Fraud Detection

Fraud Detection: A Complete Overview

This overview covers trends, best practices, techniques, and technology for effective fraud detection and prevention against today’s sophisticated criminals.

As the world becomes more digital, scammers are constantly learning new ways to outsmart fraud detection. And the stakes are high, costing some organizations millions in losses.

What were traditionally offline methods for accessing financial products are moving online: applying for a mortgage with your local bank manager, purchasing a car from your nearest dealership, meeting with an insurance broker for business insurance, renting a property with an estate agent.

But most new customers are required to submit documents to financial services providers so they can prove identity and residence, in addition to documents for other data points such as income or assets.

This creates opportunities for fraudsters to falsify information and fake documentation, and the business practices of yesterday (which rely on human review for mundane tasks) have expired. Today’s leading businesses are replacing manual processes with faster, more reliable, and more scalable automated processes for fraud detection.

In this guide, we’ll uncover the best methods and technologies for detecting fraud. But first, a simple definition.

What is fraud detection?

A fraudster trying to evade fraud detection

Fraud detection is a security barrier using tools and procedures to safeguard money, information, and assets from being stolen. Fraud comes in all shapes and forms, ranging from minor infractions to felonious crimes. For example, forging a signature on a check for $50 or stealing the credit card numbers of millions of account holders.

Thanks to the internet, there are countless ways criminals can obtain your data, access funds, or steal assets unexpectedly. Logging into an insecure Wi-Fi is enough to expose your personal information to nearby scam artists preying on the unsuspecting. But it can also happen in the privacy of your home or at your place of business.

Having a way to detect fraud before it happens is critical to prevent becoming another victim of a cybercrime. This means losing thousands, or even millions, of dollars for some businesses. Some options you'll find involve AI technology —but more on this later.

What is fraud prevention?

Fraud prevention is a strategy used to stop fraud threats before they occur. It requires the right knowledge and tools to detect fraud before it happens. For instance, a banking system identifying an unusual transaction in a location outside of the U.S. or a firewall blocking an attempt to access a file without authorization.

As the world becomes more digitized, the importance of fraud prevention measures also increases. The good news? Fraud prevention specialists are developing new solutions with better authentication and fraud detection.

The bad news is that fraudsters are collaborating and networking on the Dark Web to learn how to break through these new technologies. So it's critical to stay on top of the latest fraudulent tactics to prevent fraud.

What's the difference between fraud prevention and fraud detection?

Two people review a document to detect fraud

The two terms are often confused, but they have different meanings. Fraud prevention is about halting fraud before it happens, while fraud detection is about identifying fraud as it happens.

Here's a quick breakdown:

  • Prevention is proactive and involves taking steps to avoid fraud. Detection is reactive and involves identifying fraudulent activity.
  • Prevention is effective because it takes advantage of fraudsters' mistakes.
  • Detection is also practical because it allows you to take action immediately. This gives you time to investigate and recover any losses.
  • Prevention looks at all the data available on a user, device, or transaction. It analyzes how the data was transferred, who sent it, where it came from and when it was created.
  • Detection looks for patterns in the data that indicate fraud. For example, multiple payments made to one location within a short period of time would raise suspicion.

Why is fraud prevention and detection so important?

Fraud costs businesses and consumers billions of dollars each year. In fact, in 2020, the FTC received

2.2 million fraud reports from consumers, who lost an astounding $3.3 billion. And that's not counting the

$400 billion in unemployment benefits stolen (some of which was recovered by the NSA). Or the $100 billion stolen from pandemic relief funds (i.e., the SBA's paycheck protection program).

On the business side, the costs can be in the high billions annually. This includes direct costs for loss prevention and investigation services, as well as indirect costs from lost productivity and customer trust. Brand and reputation damage is difficult to quantify, but can be irreparable.

Fraud can ruin people's lives, businesses, and livelihoods. But if this isn't enough to demonstrate the importance of fraud prevention and detection, then let's reveal the latest trends.

Fraud trends to watch in 2022

A person uses their phone in a cafe and connects to public Wi-Fi, putting them at risk for fraud.

The internet is constantly evolving, and with it, cybercriminals. Today's threats look a lot different than those in the ‘80s and ‘90s. Here's a look at the leading dangers consumers and businesses face today.

Digital natives (and digital converts) are on the rise

Consumers are using more mobile devices than ever. The year-over-year (YoY) growth rate is 42%, and roughly 60% of transactions now occur on smartphones. Because of this, we're seeing nearly 50% more mobile device fraud attacks on a YoY basis.

This trend makes it challenging to identify users and ensure they're actually the device's owners (instead of a fraudster). Upwards of 95% of applicants are using synthetic identities—none of which are flagged by traditional fraud models.

Then there's the 38% YoY increase in bot attacks on the financial services industry. Fraud network attacks are also costing online retailers and marketplaces millions. A fraud network with 850 devices, 134K email addresses, and 61K phones targeted five retailers and one marketplace, costing them almost $28 million.

Fraud attacks are becoming more sophisticated

Criminals are increasing the sophistication of their attacks. But that's no surprise, given the advancements in technology. Hackers are attacking every 39 seconds, and nearly half of Americans have had their data compromised in data breaches in the past five years.

Account attacks are up 72% YoY, with one out of seven accounts created fraudulently. Sadly, COVID-19 scams are growing by the day against those affected by the pandemic.

Why’s this all happening? Because of the shift online. Especially since the beginning of the pandemic, applications for real estate, loans, and even jobs are submitted virtually. With the safety of a computer screen, fraudsters can lie about their identity, income, and other key details.

There are even social media websites and online forums where criminals share information to avoid fraud detection and create synthetic identities. Our data shows that up to 3-5% of high-value online applications now contain fraudulent information.

Uncertainty is putting consumers (and the market) at risk

People today are stressed and fearful, and they have every right to be. The pandemic made folks more comfortable using technology to shop, apply for benefits, and obtain credit. Unfortunately, the fear and uncertainty are causing people to perform riskier online transactions, increasing the odds of malware infection on their devices.

This led to a growth in successful attempts to exploit consumers' weaknesses. And we're seeing this across verticals:

  • Retail saw a 61% increase
  • Ecommerce saw an 83% increase
  • Lending saw a 40% increase
  • Financial services saw a 58% increase

Business email attacks are increasing

According to IC3, BEC (Business Email Compromise) attacks are a sophisticated scam targeting people and businesses performing funds transfers. The scammer uses a legitimate business email account acquired through computer instruction techniques or social engineering. Then, they use it to conduct unauthorized funds transfers.

In 2020, IC3 received over 19,000 BEC and EAC (Email Account Compromise) complaints. The total losses due to this almost reached $2 billion. What's concerning is the fraudsters are taking the money extorted and converting it into cryptocurrency, which may be harder to retrieve.

Faster money means faster scams

Instant gratification is sought after by consumers and businesses alike. Unfortunately, it's coming at a high cost for some. Banks are offering faster digital financial transactions to appeal to customers in the finance sector.

We're also seeing this trend among B2B sectors offering real-time payments. It's an attractive way to handle business, but not when criminals are on the prowl. If a fraudster takes your money in real-time, there's no way to prevent the scam.

By the time you learn of the misdeed, it may be too late.

These are just some of the latest trends in fraud since the 2020s. Others you might find jarring include IoT attacks on platforms like Google Home and Alexa, as well as criminals stalking their victims, learning their patterns and posing as vendors to scam them in a fake transaction.

How does fraud happen?

 person sits in front of a computer hoping to detect fraud.

Fraud can happen in several ways. The most common types today include:

  • Identity theft: stealing someone's personal information, such as birth date, Social Security number, addresses, and phone numbers to apply for credit.
  • Phishing scam: Sending emails as a vendor or government agency that contains links or attachments containing malware to steal login credentials.
  • Malware: software that secretly records your keystrokes to steal usernames and passwords for accounts.
  • Documents: forging documents, such as licenses, passports, birth certificates, contracts, invoices, and credit applications.

This just scratches the surface—there are many types of fraud, including tax fraud, charity scams, auto accident fraud, mortgage fraud, and credit card and bank account fraud, to name a few.

How can I protect myself from fraud?

Being connected to the internet makes it harder to protect yourself from fraud. However, you can use methods and tools to make fraud prevention easier. For instance, you can:

  • Create strong passwords, preferably those made up of symbols, letters, and numbers (vs. words and names), then keep them somewhere safe and never share them.
  • Avoid clicking links in emails or online ads unless it's from a trusted source (check the email address to ensure it's not posing as an entity or person).
  • Never give out your personal information over the phone unless you're sure it's legitimate. Most companies and government facilities don't call to request personal information over the phone.
  • Never provide personal information over the internet unless it's on a secure website of a legitimate company.
  • Use anti-spam filters. Spammers send millions of emails daily to trick people into revealing their personal information. Anti-spam filters can prevent many of these emails from reaching your inbox.
  • Don't open suspicious files. When you receive an attachment from someone you don't know, always check its source first. You can find the sender's name and address by going to the website listed in the message.
  • Don't respond to unsolicited calls or emails. Scammers often pretend to be a bank or utility company. They may claim they need access to your account or ask for personal information, such as a Social Security or credit card number.

How do I detect fraud?

A women at a computer relies on artificial intelligence for fraud detection.

The best way to detect fraud is by using AI-powered software. Artificial intelligence works around the clock to identify unusual behaviors, and when mixed with machine learning, it continues to evolve. This is critical since criminals consistently find new ways to infiltrate your accounts and steal your identity.

Most software uses an analytic model to identify predictors of fraud. For instance, software that can detect fraudulent documents, like Inscribe, is able to determine inconsistencies in the font on a bank statement, showing a possibility of fraudulent modification.

This type of software learns by analyzing historical data to identify fraud actions and better predict them in the future. Some software is niche and will look for certain types of fraud. For instance, transaction monitoring software searches for fraudulent transactions and charge amounts in the banking industry.

Fraud detection often also has different classification levels, such as “potential threat“ or “high-risk threat.“ The first typically notifies the account owner to confirm if it was them. The second may automatically lock down the account until the account owner contacts the company.

There are two methods for building fraud models: supervised and unsupervised.

Unsupervised learning for fraud models

Unsupervised methods use fraud modelling to detect abnormal events. These events are characterized based on symptoms of past fraudulent activities. However, the statistical classification doesn't prove it's actually a fraud. It simply suggests a probability of fraud and will require further investigation.

An example of this is document fraud detection. The software learns how particular bank statements look and then matches them with the newly scanned document. If there are abnormalities in font or format, it'll flag it for review.

Unlike supervised learning, the AI assesses and examines data that isn't already identified as fraudulent. So it's teaching itself to find anomalies and patterns without human intervention. Another example is text analytics used to identify names, companies, ties, monetary values, and other content to extract and categorize information.

This can determine if there are too many characters in a routing number on a fake check or if a name and address don't match the account owner.

Supervised learning for fraud models

Supervised learning requires human involvement to teach the AI with samples. The person will tell the system if a behavior is fraudulent or non-fraudulent to teach the machine how to detect patterns independently. For the best results, the machine learning model requires large sample sizes.

An example of supervised learning is in credit card fraud detection, auto claim fraud detection, medical insurance fraud, and telecommunications fraud.

Telecommunication companies use a hybrid learning approach with experts and integrated statistics and data mining to detect cellular clone fraud. This is possible by using a rule-learning program to find fraudulent behaviors using a large database of customer transactions.

Fraud detection techniques

There are two ways to detect fraud: using artificial intelligence or manual processes. Here’s an overview of the two.

Detecting fraud using AI

Accurate fraud detection takes more than having the right AI software—it requires a system of methods carried out by you and your team members. These steps are a part of a procedure to ensure fraud detection prevails. Data analysts create algorithms to detect anomalies and patterns.

With AI, companies can create models that can:

  • Calculate statistical parameters
  • Match data
  • Perform regression analysis
  • Use probability models and distributions
  • Mine data to classify, segment, and cluster data to find associations and rules signalling patterns of fraud
  • Use machine learning to detect fraud using rules
  • Recognize patterns to detect suspicious activities and behaviors

There are two options for implementing fraud detection techniques:

  1. Building models and training the AI on your own
  2. Purchasing an "off-the-shelf" technology designed for fraud detection

Using your experts and AI, you can build a system that involves screening applicants and implementing training models to catch the things humans can't.

Detecting fraud manually

Without AI and machine learning, you’re left with manual processes for detecting fraud. This includes using the human eye to spot inconsistencies in a document’s text style, alignment, spacing, and color.

Unfortunately, this isn’t easy to do without a trained eye. There are fraud analysts that spend years learning different documents and how to detect discrepancies. For example, looking for possible manipulation of a PDF using Photoshop.

It can take up to 10 minutes to analyze a single document, which can translate to hours for one application. Not efficient or reliable, since photo editing tools are becoming more advanced.

Which industries use fraud detection and prevention?

Both businesses and government entities use fraud prevention technologies, including data visualization and AI. A team of analysts and investigators collaborate to remove data silos, identify threats, and score them based on severity.

Here's how various sectors have adopted fraud detection and prevention:

  • Banks use software to identify synthetic identities, fraudulent transactions, and nefarious applications.
  • Insurance companies use algorithms to find anomalies in insurance claims as it occurs to prevent processing fraudulent claims.
  • Public sector entities combine data across departments to identify tax fraud and abnormal behaviors and block real-time threats. They often work with law enforcement, border security, and social services.
  • Healthcare organizations are often focused on preventing fraudulent health insurance claims, which can cost millions (or billions), using advanced analytics.

5 best practices for fraud detection and prevention

A man reviews documents for potential fraud.

So you’re ready to set up fraud detection and prevent heinous crimes in your organization. Where should you begin? And how do you maintain it? Use the following best practices to get started.

1. Identify potential fraud threats

It all starts with a fraud risk profile. Identify the different types of fraud threats your business may have in each department. Then categorize the risks as either high, medium, or low threats. Get help from all stakeholders in each department with first-hand experience dealing with fraud.

2. Implement artificial intelligence

Using AI simplifies and enhances fraud detection. It works fast and around the clock to safeguard your organization from criminals. It's ideal to use a platform with machine learning, so it continues to evolve. Make sure to update rules to detect new threats, which bring us to our next best practice.

3. Audit and monitor for fraud threats consistently

Once you put your fraud detection and prevention methods into play, continue auditing and monitoring for threats. This ensures your techniques are working to stop alternative forms of fraud from happening. You may find new threats your current system isn't screening for or detecting and will require training the AI or adopting a new solution.

4. Educate your organization about your fraud detection system

Fraud prevention works better when everyone in the company understands how it works. Educate your teams to use the AI system and identity problems. Delegate tasks to the right experts that can deal with flags raised by the fraud detection system.

5. Revisit your fraud profiles regularly

What fraudulent behaviors did your system detect over the past six months? Are there developments in a type of fraud that need updating? Re-examine your fraud profiles and add risks that arise over time. Criminals are consistently escalating their methods, so be sure to include them so your AI and teams can identify them quickly.

Fight fraud with Inscribe's AI technology

Fraud detection is critical in businesses of all sizes and types. Criminals don't discriminate and will attack any entity they deem penetrable. So don't be that defenseless organization — it's time to update your system and processes with AI technology and ongoing auditing and monitoring.

In this article, you learned various ways fraudsters get their hands on information and assets. Use it to guide your efforts to detect and prevent fraud from hurting your company's reputation and financial well-being.

Inscribe automates the process of reviewing documents such as bank statements, pay stubs, tax documents, driver's licenses, and more. Inscribe instantaneously detects fake and manipulated documents by forensically examining documents and extracting key details such as names, addresses, dates, and transaction information. Inscribe provides you with no-touch automation that you can trust.

Once a document is submitted, it goes through a rigorous set of checks that alert you if any fraud is present. By integrating Inscribe directly into your workflow, you can save time on manual reviews and reduce fraud loss across your business.

Need this in your fraud detection tech stack? Get started with Inscribe today.

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