Did you know First-party fraud is a growing $100 billion problem? First-party fraud (also known as ‘Friendly Fraud’), where individuals use their own personal information to knowingly and deceitfully commit fraud for financial gain. This is pervasive, with 35% of Americans in surveys admitting to committing some form of first-party fraud, often driven by economic hardship1. The challenge with first-party fraud is that it is much harder to detect than identity theft or synthetic fraud, as the perpetrator is using their own legitimate personal information.
This whitepaper explores the type of First-Party Fraud, current market trends, challenges with existing fraud solutions and case study on how VALID AI-powered solutions are helping fintech, banks to combat fraud and proactively mitigate fraud risks.
First-party fraud poses a significant threat to financial institutions, resulting in substantial losses and reputational damage. Traditional approaches to combat this issue often fall short due to the fragmented nature of data and the lack of comprehensive insights into fraudulent activities. In response, the establishment of a First-Party Fraud Data Consortium emerges as a promising solution.
First-party fraud, a rapidly escalating threat, involves individuals knowingly misusing their own legitimate personal information to deceive businesses and financial institutions for illicit financial gain, often driven by economic hardship. Unlike identity theft, where perpetrators steal someone else's identity, first-party fraud is committed by the individual themselves through misrepresentation of details like income, employment status, or intent to repay loans. Common tactics include application fraud, sleeper fraud, chargeback abuse, and refund fraud. Detecting and preventing first-party fraud poses unique challenges as perpetrators use their real identities, making it harder to distinguish from legitimate consumer behavior. Combating this escalating $100 billion problem requires a multi-layered strategy - leveraging advanced analytics, machine learning models trained on cross-industry insights, robust identity verification, and tailored fraud mitigation policies.
There are common types of first-party fraud include2:
Sleeper fraud: Opening a loan account with no intention of ever repaying the debt, only to disappear after making a few payments
There are various interesting statistics identity and first party fraud verification.
Leveraging data consortiums and pooling data from multiple sources to identify patterns of first-party fraud across different platforms and accounts. This helps detect fraudulent behavior that may not be visible from a single company's data.
Using advanced analytics and machine learning models trained on consortium data to accurately identify first-party fraud risk during account opening and onboarding processes.
Fraudsters are exploiting account funding and/or item verification processes by providing false information or misrepresenting their financial circumstances to obtain services or credit lines. Common tactics include income exaggeration, employment fabrication, and address fronting
Originations: First party fraud involves an individual who makes a promise of future repayment in exchange for goods and services without the intent to repay.
Account Management: Continuously monitoring customer behavior and account activity patterns post-lending to detect potential first-party fraud bust-outs before full credit lines are maxed out.
Valid Edge is an AI-powered data consortium, a game-changer solution to combat identities and fraud detection challenges. Valid Edge comprises of 300M+ unique bank accounts with $6T annual check transaction volume in 2023, which is 22% of $27T total check payments in US per Federal Reserve Payments Study (2021)5. By aggregating data from multiple sources, primarily from 7 out of top 25 banks, the consortium fosters a collaborative environment for fraud detection and prevention. Leveraging advanced analytics and AI-powered machine learning techniques, the consortium enables real-time identification of suspicious behaviors and patterns, empowering members to proactively mitigate fraud risks.
Valid Edge Case Study: Identify Fraudulent Checks
Problem Statement: There is a growing check fraud in banks, increasing 50%+6 YoY. It is critical for banks to identify the fraudulent checks and ways to reduce fraud $ losses
Clients: 3 out of top 20 banks
VALID has developed ML-algorithm based fraud consortium and leveraged behavior analytics to study 3 out of top 20 banks check deposits activity of 2M+ checks worth $3T and predict the likelihood of fraudulent checks.
By performing the analysis based on the activity on check numbers, VALID classified the checks into high vs. low risks level based on the check deposit transactions.
| Category | High Risk | Low risk | |
| % of total items | 22% | 78% | |
| % of total returns ($) | 68% | 32% | |
| Likelihood of return | 5X | 1X |
The check deposit data was used from 3 out of the top 20 banks. This included ~2M checks with $3T+ check deposit amount from the 3 months of transaction activity. These 2M checks were categorized into 6 risk grades based on check number range deviation grades as shown below. The check number deviation grade is defined based on the comparison of check number to the min/max historical check numbers from the payer.
| Risk category | Check Number Pange deviation |
definition |
|
| High Risk |
-1 |
Null value when no check number in historical data with last 6 weeks |
|
|
0 |
High deviation 1000+ |
|
|
|
1 |
Medium deviation 100-1000 | ||
|
2 |
Low deviation 1-100 | ||
| Low Risk |
3 |
Low volume (<=3 items) | |
|
4 |
Multiple ranges | ||
|
5 |
In range |
When the ‘check number range deviation’ category was analyzed, the checks with <= $100 and check numbers with < 100 value were excluded. Just taking mobile check deposits of 2 months of activity from the 3 banks, translated to 2M+ checks. Though the overall return rate was 124 bps but was highly skewed towards high-risk checks and clearly triage the risk into high vs. low-risk checks. After the risk categorization, % of items, return rate and % of return amount were analyzed using various fraud consortium features. It was observed that there is 5X likelihood of fraud in high-risk checks vs. low-risk checks, just based on transaction activity.
First Party Fraud is a growing $100 billion problem. The challenge is that it is much harder to detect than identity theft or synthetic fraud, as the perpetrator is using their own legitimate personal information. Signals like having multiple closed accounts due to fraud or accounts with many authorized users can indicate higher risk but measuring true consumer intent is extremely difficult. To effectively combat first-party fraud, organizations need a comprehensive, cross-industry approach i.e. fraud data consortium.
Using AI-powered analytics and machine learning to identify predictive patterns of fraud behavior can help both in account-level, transaction monitoring and several lending related use cases to intelligently detect fraudsters.