of every digital business. Though they are smart things to adopt for any industry, their importance is also indicative of the pervasive impact that fraud has on businesses today.
But what is fraud prevention and detection, and how can your organization choose the one that best suits your needs?
What Is Fraud Detection?
Fraud detection refers to actions set in place to prevent criminals from gaining monetary advantages through false pretenses. In the online business world, fraud, scams, and bad agents are damaging in a number of ways. Companies have to put steps in place to ensure that fraud is detected and stopped before it affects business.
Fraud prevention refers to the countermeasures established to mitigate the impact that fraudsters can have on business operations, once detected.
Detecting fraud is the first step in identifying where the risk lies. You can then prevent it automatically or manually using fraud detection software, RiskOps tools, and other risk management strategies.
Beyond the technological tools put in place for prevention and detection, a holistic fraud program includes:
- A dedicated fraud team. Though risk-based software approaches to fraud-fighting offer a high degree of automation, human oversight is necessary for when a manual review needs to take place.
- Policies and procedures that establish both the risk thresholds — the amount of risk an organization is willing to assume for each customer or transaction — and the procedures around those risks is important for internal processes. This will cut down on resources devoted to manual reviews.
- A system of training and awareness is a mandated aspect of diligent fraud prevention. This extends beyond fraud and compliance teams, as even executives should be aware of the prevailing fraud risks for the company, as well as the potential security hazards they may find themselves embroiled in.
- Monitoring, documentation, and reporting are also important parts of a fraud prevention program, both for internal data hygiene as well as (in regulated industries) compliance with existing statutes.
- Fraud prevention and detection practices should be continuously updated and improved. This should include regular scrutiny, both internally and by third-parties, to make sure the protocols are working and remain compliant.
Though different industries have different regulations that may require such a framework, it is also best business practice to maintain such a program, to avoid legal complications, large dents in ROI, and provide a safe business environment for customers. Failure to do so can lead to reputational damage or worse.
Why Is Fraud Detection Important
There’s simply no way around it: if your business is online, you’ll need real-time fraud detection and prevention software. Attacks take on many forms and affect businesses differently, but they are certainly pervasive. According to PwC’s most recently published 2022 Global Economic Crime and Fraud Survey:
- Over half of organisations stated that fraud resulted in financial loss
- Of these companies, a quarter reported a financial impact in excess of $1M
- 30% said it disrupted business
- 23% claim it lowered employee morale
- hackers and customers grew as perpetrators of fraud, to 31% and 29% respectively
What Are the Common Types of Fraud?
Fraud takes on many forms, and it adapts to every business model. However, there are a few recurrent attack vectors worth knowing about. These include:
- Credit card fraud: Criminals steal credit card numbers and use them to buy services or products from your company. A chargeback is then submitted, for which you must cover the administrative fees.
- Account takeover fraud: more sophisticated attacks, which use identity theft (often through phishing) to steal credentials of an existing account. The end goal, however, is still the same: steal money or personal data from the original user.
- Fake accounts: Fraudsters falsify information or use stolen IDs to create a new account. A lax signup policy may allow easier onboarding for traction, but it also opens the door to bad agents. It’s one area we’ve seen a boom during the pandemic – for example in the FX trading world.
- Bonus abuse: Fraudsters use linked accounts to abuse merchant terms, whether it is to benefit from signup promotions or loyalty rewards.
- Friendly fraud: This fraud happens when the legitimate cardholder contests a payment. This is either because they forgot, regret their purchase, or maliciously anticipated a chargeback request.
- Affiliate fraud: A marketing partnership can quickly turn sour if your affiliates send bad traffic to your site on purpose. This is particularly prevalent in the iGaming industry, where unscrupulous affiliate fraudsters target PPC (pay-per-click) and PPL (pay-per-lead) acquisition models.
- Return fraud: Another attack vector, growing in popularity due to changing return policies across the ecommerce landscape. Fraudsters purchase items on your site and take advantage of your return policy to get free items, or intentionally deplete your inventory.
The Best Fraud Detection and Prevention Methods
Fraud detection and prevention requires a three-pronged approach, combining education about fraud risks, anti-fraud technology and an elaborate risk strategy.
Education and Awareness
Educating employees and customers on fraud risks is crucial. Awareness can significantly reduce account takeover incidents, phishing, social engineering, and CEO fraud by teaching your staff how to recognize suspicious activities.
Anti-Fraud Features and Transaction Monitoring
A robust anti-fraud strategy involves detailed user fingerprinting through digital footprinting, social media lookups, device fingerprinting, and AML checks to identify risky users. Adding transaction monitoring enriches this approach by analyzing real-time payment data, utilizing tools like card BIN lookups to validate credit card details and identify high-risk transactions. This comprehensive data use helps spot discrepancies and prevent fraud at the payment stage.
Rules Creation and Machine Learning
Implementing custom risk rules and scoring is essential for adapting to evolving fraud patterns. Machine learning enhances this by analyzing large volumes of data to uncover patterns and suggest risk rules, improving detection accuracy and efficiency over time. Combining rule-based systems with machine learning allows for the dynamic and effective prevention of complex fraud attacks.
SEON offers a fully modular fraud solution and the support of a team that are experts in online fraud
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What Are the Main Challenges of Fraud Detection and Prevention?
Fraud is an inevitable byproduct of conducting business, but after identifying the most pervasive issues for your business, you can develop the resources to address them and then implement the best course of action, all while staying compliant with regulatory frameworks.
Detecting Fraud Pain Points
Looking at these challenges with more granularity, these are some of the common pain points to be aware of before they can be addressed:
- False positives: Fraud detection processes must distinguish paying customers who contribute to your ROI from fraudsters who steal from it. The margin for error includes good customers who appear fraudulent, often resulting in a bad customer experience and, of course, no checkout. Balancing risk thresholds to minimize these false positives is crucial for business optimization.
- Evolving risks: Unfortunately, fraudsters are collectively resourceful. Identifying which threats are most damaging to your revenue flows and proactively planning for the next one is an important part of fraud prevention, in addition to understanding what regulatory perimeters you fall inside. These perimeters are redrawn constantly and need constant attention to adhere to. Steering clear of fines and reputational damage associated with noncompliance is a huge part of controlling your total risk exposure.
- Optimizing customer journeys: Depending on your vertical, certain personal identifying information must be collected to satisfy mandated due diligence. Collecting this information, as well as introducing touchpoints for fraud prevention purposes, can introduce an amount of customer friction which has to be balanced and optimized. The more a company knows about its risk appetite, the more optimized the customer journey can be. Notably, fraud platforms like SEON can scrutinize a great deal of passive information to assist with identity verification while introducing minimal friction.
- Data hygiene: Fraud prevention measures that use AI to look for tiny risk signifiers hidden in data have to be trained to fit each company’s particular needs. Having a central, well-labeled pool of customer data makes this process much more cost-efficient. A hygienic data pool will also reveal answers to the aforementioned challenges, making them easier to address.
Developing the Best Fraud Prevention Framework
The next step is assembling the best-fit plug, a team to do the plugging and a system to keep the plugs in place and mop up any remaining leakage. This includes:
- Choosing the best fraud prevention and detection software: Once you know your pain points and risk exposure, a fraud prevention solution that fits your needs will ultimately save you huge amounts of human resources and losses to fraud. Currently, SEON advises choosing a solution that:
- verifies identity using digital identifiers like device fingerprints
- can be laid out in multiple layers across channels and journeys
- allows for transaction monitoring
- facilitates legal compliance
- utilizes real-time data
- Dedicated fraud team: Having in-house fraud prevention specialists is a revenue-saving piece of the fraud-fighting puzzle or can be a legal requirement. Not only will specialists be the best at adeptly wielding your chosen fraud management platform, but but regulated verticals must appoint a designated Money Laundering Reporting Officer (MLRO) when submitting Suspicious Activity Reports (SARs) to governing bodies.
- Education and awareness: The recent fraud environment has seen Business Email Compromise (BEC) and Authorized Push Payment (APP) fraud creating huge craters in returns. These are often the result of phishing scams that come from all possible channels, including email, social media, SMS, and more. All staff with infrastructural access must be trained and updated on pervasive threats, and training should be considered an important part of a best practice fraud framework.
Integrating the Best Fraud Prevention System
Implementing any new system will always have hiccups as it’s ingested into your infrastructure. In fraud prevention, though, these can and should be planned for so the execution of your chosen framework doesn’t allow major mishaps. As you move forward with your fraud solution, your workflows should include:
- Regular fraud reviews: All businesses should regularly assess the effectiveness of both software and internal procedures to ensure they are optimized to fight fraud. These assessments should be carried out by both internal and external teams.
- Compliance: As the goalposts for maintaining regulatory compliance are constantly moving, all companies should proactively learn what laws their ongoing business is responsible for. At a certain scale, all companies, regardless of vertical, should at least have a designated compliance and reporting officer that monitors adherence to mandates like Customer Due Diligence (CDD), Anti-Money Laundering (AML), and data privacy laws like GDPR.
- Establish clear appetites and practices: For the most complete fraud prevention coverage, your company needs to establish a single risk appetite and assess how much risk to fraud and noncompliance you are willing to take on, balanced against potential profit. Additionally, incidences of successful fraud should have explicit workflows attached, as time and transparency can come into play during the fraud management lifecycle, especially when it comes to reporting to authorities. Of course, having an established foundation of processes also helps avoid panic in the face of cybercrime.
How Do You Choose a Fraud Detection & Prevention System?
After evaluating how you can integrate the solution, there are key differences between the systems you should consider.
- Customizable Rule Systems: Fraud management platforms like SEON offer complete customizability, on top of blackbox and whitebox machine learning solutions that tune risk rules to suit your company specifically.
- Whitebox and Blackbox Machine Learning: Machine learning is about using your own business data to suggest precise risk rules. The accuracy of these rules improves over time, which can make them an effective tool against attacks. Blackbox solutions look at points too granular to be consistently interpretable for a human fraud team member, so don’t bother trying. Whitebox solutions, on the other hand, will do their best to deliver clear explanations in the form of decision trees or human-readable explanations, which can be useful when reporting why a transaction was or was not declined.
- Data Protection and Compliance: In the world of fraud, prevention is based on data collection. And as we know, this is a practice that is increasingly under scrutiny from government agencies, particularly as financial scandals and bankruptcies shake consumer confidence and create ripples through the economy. While these vary from one market to the next, certain regulations such as The General Data Protection Regulation (GDPR) and the EU’s PSD2 categorically must be respected. As well, by now you should be aware of your due diligence in terms of AML and KYC compliance. Solutions like SEON offer fraud teams all the tools they need to help keep regulators satisfied.
- User Experience: One often overlooked feature of fraud prevention solutions: the ease of use. The best engineers are not always the best designers of user experience, which is why some interfaces can be confusing, bloated, and frustrating. With the ballooning of the SaaS fraud prevention market, choosing a product that suits your team’s sensibilities should be a priority.
- Monitoring KPIs and KRIs (Key Risk Indicators): Finally, the fraud-prevention tool should give enough reports and analytics for your team to monitor its efficiency. The most robust solutions will also help you develop insights into your own data with machine learning-assisted processes. The efficacy of manual processes, detection accuracy, and ROI are all metrics you should be tracking in order to optimize and number of business workflows moving forwards.
- Integration and Support: Having a clear understanding of how your chosen fraud solution will integrate with your platform can save hours of costly technical difficulties down the line. Generally, modern fraud-fighting SaaS will have degrees of available technical support. SEON, for example, will help you develop your custom rules to address very specific issues, and will be alongside to assist in integration, even for SEON Free users.
- Pricing Model: In the UK this year, the amount businesses will spend on fraud prevention will amount to two-thirds of the UK’s entire national defense budget. As well, for most online businesses, margins are razor-thin, and the competition is intense. A reasonable pricing model is just as important as software features.
To Sum Up Fraud Identification
With a growing number of fraud-prevention tools available on the market, it can be easy for merchants to be confused. It is bad enough that companies have to deal with relentless attacks, on top of that they must now face the challenge of vetting the right solution as an important business decision.
Hopefully, this guide will serve as a good primer. By now, you should have a clearer idea of which tools make sense for your company. And remember that remaining informed, whether it’s about the latest attack techniques or cybersecurity tools, is always the best way to stay one step ahead of the fraudsters – and your competitors.
SEON Fraud APIs are highly configurable for various business use-cases to match your unique business needs
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Frequently Asked Questions for Fraud Detection & Prevention
Fraud solutions can help you reduce chargeback rates, account takeover attempts (account hacking), and registration with fake IDs, amongst others. It can also help with compliance for KYC and AML checks.
The fraud detection process consists of gathering user and transaction data, feeding it to risk rules, and automatically approving or declining actions based on the results. For instance, an IP address is a data point. You can detect if it is risky or not based on whether it points to certain countries or VPNs.
For fraud detection to be effective, you need as much data as possible. That includes user data (device, IP address), payment data (card BIN, shipping address), and even behavior data (login attempts, password resets, etc.).
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Tamás Kádár is the Chief Executive Officer and co-founder of SEON. His mission to create a fraud-free world began after he founded the CEE’s first crypto exchange in 2017 and found it under constant attack. The solution he built now reduces fraud for 5,000+ companies worldwide, including global leaders such as KLM, Avis, and Patreon. In his spare time, he’s devouring data visualizations and injuring himself while doing basic DIY around his London pad.
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