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?
Fraud detection is the process of using tools and procedures to prevent the theft of money, information, and assets. It is a security barrier that protects against various forms of fraud, including minor infractions and felony crimes. Examples of fraud include forging signatures on checks and stealing credit card numbers from 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 for fraud prevention.
What's the difference between fraud prevention and fraud detection?
Fraud prevention and fraud detection are often confused, but they do 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 detection and prevention 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 2023
The digital world is dynamic – and so too are the threats facing modern businesses and consumers. Here's a look at the overall risk landscape and the leading dangers consumers and businesses face today.
Fraud continues to flourish, driven by the rise of digital.
In the first half of 2022, mobile devices accounted for more than three-quarters (76%) of all global transactions. This growth – a 37% year-over-year (YOY) increase – is also a contributing factor to the rise in fraud worldwide.
In 2022, global fraud increased by 18% as compared to 2021, and digital channels are now linked to 61% of all fraud losses globally.
Banks, fintechs and other financial services providers are especially impacted by the rise in digital and, subsequently, the increase in fraud. As more consumers conduct banking activity via digital channels, the mobile app attack rate has increased by 323% on logins and 249% on payments YOY.
The impact can be truly devastating for organizations in the financial services sector with the average U.S. fintech losing a reported $51M to fraud annually. Meanwhile, the total cost of fraud for financial services organizations now exceeds four times the amount of the fraudulent transaction.
Fraudsters are leveraging more sophisticated techniques to evade detection by traditional fraud prevention measures.
Organizations don’t just need to prepare for an increase in fraudulent activity – they also need to contend with the rising sophistication of cybercriminals.
For example, our analysis of millions of documents processed in 2022 reveals that about one out of every two fraudulent documents detected by our tools (45%) were cases of first-party fraud. First-party fraud is when an individual uses their real identity to engage with an organization but alters information within their supporting documentation to improve their creditworthiness, inflate assets, or otherwise generate a positive outcome.
The main concern with the growing trend of first-party fraud is that most traditional fraud detection solutions are focused on detecting the use of fake identities – a.k.a. third-party fraud – and not alterations to the documents themselves. This makes it difficult for organizations to stop first-party fraud if they are relying on manual document reviews, traditional identity checks and other antiquated methods.
Another issue that poses a significant threat to organizations across industries is synthetic identity fraud. This is a type of identity theft in which a criminal combines both real and fake personal information to create a hybrid identity that can then be used for various identity-related schemes, such as credit card fraud, bank fraud, social services fraud and more. Like first-party identity fraud, synthetic identities can be more difficult to detect since the application is based, in part, on real information.
Again, the impact for banks, fintechs, lenders and other financial services providers can be significant. Synthetic identity fraud alone resulted in an estimated $20 billion in losses for U.S. financial institutions in 2020. Our analysis reveals that the total impact may be far greater since many financial institutions mistakenly write off some types of first-party fraud not detected during the application process as credit losses.
Business email attacks continue to increase
According to the FBI’s 2022 Congressional Report on BEC and Real Estate Wire Fraud, business email compromise (BEC) – scams that target people and businesses to transfer funds or relay sensitive personal information – is “one of the fastest growing, most financially damaging internet-enabled crimes.
In 2021, losses stemming from BEC attacks totaled more than $2.4 billion, a more than five-fold increase since 2016, according to the Internet Crime Complaint Center (IC3). These numbers are expected to grow given the rise in remote work, as well as the increasing reliance on a global, distributed workforce. In fact, in May 2022, the FBI released a public service announcement revealing a 65% spike in BEC scams between July 2019 and December 2021.
Perhaps even more concerning is that there is some indication that the fraudsters are taking the money extorted and converting it into cryptocurrency, which makes it exponentially harder to trace and retrieve.
Alternative payment methods and cryptocurrency are compounding complexity.
Alternative methods of payments, including e-wallets and cryptocurrencies, are also becoming more popular, with these methods now representing 24% of transaction volume globally and 29% of all value lost to fraud. Cryptocurrency scams in 2021 reached an estimated $6.2 billion globally, an annual increase of about 80%.
Crypto-related fraud is expected to continue its growth trajectory in the coming years, due to growing consumer interest, lax oversight and limited awareness. In short, the appeal of cryptocurrency – its secrecy and inability to be traced – is precisely what makes it a rife opportunity for criminals to exploit.
How does fraud happen?
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?
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, both of which can be used to detect fraud.
Unsupervised learning for fraud models
Unsupervised methods use fraud modeling 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 signaling 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:
- Building models and training the AI on your own
- 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
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.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.
