Fraud Prevention: How it helps protect your business – and beyond

Fraud Prevention and Protection in 2024

While we’d all like to assume that if we’re careful enough, that if we don’t share our personal information or credit card details with just ‘anyone,’ we’re somehow immune to fraud. Well, nothing can be further from the truth. In the US alone, a staggering one in ten adults will fall victim to a scam or fraud every year – and more than 1 million children have their identities stolen every year. Gone are the days of fraudulent activities lurking in dark corners or the dark web. We know that fraud prevention must consistently anticipate and proactively mitigate against evolving fraud patterns and behaviors. But how can organizations or individuals outsmart even the ‘smartest fraudsters’ if they’ve already slipped past frontline identity verification defenses? Let’s take a closer look at fraud prevention, why it matters to organizations, and how businesses can proactively protect their customers with the right technology in place.

What is fraud prevention?

While fraud prevention may seem pretty obvious to some, for most of us, it’s anything but. We know it as the most important thing businesses are obligated to do, legally and ethically – to protect their customers from fraud and scams. Combating fraud before it occurs is vital to the survival of any business. Not only do businesses protect themselves and their customers from financial loss, but it’s also how they build and maintain customer trust and loyalty.

A complex and ongoing process, fraud prevention means identifying and preventing fraudulent activities ‘before’ they happen. It combines strategies, tools, and techniques, including identity verification, real-time transaction monitoring, sanctions screening,fraud  risk assessment, and more. We sometimes confuse fraud prevention with fraud detection, where prevention is proactive to avoid fraud, and detection is all about identifying fraud as it happens, on the premise of mitigating the risk, aka, finding a ‘cure.’ Some might even say that fraud detection jumps in when fraud prevention processes don’t work, hoping to save the day before irreparable damage is done. Fraud goes far and wide. Some common examples include identity theft, account takeover, and payment fraud. It can also include more complex fraud, such as deep fakes, manipulating one person’s likeness with that of another; synthetic identity fraud, where criminals combine fake and authentic information to create a new identity; and swarm attacks, where cybercriminals use thousands of hijacked devices to attack an organization’s networks or devices, sharing information in real-time to perfect an attack as it happens.

A (brief) history of fraud prevention

Not all that long ago, to prevent first-party fraud, businesses had to rely on what some seem archaic business rules and rudimentary analytics to hunt for system glitches or irregularities, create alerts, and, for the most part, hope for the best. There was no surefire way to automate processes or cross-reference data, and organizations could not detect nor prevent fraud in real time because the adversary was already on the way to their next cyber breach. But businesses today can’t afford to wait for a fraudster to strike. Organizations must be ready at the starting gate to keep pace with their fast-evolving techniques and exceedingly complex, unpredictable behaviors. Simply put, fraudsters need only win once to succeed, while fraud prevention needs to win every time, with no exceptions. This has caused businesses to wake up and replace their legacy systems with automated processes powered by AI and a newer, more powerful player, neural networks, an AI method that teaches computers to process data in a way inspired by the human brain.

Fraud Types

We have all heard the phrase “knowledge is power,” and in this case, it couldn’t be more accurate. Just understanding that fraud exists isn’t enough; it comes in various forms, and the first step to preventing fraud is knowing how it can be carried out. Let’s delve into some common types of fraud and their implications.

Document Fraud

Document fraud involves manipulating or fabricating documents to deceive individuals or organizations. Fraudsters may alter identification documents, financial records, or other paperwork to conduct fraudulent activities. For example, a fraudster might forge a driver’s license to obtain a loan under a false identity.

Serial Fraud

Serial fraud refers to repeated fraudulent activities targeting multiple victims or systems. Fraudsters engage in serial fraud to exploit vulnerabilities and maximize their illicit gains over time. An instance of serial fraud could involve a scammer repeatedly selling counterfeit products online under different aliases to unsuspecting buyers.

Deep Fake Fraud

Deep fake fraud utilizes artificial intelligence (AI) technology to create convincing replicas of individuals for malicious purposes. These fraudulent videos or images can be used for identity theft, misinformation, or blackmail. For instance, deep fake technology could be used to fabricate a video of a CEO making inflammatory remarks, causing stock prices to plummet.

Fraud Protection in the Era of the AI Revolution

With artificial intelligence (AI) making its way onto the scene, fraudsters have gained access to sophisticated tools, enabling them to complicate their once easily detected schemes. Deep fake technology, for instance, threatens its victims by creating convincing replicas of individuals, leading to identity theft and misinformation. Furthermore, AI algorithms can quickly and accurately analyze tremendous amounts of data, allowing fraudsters to identify and exploit vulnerabilities.

AI-driven fraud is emerging as a new challenge to traditional fraud prevention methods. Organizations must stay current with AI and the latest technology to keep fraudsters away and develop effective prevention strategies.

Common Fraud Techniques Using AI

1. Synthetic Identity Fraud: Synthetic identity fraud involves creating fictitious identities by combining genuine and falsified information. For example, a fraudster might use AI algorithms to generate a synthetic identity by blending stolen Social Security numbers with fabricated personal details, creating a seemingly legitimate persona that can be used to open fraudulent accounts or obtain credit.
2. Swarm Attacks: Swarm attacks, orchestrated by AI-powered bots, involve coordinated cyber assaults on networks or systems. These attacks exploit vulnerabilities in networks by utilizing a large number of compromised devices to overwhelm defenses. For instance, a fraudster might deploy a swarm of AI-driven bots to flood a banking website with traffic, causing it to crash and enabling unauthorized access to sensitive information.
3. Deep Learning Fraud: Deep learning fraud utilizes advanced AI techniques to analyze and mimic patterns of legitimate behavior, allowing fraudsters to evade detection by traditional fraud detection systems. For example, fraudsters may employ deep learning algorithms to train AI models that mimic genuine users’ behavior, making it difficult for fraud detection systems to distinguish between legitimate and fraudulent activities.
4. Adversarial Attacks: Adversarial attacks use AI algorithms to manipulate or deceive machine learning models. Fraudsters may use adversarial attacks to exploit weaknesses in fraud detection systems by introducing subtle modifications to input data that can mislead the model into making incorrect predictions. For instance, a fraudster might use adversarial techniques to alter images or transaction data to evade detection by AI-based fraud detection systems.

Laws and Regulations

Unfortunately, anyone could be the next victim of fraud. To ease this risk as much as possible, governments worldwide have passed strict laws and regulations to keep us safe.

The Gramm-Leach-Bliley Act (GLBA) in the United States is one example. Enacted in 1999, the GLBA mandates financial institutions to ensure the security and confidentiality of customer information in various ways.

Similarly, the European Union’s General Data Protection Regulation (GDPR) imposes strict requirements on organizations regarding the processing and protecting personal data.

We also have the Payment Card Industry Data Security Standard (PCI DSS), which sets requirements for securely handling payment card data to prevent fraud and data breaches in the industry. Organizations that process, store, or transmit payment card information must adhere to PCI DSS standards to protect cardholder data.

Furthermore, regulatory bodies such as the Financial Crimes Enforcement Network (FinCEN) in the United States and the Financial Action Task Force (FATF) globally establish guidelines to fight money laundering, terrorist financing, and other financial crimes.

Get to Know Your Fraudster

While we are not suggesting a meet and greet with a cybercriminal, an integral part of fraud prevention means putting strategies in place and managing a well-defined fraud prevention plan. To tackle threats head-on, this plan must be flexible and customizable to grow as your business grows and to keep up with the ever-changing cyber landscape. One smart preventative measure is to know your fraudster, become familiar with their current tactics, and better understand them so that you can predict and prevent fraud. When it comes to safeguarding your business and protecting your users from financial fraud, knowing your customer (KYC) is key, verifying a customer’s identity and financial profile, screening customers against global sanctions and PEP watchlists, and maintaining compliance. Today’s KYC-regulated industries, Banking and Financial services, Crypto, Insurance, and Gaming, are now hard-pressed by government and industry regulations to move from the ‘cure’ mindset of fraud detection to the ‘prevent’ mindset of ‘fraud mitigation.’

KYC regulations designed to protect customers from fraud are a clear sign of just how sophisticated fraudster methods have become, such as the deep fakes, swarm attacks, and synthetic fraud mentioned earlier. But the challenge for KYC-regulated industries remains unchanged – it’s about balancing fraud prevention with the customer experience. In addition to being error-prone and arduous, manual processes nip the customer experience in the bud with lengthy processes to verify a person’s identity and ID documents, creating delays, onboarding friction, and high churn rates. Businesses intent on skirting the system or taking shortcuts run the risk of heavy fines and reputational loss.

Protect Your Business From Serious Fraud

AU10TIX Serial Fraud Monitor is the only neural network fraud prevention platform of its kind used by financial institutions around the globe. It offers dual-layer protection by monitoring existing fraud patterns and behaviors and evolving patterns at the customer traffic level. Moreover, Serial Fraud Monitor enables organizations to identify and stop fraudulent customers, recognize customers that accumulate positive scores over time, and give them better service as trusted users. Fraud prevention is critical for financial institutions and all businesses and organizations operating in a highly competitive and heavily regulated

 environment today. By implementing advanced fraud prevention processes, companies have more than a fighting chance – they can reduce financial losses, enhance compliance, boost customer trust and brand loyalty, and remain competitive.