Insurance fraud is a critical issue that affects both individuals and the insurance industry as a whole. It refers to any act committed with the intent to fraudulently obtain benefits, coverage, or compensation from an insurance policy. Fraudulent insurance claims contribute to rising premiums for policyholders and significant financial losses for insurance companies. In this essay, we will delve into the various aspects of insurance fraud, its consequences, methods of prevention, and the role of technology in mitigating fraud.

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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:

1. Over half of organisations stated that fraud resulted in financial loss
2. Of these companies, a quarter reported a financial impact in excess of $1M
3. 30% said it disrupted business
4. 23% claim it lowered employee morale
5. 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:

1. 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.
2. 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.
3. 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.
4. Bonus abuse: Fraudsters use linked accounts to abuse merchant terms, whether it is to benefit from signup promotions or loyalty rewards.
5. 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.
6. 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.
7. 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 a risk strategy. Let’s break it all down in detail.

Anti-Fraud Education and Training

An often overlooked yet highly effective way to reduce fraud is to educate your employees and customers about it. This is particularly powerful when it comes to teaching users about the value of their accounts, for instance, as it can drastically curb rates of account takeover attacks.

Similarly, you may be able to prevent sophisticated attacks such as phishing, social engineering, and even CEO fraud, simply by teaching your staff and employees how to recognize suspicious online interactions.

User Fingerprinting

When it comes to fraud detection and prevention, the more data you have about your users, the better. This is why a complete user fingerprinting process is recommended. This can be done thanks to a number of tools, such as:

1. Digital footprinting: To learn more information based on a single data point. This process aggregates external data to complete a picture about a user, for instance. A good example is reverse email lookup, which lets you get a complete picture of a user based on a single email address.
2. Social media lookup: A powerful way to learn if your user has a social media presence. This can be useful for compliance reasons, or simply to verify someone’s ID. Make sure that your solution can check as many social media networks as possible, and in as many regions as possible.
3. Device fingerprinting: Looking at your users’ configurations of software and hardware is a great way to ID them or to spot suspicious devices that may point to fraud.
4. AML lookups: Businesses within regulated verticals are increasingly exposed to noncompliance risk and money laundering risk. Understanding whether your users are high-risk as soon as possible is a great way to reduce fraud in the long run.

There is no one-size-fits-all solution for user fingerprinting, which is why the best risk managers will rely on a combination of tools in order to decide who is risky and who isn’t.

Custom Rules and Risk Scoring

Most online fraud prevention tools work by using risk rules. They can be simple, blocking certain IP addresses, or complex, looking at how often a user performs a certain action. Since fraudsters adapt to your strategy, however, it is important to be able to edit the rules or to create new custom ones as needed.

Another crucial point to consider is the deployment of risk scores, in order to calculate risk to make sure the results adapt to your business needs. This is not only important to improve accuracy, but also to automate the approval, review, or rejection of certain user actions.

Transaction Monitoring

The payment stage is the best one to catch fraudsters, as they will often use stolen credit card details. This is why it’s vital to gather as much payment data as possible – ideally in real-time.

For instance, a card BIN lookup can instantly let you know whether the credit card is valid, where it was issued, and what kind of card it is. It’s worth noting that pre-paid and gift cards are usually considered high-risk, for instance.

All the transaction data should also be used in combination with the user data you have gathered. This is to identify suspicious discrepancies, such as, say, a credit card issued in Cyprus for an item that is shipped in Brazil.

Machine Learning

If you are dealing with complex fraud attacks on a daily basis, you might be overwhelmed by the data. This is precisely where machine learning systems can help. By analyzing fraudulent users, payments, or behavior, an ML system can extract valuable patterns and suggest risk rules.

Machine learning systems work best when you have a large volume of historical data to train the models on, and the key advantage is that it may identify patterns that a risk manager would have missed by manually poring over the data. This kind of system’s accuracy also improves over time.

What Are the Main Challenges of Fraud Detection and Prevention?

After accepting that fraud will be an inevitable byproduct of conducting business, the most important challenges to overcome are identifying the most pervasive issues for your business, developing the resources to address them, then implementing the best course of action, all while staying compliant to regulatory frameworks.

Detecting Fraud Pain Points

Looking at these challenges with more granularity, common pain points that must be known before they can be addressed include:

1. False positives: Fraud detection processes have to be able to distinguish paying customers who contribute to your ROI from fraudsters who steal from it. The margin for error includes good customers who appear fraudulent to software, often resulting in a disgruntled customer and possibly a damaged reputation — and of course no checkout. Balancing risk thresholds to minimize these false positives is a crucial business optimization.
2. Evolving risks: Unfortunately, fraudsters are collectively resourceful and creative, and will find new pathways to their goals whenever another one closes up. Identifying which threats are most damaging to your revenue flows, as well as proactively planning for the next one is an important part of best practice fraud prevention. Another part of internal risk review is understanding what regulatory perimeters you fall inside, if any. 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.
3. Optimizing customer journeys: Depending on your vertical, certain amounts of 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 against optimizing the smoothest road to checkout. 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 a minimal amount of friction.
4. Data hygiene: Fraud prevention measures that deploy an AI to look for tiny risk signifiers hidden in data have to be trained in order 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.

Once the answers to these questions are known and, hopefully, consistent, the next challenges can be addressed off the back of them.

Developing the Best Fraud Prevention Framework

Once you have identified leaks in your ship, 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:

1. Choosing the best fraud prevention and detection software: Once you know your pain points and risk exposure, finding the best-fitting fraud prevention software will ultimately save you huge amounts of human resources, as well as losses to fraud. Currently, SEON advises choosing, agnostic of any particular industry, a solution that:
   • verifies IDs 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
2. Dedicated fraud team: Having in-house fraud prevention specialists is a revenue-saving piece of the fraud-fighting puzzle, and in some cases can be a legal requirement. Not only will specialists be the best at adeptly wielding your chosen fraud management platform, regulated verticals are required to appoint a designated Money Laundering Reporting Officer (MLRO) when submitting Suspicious Activity Reports (SARs) to governing bodies.
3. 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 a result of phishing scams that approach 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.

After the framework has been designed and signed off on, the final hurdle is implementation and process execution.

Integrating the Best Fraud Prevention Solution

Implementing any new system will always have hiccups as it’s being 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:

1. Regular fraud reviews: All businesses should regularly assess the effectiveness of both software and internal procedures to make sure they are optimized to fight fraud. These assessments should be carried out by both internal and external teams.
2. Compliance: As the goalposts for maintaining regulatory compliance are constantly moving, all companies should be proactively learning 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), as well as data privacy laws like GDPR.
3. Establish clear appetites and practices: For the most complete fraud prevention coverage, your company needs to establish a single risk appetite — that is, how much risk to fraud and noncompliance are you willing to take on, balanced against potential profit. Additionally, incidences of successful fraud should have explicit workflows attached to them, 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.

The biggest challenge, however, may be the integration process. We’ll dive deeper into the options you have below, but always keep an eye on:

How Do You Choose Fraud Detection & Prevention Features?

After evaluating how you can integrate the solution, there are key differences between the systems you should consider.

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.