Online Lending Fraud Detection Solution

Loan fraud prevention is performed via multi-factor authentication and device fingerprinting tools. Explore the use of JuicyScore for online lending companies.

Loan fraud detection is an issue financial institutions should not ignore. Analysts expect the online loan industry to exceed the $20,5 billion global market cap by 2026. Digital lending is rapidly turning into the biggest financial sector.

However, with greater success come greater risks of commercial fraud. Hackers develop their malicious approaches to take advantage of financial products delivered online. To overcome these challenges, companies should think of effective financial fraud prevention tools to be integrated. Besides, it is essential to be aware of different lending fraud types prevalent in the financial industry.

How Does Lending Fraud Work?

Loan fraud refers to the type of fraudulent activity when an individual performs an identity theft. The main aim is to use stolen credentials to obtain financial services illegally. For instance, a hacker can use stolen personal data for committing mortgage fraud leaving the victim to pay off the debt.

The method itself supposes stealing credentials like bank account info or social security numbers from the victim. In simpler words, businesses have to deal with the kind of synthetic identity fraud when scammers gain access to others’ info via different techniques.

In some cases, they utilize phishing scams or simply purchase stolen data on the Dark Web. The main problem is that most lending organizations require only baseline personal info to apply for cash. It is quite easy for hackers to obtain and use it for income fraud and other types of malicious activities. This is why lending fraud is so popular nowadays.

Generally, the mot popular loans fraud trends involve the first party loan fraud (when a hacker uses fake credentials) and the third party loan fraud (when fraudsters steal data from third parties).

Here are some of the most common loan fraud types:

  1. Credit Card Fraud – a thief steals information from the victim to apply for a credit card. After receiving money, a hacker racks the debt up and leaves a victim responsible for paying it off.
  2. Car Lending Fraud – works similarly to the first one. The only difference is that hackers use stolen data to get a car loan. The same method ap[plies to personal loan fraud.
  3. Advance-fee loan Fraud – refers to fake “lenders” who charge fees and promise borrowers mild lending conditions despite the credit score. Once they get a fee, you will never hear back from them.
  4. Mortgage Fraud – hackers steal credentials to falsify property ownership and take advantage of mortgages on the victim’s behalf.

COVID loan frauds have been the new trend recently. Fraudsters got around $200 billion which was supposed to be federal aid to support small businesses. With so many different types to manipulate stolen data, lending organizations must have effective fraud detection and prevention instruments to keep their clients and businesses safe.

The Impact of Digital Loan Fraud

More than half of all lenders experience at least one lending fraud within a year. Organizations spend thousands of dollars to clean up the mess and restore reputation after fraudulent activities. Cybercrimes have a great impact on the entire industry. It is not only clients who risk having their info stolen but also financial institutions that risk their reputation, lost funds, and decreased loyalty.

When experiencing lending fraud, organizations face different types of risks. The first one involves loan default when a fraudster does not pay off the debt or repays it fraudulently. Every new default results in extra funds needed for the company to recover. If a lender specializes mainly in unsecured loans, the results can be disastrous.

What’s more, we are talking of lost reputation and customers’ loyalty. One is likely to look for another loan provider instead of working with the one spotted in potentially fraudulent situations. Besides, customers are looking for simple and fast onboarding along with a transparent application process. They will never appreciate someone trying to take advantage of financial products using fake or stolen credentials.

Loan Charge-off Risks

The term “charge-off” means a lender writes the borrower’s account off as a loss. In simpler words, the company terminates the agreement without having the debt repaid. What’s more, the account will be closed for any kind of charges in the future.

Mostly, lenders sell it to collecting agencies and debt buyers. The main problem is that a victim who had his or her data stolen is still obliged to repay the debt. As for the business risk, a loan charge-off is a financial loss. If the creditor can no longer collect revenues generated by its products, it may go bankrupt.

Obstacles to Customer Onboarding

While customer onboarding is supposed to teach potential clients about the company’s products and values, businesses will find it hard after services are compromised. A broken online reputation is very hard to restore. The onboarding process happens at two key milestones:

  1. When a client signs up for the product for the first time (applies for a loan).
    When a client got approved for the first time. He or she actually experiences the product usage.
  2. Using advanced fraud detection software will let businesses ensure safe and fast onboarding procedures with minimum charge-off and lending fraud risks.

JuicyScore Online Lending Fraud Detection Solution

JuicyScore offers practical ways to assist lenders in preventing loan fraud risk. We offer technology guidelines to the lending sector. At one point, businesses may establish lower credit risks and greater approval rates.
Financial organizations can gain from our solution against loan fraud in the following ways:

  • Fraud Detection Algorithms. Based on several behavioral biometric data, we create an accurate end-user linkage by utilizing different device authentication mechanisms.
  • Credit Risks Minimization. The system detects several risk variables. It examines thousands of configurations, qualities, and device-assisted markers assisted alongside the quality of the network infrastructure and Internet connection.
  • Approval Rate Increase. JuicyScore represents an anti-fraud instrument kit designed to assess a broader range of factors, including disposable income and low-risk market groups. The solution examines various characteristics that aid in identifying a reliable client who is willing to cover the debt.

In the end, online loan providers receive a full-scale lending fraud prevention toolkit that uses a collection of indices to generate a range score. With the assistance of 214 variables found in the technological vector, the solution enables enterprises to raise revenues and customer loyalty levels.

Utilize the Power of Artificial Intelligence

As the online loan sector expands, so does the volume of transactions. Furthermore, we observe an increase in the sophistication of fraudulent actions. AI-based fraud-detection systems can reduce the possibility of human error, process tremendous data volumes in real-time, and speed up the potential customer evaluation process WITHOUT using personal data.

AI-based systems are taught to perform technical analysis and identify various tech and behavioral patterns. The system suggests possible fraud risks relying on the collected dataю The system tracks a variety of parameters as a part of a given pattern. Artificial intelligence analyzes different behavioral parameters to detect suspicious actions, rare events, and other types of anomalies that are not typical for the user.

Boost the Decision Making with Reliable Data Vector

As a result, lenders create a customizable scoring model that helps to contribute into a better decision-making process. Businesses gain enough data to automatically evaluate a client’s trustworthiness on the application stage with no loan charge-off risks.

Our solution is capable of processing huge data volumes without using personal data to ensure accurate decision-making for loan providers. The system can detect any of the following:

  • Randomizers and Anonymizers – when clients try to hide their online activity using device-assisted applications for excessive privacy.
  • Device Manipulation – the solution ensures comprehensive analysis including authentication tools. They can identify methods for manipulating and virtualizing devices, creating multiple accounts, etc.

More than 65,000 criteria make up our data vector. Lending companies can use it to build their own basic range score. The system is fully adaptable.

Key Features

Based on the experience of our partners, the solution assists lending institutions in reducing the number of high-risk application inflows by 75+% and the number of non-performing loans (NPLs) by 90+ by 5+% in absolute terms.

Based on behavioral biometry and device configuration markers, the software generates the end-user digital profile. It helps to identify different kinds of fraudulent activity concerning the end-user or device itself, as well as data related to the Internet connection, installed apps, software parameters, etc. It guarantees a reliable model for estimating fraud risk.

Device Fingerprint Analysis

Apart from baseline attributes, the solution processes a combination of secondary loan fraud risk factors. The idea lies behind the device characteristics monitoring. Our tools can detect the type of carrier (tablet, desktop, laptop, or mobile), screen size, display quality, RAM, and other crucial attributes necessary for accurate device fingerprinting.

As a result, the system detects any kind of anomalies and mismatches. They can be a sign of device randomization or cloning, remote access, different fake routing markers, and so on.

Behavioral Analysis of Users

Behavioral characteristics come as a crucial part of the credit score methodology. The system uses a mix of aggregated variables to examine various parameters. They include duplicated or randomly selected devices, the number of apps used on the same device in a specific period, etc.

Behavioral metrics include dwell or flight time, online sessions, and average typing or content reading speed.
Numerous integrated markers form the data vector background. They all work as a single instrument to help loan providers identify risky customer groups that might be fraudsters.

How Our Solution Works

The system uses filters to detect and sort out high-risk flows according to device characteristics (OS, browser version, Internet connection quality, behavioral factors, and so on) into niche-specific segments.
JuicyScore provides high informative value for the enrichment of the loan risk model by enabling you to assess each user who accesses your financial products. Ultimately, companies might create a low-cost risk-assessment strategy that results in lower operating expenses and a higher loan approval rate.


We utilize the best user and device authentication technology available on the market. Together with reliable, timely device fingerprinting it lowers potential risks of fraud. Our solution involves multiple authentication methods making it far more difficult for fraudsters to take advantage of financial services.
MFA is an effective authentication method that provides an additional layer of protection to guarantee that users’ data is protected against all kinds of intrusions to the detriment of business organizations and clients.

Flow Segmentation

The system is capable of sorting out potentially risky flows and dividing them into specific categories depending on the score. Our data vector processes huge amounts of data to analyze various technical attributes, behavioral factors, and other important characteristics that help detect and prevent different fraudulent approaches.

Flow Quality Evaluation

Using specific variables, the system evaluates flows to pinpoint potentially risky sessions. It is possible to detect different device manipulation methods while assessing network and application quality. In the end, you have a clear digital profile of all potential applicants and the ways they engage with the online lending services provided. Book a demo with us to learn more.


What Are the Most Common Types of Loan Fraud?

Credit card frauds are the most typical way of loan fraud. Individuals steal bank account information to apply for a credit card. The latest stats show that 81% of all data breaches involve stolen passwords. This makes identity theft the most common type of online fraud.

How Do You Prevent Loan Fraud Risks?

Companies can apply extra means to prevent loan fraud risks. Some organizations use enhanced ID verification. Lenders ask applicants to provide additional government-issued IDs or implement KYC compliance. Facial recognition via a selfie or short video call is also a common practice today.

What Are the Best Practices for Implementing Fraud Prevention?

Multi-factor authentication and device fingerprinting help detect and prevent different types of loan fraud. They help track possible mismatches that are the red flags of a digital threat. Advanced toolkits process different characteristics using niche-specific markers that spot technical and behavioral anomalies.

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