Table of Contents
November. The start of peak shopping season – Thanksgiving, Black Friday, Cyber Monday and the countdown to Christmas. It’s a joyous time for consumers – but it’s also peak time for fraudsters to exploit the increased online activities and surging of private data across networks.
For businesses, this period is equally nerve-wrecking. Teams are under immense pressure to drive sales and give year-end wrap-ups, while also having to deal with a surge in activity and customer data.
In the midst of all this chaos, fraudulent data is overlooked until it’s too late. By the time fraudulent data is detected, the damage may have already been done. Hackers could have exploited vulnerabilities, stolen sensitive information, or disrupted critical operations. the data.
How do you mitigate these challenges?
There is no simple formula, however, there are processes, frameworks, and technologies you could implement to prevent fraudulent data from hijacking your data’s integrity.
Our team of experts have put together this comprehensive guide for business managers, data managers, IT managers, and anyone working directly with customer data to meet business objectives.
We also show how you can leverage WinPure’s AI-powered technology to catch fraudulent data without requiring extensive human resources or manual interventions.
Let’s roll.
Different Types of Frauds Businesses are Exposed To
Fraudulent data is now a major concern for organizations across industries. With billions of dollars lost to identity fraud, merchant fraud, financial fraud – and now AI scams, businesses are truly at their wits’ end.
In such critical times, businesses must adopt proactive measures to control fraudulent activities. Moreover, team members working on customer data must be given basic training on identifying fraudulent data.
To help, here are some common forms of fraudulent data.
1. Synthetic identity fraud or Frankenstein fraud
What it is: Synthetic identity fraud, sometimes called “Frankenstein fraud,” involves blending real and fake data to create new identities that appear legitimate.
Example: A fraudster uses a stolen SSN along with fake personal details to create a new account and purchase electronics during Cyber Monday. After establishing a good transaction history over the holidays, they max out the account and abandon it, leaving the retailer to absorb the unpaid balance.
Impact: Synthetic identities are difficult to detect as they mimic real profiles, making it hard for systems to flag them. Businesses unknowingly interacting with synthetic identities face financial losses from uncollectible accounts, as these identities are used to secure credit or make purchases with no intent to pay back. Moreover, synthetic identity fraud often bypasses traditional verification systems, creating ongoing vulnerabilities that weaken data integrity and customer trust.
2. Duplicate identities with variations in record details
What it is: Duplicate records occur when the same individual or entity is recorded multiple times with slight variations, such as different spellings or address formats.
Example: For example, a fraudster named “John Smith” creates multiple online shopping accounts to exploit a retail store’s “first purchase” discount. To avoid detection, he uses variations of his name and address, such as “John Smyth” or “Jonathan Smith.” He registers these accounts with slight address changes like “123 Main St.” and “123 Main Street” or by adding unit numbers, even though he resides at a single location. Through these variations, John repeatedly qualifies for the discount meant only for first-time customers, costing the retailer in lost revenue.
Impact: Duplicate data not only skews customer insights but also hides potentially fraudulent patterns that are difficult to detect on a spreadsheet. For data teams, this type of fraud can lead to inflated customer counts, unreliable reporting, and increased costs as efforts to clean up duplicates take time and resources.
3. Account takeover (ATO) fraud
What it is: ATO fraud involves unauthorized access to a legitimate customer’s account. Fraudsters use phishing, credential stuffing, or social engineering to gain entry, allowing them to control accounts, alter details, and initiate unauthorized transactions.
Example: For example, a customer receives an email that looks like a holiday discount alert from a popular retailer. After clicking the link and entering their credentials on a fake login page, fraudsters use the stolen details to access their account, change the shipping address, and make large purchases, which go undetected until after the holidays.
Impact: This type of fraud compromises customer trust and results in financial losses due to unauthorized purchases or fund transfers. Businesses face increased security costs, potential regulatory fines, and reputational damage as customers lose faith in the security of their accounts.
4. First-Party fraud or misrepresentation
What it is: First-party fraud occurs when an individual uses their own information but misrepresents their identity, financial situation, or intentions to gain credit or other benefits they don’t intend to repay. Since their actual information is used, detection can be challenging.
Example: A customer buys high-demand electronics during a holiday sale, knowing the item has a no-questions-asked return policy through the season. After receiving the product, they file a claim with customer service, saying the item arrived damaged or didn’t arrive at all. The retailer, prioritizing fast holiday service and customer satisfaction, issues a refund without requiring a return. The customer keeps the item, as well as the refunded amount, resulting in a loss for the retailer.
5. Gift card fraud
What it is: Gift card fraud involves fraudsters tampering with or exploiting gift card systems, often draining balances before legitimate customers can use them. This type of fraud spikes during the holidays when gift cards are a popular purchase.
Example: A fraudster steals gift card codes by tampering with physical cards or by purchasing and quickly reselling them online. Legitimate customers attempting to redeem the cards find the balances already depleted, leaving customer service to handle the complaints and resulting in customer dissatisfaction and financial losses.
Impact: For data teams, tracking and managing gift card data becomes complicated, as fraudsters’ activities can lead to discrepancies in issued versus redeemed balances, customer complaints, and lost sales. Fraudulent gift card activity also disrupts inventory and financial data.
6. Shipping and address fraud
What it is: This fraud occurs when fraudsters use fake or altered addresses to misdirect or intercept packages. During holiday shipping rushes, fraudsters exploit leniencies in address verification systems to reroute goods.
Impact: Shipping fraud affects data accuracy, logistics tracking, and customer experience. For data teams, mismatched shipping and billing information leads to database inconsistencies and affects fraud detection models.
Example: A fraudster places multiple orders using a stolen account and a fake address, altering the delivery location after purchase. The goods are intercepted, but the legitimate account holder is unaware until they are billed, causing a loss for the retailer and confusion in address tracking systems.
This list barely scratches the surface. Organizations now don’t just have to deal with “basic” fraud, they also have to watch out for AI generated fake profiles, fake phishing calls and audios mimicking the original institution with astounding accuracy.
In the battle against rising cyber-attacks caused by fraudulent data, businesses need more rigid data governance frameworks, more training for data-facing teams, and better tool stacks.
Why Are Fraudulent Identities So Hard to Catch?
Fraudsters are no longer restricted to basic identity theft. They employ complex strategies that often bypass standard detection methods. Unlike typical data anomalies, fraudulent identities are crafted to mimic legitimate behaviors, making detection difficult, especially during peak transaction periods when systems are flooded with data.
Here’s a deeper look into why these fraudulent identities remain undetected:
The complexity of synthetic identities
Synthetic identities, a sophisticated form of identity fraud, combine real and fictitious data to create a profile that looks genuine on the surface. For example, a fraudster might use a legitimate Social Security number but pair it with a fabricated name and address. This identity can pass through standard verification methods because parts of it are authentic, allowing fraudsters to establish credit lines, make purchases, and operate under the radar for extended periods.
By the time these synthetic profiles are flagged, the financial and reputational damage may already be done.
Data silos and fragmentation across systems
Many organizations struggle with data stored in fragmented systems that don’t fully integrate, leading to isolated pockets of customer information. Fraudsters exploit these gaps by registering multiple accounts with minor variations like different spellings, email addresses, or slightly altered addresses. Without centralized data linkage or advanced identity resolution, such variations remain unflagged, enabling fraudsters to operate without triggering alerts.
For example, a single user might exist under “John Doe” in one department and “Jonathan Doe” in another, concealing their true nature as a single fraudulent entity across multiple records.
Account takeovers and social engineering
Account Takeover (ATO) fraud further complicates detection by exploiting human vulnerabilities. Fraudsters use tactics like phishing to gain access to legitimate accounts, effectively bypassing standard security checks. Once they control an account, they can initiate unauthorized purchases, change details, and drain funds. Because the activity occurs within a genuine account, it’s challenging for businesses to distinguish between legitimate customer behavior and fraud.
For example, if a fraudster initiates purchases or refunds that mimic real transactions, these can easily go unnoticed until it’s too late.
Adoption of AI by fraudsters
As businesses use AI for fraud prevention, fraudsters are also using AI to stay ahead. Advanced algorithms can now generate highly realistic fake profiles, complete with fabricated transaction histories, making it difficult to differentiate between genuine and fraudulent activity. Fraudsters also use AI-driven deepfakes and voice synthesis to bypass security measures that rely on biometrics or voice recognition.
As these techniques become more sophisticated, businesses face greater difficulty in ensuring the integrity of their customer interactions and preventing fraud without overwhelming their systems with false positives.
Time-Lagged detection and the “Bust-Out” tactic
Fraudsters often take advantage of delayed detection processes. Synthetic identities, for instance, may undergo “bust-out” scenarios where the fraudster builds up a credible transaction history over time before maxing out credit limits or draining accounts. Since traditional fraud detection methods rely on historical data, they may not identify these activities until significant damage is done.
In high-transaction seasons, such delays only compound the difficulty of separating real customers from fraudsters, as the burst of activity can mask fraudulent actions until the impact is undeniable.
Some Real-World Cases of Fraudulent Data Causing Havoc
Duke University Cancer Research Fraud (Healthcare/Research)
Dr. Anil Potti, a cancer researcher at Duke University, falsified data to make it appear that his experimental cancer treatments were more effective than they were. He misrepresented the success of a personalized treatment for cancer patients, resulting in hundreds of patients enrolling in flawed clinical trials. This fraud led to significant harm, legal repercussions, and a damaged reputation for Duke, which ultimately settled lawsuits with affected patients and withdrew Dr. Potti’s research findings from major medical journals.
Giant Tiger Data Breach and Fraud (Retail)
In 2024, Giant Tiger experienced a data breach due to compromised data from a third-party vendor. The breach exposed customer information, which was then used by fraudsters to carry out unauthorized purchases. This incident forced Giant Tiger to handle public relations, implement stronger cybersecurity measures, and deal with regulatory pressures due to the compromised customer data, underscoring the risks of third-party data handling without adequate security measures.
Lehman Brothers Mortgage Crisis (Finance)
Lehman Brothers played a key role in the 2008 financial crisis, largely due to fraudulent mortgage data practices. The firm overestimated borrowers’ ability to repay loans by issuing “Alt-A” mortgages to individuals without proper documentation. These mortgages were bundled into mortgage-backed securities, creating misleading data on loan quality. When the real risk became evident, Lehman collapsed, catalyzing a global financial crisis. The misrepresentation of mortgage risk data was a central factor in this catastrophic failure.
Public Health England COVID-19 Data Error (Public Health)
In 2020, Public Health England mistakenly underreported over 15,000 COVID-19 cases due to a technical error involving outdated data formats. This error caused delays in contact tracing, allowing potentially infectious individuals to go unnotified. The incident underscored the critical importance of accurate data handling, particularly in public health, as the oversight risked exacerbating virus spread during a high-stakes period
These cases show how data fraud or mishandling can lead to public health risks, financial crises, and severe reputational damage across sectors. Rigorous data management practices and verification systems are essential to avoid such crises and to maintain trust with stakeholders.
How Fraudsters Target Companies with Fraudulent Data
1. Data collection and identity theft
Fraudsters begin their schemes by gathering vast amounts of personal data through various means, including data breaches, phishing attacks, and purchases on the dark web. High-profile breaches, such as the Equifax incident, have exposed millions of records, and social engineering tactics like phishing further trick individuals into revealing sensitive information. With an estimated 4.1 billion records compromised globally in 2021 alone, fraudsters have a wealth of personal data to exploit as the foundation for their fraudulent activities.
2. Creating synthetic identities or manipulating data
Once they have this information, fraudsters often create synthetic identities by blending real data like Social Security numbers with fake names or addresses. This tactic allows them to build credible profiles that can slip through standard verification checks undetected. Synthetic identity fraud is particularly costly, with businesses losing over $20 billion each year due to the difficulty of distinguishing these fake identities from legitimate ones.
3. Exploiting weak data validation systems
Armed with synthetic identities, fraudsters target companies that rely on outdated data validation systems. Many businesses still use static data points and exact matches, which fail to detect slight variations in records. By creating multiple profiles with minor differences, such as abbreviating “Street” to “St.,” fraudsters can bypass duplicate detection and blend into customer databases.
4. Accessing financial services or benefits
With these established fake identities, fraudsters gain access to financial services, credit accounts, or loyalty programs. They exploit these services to obtain goods, credit, or benefits with no intent of repayment, extracting maximum value until the fraud is eventually discovered. Financial institutions face substantial losses, as synthetic identity fraud now accounts for around 20% of credit-related financial damages.
5. “Bust Out” or repeating the cycle
The cycle often concludes with a “bust out,” where fraudsters max out credit lines or drain account funds before abandoning the identity altogether. Many then start the process again, using multiple synthetic identities across different organizations. This cycle is difficult to break, with around 85% of synthetic identities going undetected in organizations that rely solely on traditional data validation methods.
When are Companies Most Vulnerable to Fraud?
Companies are particularly vulnerable to fraud in the following contexts:
- During High-Transaction Periods
- Seasonal Peaks: Holidays, sales events, and tax seasons significantly increase transaction volumes, creating high demand on resources and systems. Fraudsters capitalize on this by blending fraudulent transactions with legitimate traffic, making it harder for companies to identify anomalies in real time. During Black Friday and Cyber Monday, for example, fraud attempts often increase by up to 30% due to the influx of online shopping
- Operational Strain: The increased workload during these times may push companies to prioritize quick processing over thorough verification, inadvertently lowering fraud detection safeguards. This makes peak seasons particularly attractive to fraudsters looking to bypass standard verification steps.
- Cross-Platform and Multi-Device Environments
- Multiple Payment and Login Options: Customers often use various payment systems and login options (e.g., Facebook, Google, email) on the same site, creating data silos that are difficult to connect. If one account is compromised, fraudsters can gain access to other linked accounts. Companies often struggle to consolidate these separate data points, allowing fraud to slip through unnoticed unless reported by the customer
- Increased Device Usage: With customers accessing accounts from multiple devices, it becomes challenging for companies to monitor for consistent behavior, as device-based security and fraud detection systems may fail to correlate fraudulent actions across platforms
- Third-Party Partnerships and Vendor Integrations
- Data Security Weaknesses in Third-Party Systems: When companies rely on external vendors for services like payment processing or customer data storage, they are vulnerable to any security weaknesses in these third-party systems. Fraudsters target these integrations, knowing that many companies lack visibility into vendor-level security practices.
- Compromised APIs and Shared Data: Fraudsters may exploit vulnerabilities in application programming interfaces (APIs) or shared data channels between a company and its partners. Misconfigurations or weak authentication protocols can allow unauthorized access to sensitive customer data
- Onboarding and Account Creation
- Synthetic Identity Fraud: Fraudsters use stolen or fabricated information to create synthetic identities during account registration, which can then be used to exploit credit lines, loyalty programs, or services. Traditional validation methods struggle to detect these fraudulent identities, particularly if they use valid but slightly altered information like an authentic SSN with a fabricated name
- First-Party Fraud in New Accounts: Some individuals create accounts with the intent to commit “first-party fraud,” misrepresenting information to obtain credit or discounts they don’t intend to repay. This is especially prevalent in financial services where customers inflate their income or creditworthiness on applications
- Data Silos and Fragmented Data Management Systems
- Disconnected Data Systems: When companies have data stored in isolated systems across departments (e.g., sales, customer service, billing), it creates challenges for fraud detection, as fraudsters can exploit gaps between these systems. Without a centralized view, it is harder for companies to spot discrepancies or unusual patterns in customer behavior.
- Difficulty in Linking Customer Data Across Systems: Without advanced data linkage technology, companies cannot easily connect related customer profiles or detect fraud attempts that span multiple departments or services, making them more susceptible to fraud
How Can Companies Solve Identity Fraud Challenges?
To address identity fraud challenges effectively, companies must deploy a layered approach that combines advanced technology with strategic data management practices. Here are essential steps businesses can take to strengthen their defenses:
✅ Implementing Advanced Identity Verification Systems
Companies need to adopt dynamic, AI-driven identity verification systems that analyze real-time behaviors and complex data patterns. Techniques like behavioral biometrics, which monitor individual actions like typing speed and navigation patterns, help distinguish legitimate users from potential fraudsters. By applying machine learning models that continuously adapt to new fraud tactics, businesses can proactively detect anomalies and prevent unauthorized access.
✅ Integrating Centralized Data Linkage and Identity Resolution
Businesses should implement centralized data management solutions that facilitate entity and identity resolution across all customer records. By connecting data points, such as varying address formats or multiple accounts, these solutions can detect suspicious inconsistencies. An integrated data environment allows for a comprehensive view of customer interactions, which not only strengthens AI Powered fraud detection but also supports accurate reporting and customer analytics.
✅ Utilizing Multi-Factor and Adaptive Authentication
Multi-factor authentication (MFA) adds a crucial security layer by requiring additional verification steps beyond usernames and passwords. However, adaptive authentication goes a step further by adjusting security measures based on risk. For instance, a customer logging in from a familiar device may require standard MFA, while access attempts from unusual locations or devices may trigger additional verifications. Adaptive authentication helps balance security with user experience, increasing barriers for fraudsters without compromising legitimate customer access.
✅ Deploying Continuous Monitoring and Anomaly Detection
Fraud detection should not be limited to point-in-time verifications. Continuous monitoring through AI-powered anomaly detection systems allows companies to track behavioral changes over time, making it easier to spot suspicious activities that would otherwise go unnoticed. For example, abrupt increases in transaction volume, unusual login times, or sudden changes in account information can signal potential fraud attempts. Continuous monitoring provides a proactive approach, enabling companies to respond to threats before they escalate.
✅ Training and Educating Data-Handling Teams
Human oversight remains a critical factor in combating identity fraud. Training data-handling teams on recognizing and responding to fraud indicators enhances the effectiveness of technological measures. Employees should be well-versed in detecting social engineering tactics, such as phishing, that fraudsters use to gain unauthorized access. Regular training on fraud trends and internal reporting mechanisms can significantly reduce the risk of internal oversights and errors that fraudsters often exploit.
✅ Establishing Data Governance Frameworks
Effective data governance is essential for managing the integrity and security of customer information. Companies should implement governance frameworks that define data access, usage policies, and security standards across all departments. By enforcing consistent data practices and establishing regular audits, businesses can reduce vulnerabilities in their data processes. Data governance also ensures compliance with regulatory standards, reducing exposure to fines and reputational risks associated with data mishandling.
To truly tackle identity fraud, data quality must be at the core of these strategies. Without reliable, consistent, and integrated data, even the most advanced AI Powered fraud detection measures are compromised. Strengthening data quality practices is the first essential step in building a robust defense against fraudulent identities.
Data Quality as The First Step to Fighting Fraud
Fraud is often built on the manipulation or fragmentation of identity data. Fraudsters exploit slight variations in names, addresses, or contact information to create synthetic identities or evade detection.
While you can perhaps reduce fraud with real-time monitoring, the fact remains that if you don’t have a data quality parameter in place, chances are you’re already fighting a losing battle. Without reliable data quality, it’s nearly impossible to link disparate data points to a single legitimate entity, a shortfall that fraudsters can easily exploit.
How?
Here’s a quick explanation.
Dirty, duplicate, disparate data are the three primary evils to watch out for.
Here’s how they look in a database:
Now imagine the same data challenges at scale – in millions of records.
That’s where you would need a data quality solution that also enables you to resolve variations in identity data.
Identity Resolution and Entity Resolution – The Key to Detecting Fraudulent Identities
The battle against fraudulent identities requires a solid foundation, and that begins with identity and entity resolution. Fraudsters are no longer just gaming the system with easily detectable mismatches. Effective identity and entity resolution link data points, detecting irregularities that would otherwise remain invisible. WinPure’s AI-powered entity resolution uses advanced fuzzy matching and custom definitions to unify disparate records, linking subtle variations that hint at fraudulent intent.
A synthetic profile built with “Richard Smith” as the name, but with variations like “Rich Smith” or even “R. Smith” across multiple records. Traditional systems might miss these discrepancies. WinPure examines address histories, behavioral data, and transaction patterns to reveal that these identities stem from the same source.
Another example lies in cross-border address discrepancies—addresses formatted differently across regions. For instance, a fraudster might register multiple accounts under “10 Downing St.” in one system and “10, Downing Street” in another. WinPure’s global parsing algorithms recognize and reconcile these variants, highlighting hidden connections across records that evade standard checks.
This approach does more than spot isolated irregularities. It builds a cohesive data network that uncovers relationships between records, patterns of manipulation, and connections. For businesses, this means not just reacting to fraud after it happens but actively dismantling its very foundation.
Final Thoughts
With global losses to fraud surpassing $5 trillion, businesses are not just facing financial risk. They’re facing reputational fallout that is hard to repair. This is where the importance of a data-centered approach comes into play.
Companies need a foundation of clean, connected data and advanced AI-driven identity and entity resolution. It’s no longer enough to catch fraud after it happens. Today’s threat landscape requires a proactive approach where AI uncovers hidden patterns and connects disparate data points—exposing fraud networks that would otherwise go undetected.
But AI alone isn’t a silver bullet. Without reliable, high-quality data, even the most advanced systems miss critical connections, leaving gaps that fraudsters readily exploit. The fusion of data quality management and intelligent identity resolution is now a fundamental part of safeguarding trust, preserving revenue, and positioning businesses to act ahead of fraud—not after it strikes.
