According to FBI statistics, identity fraud is one of the most prevalent forms of fraud in the personal finance sector. It accounts for billions of dollars lost annually, with the average victim losing $1100.
The financial sector is a massive target for malicious actors because of the volume of cash it handles. Even more alarming is the amount of personal data financial institutions hold.
Financial fraud happens when one party takes money or other valuable assets from another through criminal activity or deception. It poses a significant threat to the financial sector, with the most visible impact being the loss of financial resources.
However, we also need to consider the reputational ruin companies face when it is discovered, the regulatory fines and penalties, and the operational costs required to prevent or rectify it.
The Role of Technology in Combating Financial Fraud
Businesses at a higher risk of fraud, especially those in the financial sector and those that handle personal information, have always looked for ways to combat it. Although it remains challenging due to malicious actors always looking for ways to steal finances and data, these institutions are now using technology to reduce fraud.
Traditional Fraud Detection Methods
Before looking at modern solutions, it is crucial to understand where these institutions are coming from. The most common traditional fraud detection methods included signature-based detection, rule-based systems, and manual review.
Manual review is easy enough to understand; look through the “books” and find out if anything nefarious has happened.
Signature-based detection entails checking network activity to see if anything suspicious has occurred. When network activity matches a given signature, the relevant parties are alerted to an intrusion and have to work to thwart it.
Rule-based detection uses rules to assess actions at specific times. A simple rule might be to only authorize connections to specific systems from specific IP addresses. If another IP address tries to connect, that rule is broken and an alert is raised.
While these methods have been somewhat successful, newer technologies and solutions are even better at fraud detection.
Leveraging Machine Learning and Artificial Intelligence in Fraud Detection
Machine learning (ML) and artificial intelligence (AI) have emerged as some of the best tools for combating fraud in the financial sector. Their strengths stem from their ability to analyze massive data sets and pattern recognition. Today, businesses work with a GPU cloud provider to access the computational power they need to train the machine learning models and algorithms that help them detect fraudulent activity.
ML algorithms, as mentioned, are perfectly suited for detecting unusual patterns in transaction data. Any breaks in these patterns, such as the time of activity and the amounts involved, can indicate fraudulent activity. AI, on the other hand, is great at analyzing customer behavior patterns. Any deviations from normal behavior can indicate fraud and the need to take action.
In recent years, banks and credit card companies have also used artificial intelligence to flag transactions occurring outside typical login and translation times, out-of-state transactions, and transactions of a much higher-than-typical value or spend as part of their pattern recognition efforts.
For example, it would not make sense for someone who lives in New York and mainly uses their credit card for small purchases under $1000 to suddenly purchase a $3,000 8K TV in Nebraska. That could indicate fraud unless the account holder can show they initiated the transaction.
Machine learning and artificial intelligence are also helping financial institutions reduce the number of false positives when identifying fraud.
In the past, banks, lenders, and other financial institutions would stop all suspicious transactions.
While commendable, doing this regularly stopped legitimate transactions due to false positives. Now, these technologies are helping reduce instances of such disruptions. Additionally, these institutions are always tuning their models as they receive more data to better differentiate between legitimate and fraudulent activity.
The Role of Big Data in Fraud Detection
Data is the cornerstone on which modern fraud detection is built. It is the raw material machine learning models and artificial intelligence algorithms need to identify anomalies, patterns, and potential fraud.
Financial institutions collect a lot of data besides their customer’s personal and banking information. Although anonymized, your bank likely knows about all your transactions and can map your financial behaviors accurately. While this can be scary in some cases, collecting this data is the best way to protect you.
For example, your bank can use the data it has on you to provide historical context. If you have never shopped at a specific store, the bank can flag that transaction because it does not align with the historical data it has on you, for example.
Crucially, this data is essential for machine learning training. Financial institutions must train their machine learning models on vast amounts of data. Doing this helps them learn and improve, especially as they are fed more data. Using a GPU cloud provider to access the GPU computational power needed is common for these workflows and training exercises.
The key is not only having these vast amounts of data but also ensuring it is of high high-quality and relevant. These institutions must also have the right tools, including machine learning models and artificial intelligence algorithms, to make the most out of it.
Using Biometrics to Prevent Financial Fraud
The most common use of biometrics in the modern world is to gain access. We use our fingerprints and faces to unlock phones and computers and even gain access to buildings. In recent years, financial institutions have incorporated biometrics into their security practices.
If your phone has a fingerprint reader, you know you can initiate transactions using that type of biometric authentication. Doing so ensures you are the only one who can authorize transactions, and this has helped reduce incidences of financial fraud.
Biometrics are not perfect, though. For example, some systems can be fooled with a picture of a user’s face, and two people can have voices that are so similar that they can access each other’s accounts. Financial institutions and their tech partners have been working on it in the last few years, so it is mostly sorted now.
However, the benefits of using biometrics far outweigh these risks, which is why they remain one of the best options for reducing financial fraud.
The risk of financial fraud is higher than ever, with malicious actors looking for and finding new ways to steal your money, assets, and sensitive information. Fortunately, numerous technologies have emerged that are helping customers and financial institutions fight back. Options like machine learning, artificial intelligence, and leveraging big data are also important elements in the fight against financial fraud.