Nov 24, 2022
Learn from a former risk officer how to develop sophisticated fraud detection models that understand not only a fraudulent action but also the intention behind it.
Customer Success Manager
In this age of increasing crime, fraud prevention is undoubtedly a pressing issue, especially within the financial sector, which is a highly targeted market due to its high asset volume. A study performed in 2021 by Sophos, a world leader in cybersecurity, reveals that 34% of financial service organisations were attacked by ransomware, allowing scammers to commit fraudulent activities. As a result, an average of 2.1 million dollars was spent to restore damage caused by these attacks. According to Cybersecurity Ventures, cyberattacks are expected to cause $10.5 trillion in damages by 2025 and cybercrime costs include fraud.
To avoid being blindsided by any attack, a strong fraud prevention strategy and fraud detection tools are now not only expected but crucial to protecting your business. This two-part article explores the different types of fraud, fraud characteristics, and how new and innovative tools enhance fraud detection methods.
First Party Fraud and Third Party Fraud are two categories of fraud attacks that share a similar goal but differ in methodology.
First-party fraud involves committing a crime with false information about oneself. Rather than misrepresenting their identity, the fraudster is deceptive about their information and intentions. An example of this might be a person applying for a loan without the intention of paying it back.
After the pandemic, First Party Fraudsters' profiles have changed significantly. Financial difficulties led many good customers to resort to this fraudulent behaviour and provide false information about themselves. In fact, a recent CIFAS report states that 1 in 13 Brits admit to committing at least one form of first-party fraud in 2021.
There are many types of First Party Fraud, the most common within the financial industry being:
Third Party Fraud involves impersonating a real person and misrepresenting their identity to gain credit or products. For example, this occurs when a malicious actor uses another person’s personal details to open new accounts or take over existing ones. Fraudsters can also create fake identities using stolen and false data referred to as manufactured identities. The main difference between Third Party Fraud and First Party Fraud is that the former has a clear victim and, in general, is associated with organised criminal activities.
According to Experian’s Fraud Index Report, fraud varies significantly across lenders’ portfolios and product types. For example, this report shows that Third Party Fraud is a growing problem for current accounts, as well as loans, cards and savings, while First Party Fraud affects other products, such as mortgages and asset finance.
A fraudulent application is a type of criminal activity in which a dishonest individual applies for a loan, credit card, or line of credit with no intention of repaying it. Fraudsters of this type keep costs to a minimum and use a single device for various purposes to maximise profits.
According to the Federal Trade Commission, there were 204,967 loan fraud reports in the United States in 2020 alone. Finding these fraudsters is a top priority, so what are some characteristics of fraud strategies to combat fraud?
As fraud affects the financial sector, risk officers need to find new strategies to avoid fraud across the entire organisation better. These include:
Due to the rise of cyberattacks in recent years, and the proliferation of cybercriminals committing fraudulent activities, companies, especially financial institutions, have been forced to innovate and get more sophisticated to combat fraud. Behavioural Biometrics and Analytics are one of the most popular tools to fight fraud right at its source.
Behavioural Biometrics uses personal and physical identification coupled with interactive gestures, such as how an individual types on a keyboard or moves a mouse and compares those characteristics with typical digital behavioural traits from fraudster users. Behavioural Biometric approaches can often fall short of providing the highest levels of security since sophisticated criminals can outsmart these systems easily. Face and fingerprint IDs can be replicated and disseminated globally in minutes, as evidenced in the past.
Behavioural Analytics provides wider behavioural models using neuroscience-based analysis to assign real-time risk scores to responses using a mouse, touch screen or other devices. Behavioural Analytics examines user state of mind, hesitancy, cognitive load and answers switching, providing a clear and relevant portrait of user intent. Thus, Behavioural Analytics goes beyond simply identifying bad actor signals. Through this analysis, it is possible to score and rank real-time behavioural fraud attributes for each user, helping security systems score potential risks while protecting good customers.
Needless to say, both Behavioural Biometrics and Behavioural Analytics use Machine Learning (ML) and AI to improve the accuracy of their fraud detection mechanisms. Since the introduction of machine learning, fraud has been easily detected. In the past, it could only be detected through a Rule-Based Approach which uses a set of on-surface and evident signals written by fraud analysts. The use of ML uncovers subtle and hidden events in user behaviour. It is possible to create algorithms that process large datasets with many variables and find hidden correlations between user behaviour and the likelihood of fraudulent actions. However, this does not mean the Rule-Based Approach should be ignored. In fact, it can be a powerful supplement to machine learning, and combining more than one tool will probably result in a more thorough analysis.
ML is best suited for fraud detection since it offers four top benefits:
Thus, the more complete fraud detection solutions are the ones based on ML and Behavioural Analytics. These combined offer a more sophisticated technology, understanding a fraudulent action and its intention and providing a holistic approach to human activity.
Credolab’s solution uses ML algorithms to identify behavioural patterns for early fraud detection. Anonymous data stored in smartphone devices are converted into quantitative figures that assess the person’s probability of defaulting and also the probability of committing fraud. Using a combination of the proprietary smartphone device ID and first-party metadata, credolab can arrive at a real-time fraud score for each applicant in a non-intrusive, privacy-consented and secure manner.
Clients can collect specific information to perform checks for their fraud strategy, securing an effective user verification while exposing possible fraudulent behaviours. For example, these insights from smartphone and web metadata can:
All of the above shed light on users’ behaviours, keeping the identity of each user protected and avoiding the kind of cyber crimes that seek to steal customer information and commit fraudulent activities. Uncover fraudsters more easily by including existing device data in decision-making strategies, especially fraud strategies.
Taking a proactive approach to fraud can help financial institutions avoid big losses. Risk and fraud management can be improved by investing time and money in new technologies and strategies. By combining human resources management and fraud detection training with cutting-edge technology, a strong fraud prevention strategy can uncover the real intent behind each application before fraud occurs.
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