Fraud Detection and Machine Learning

Technology has had a significant impact on the banking and financial services industry. One of the places where it had the most significant effect was on payment processing and financial transactions. Overall, technology serves to make these transactions faster and more efficient. As a result, digital payments are now more prevalent than ever.

Unfortunately, this brings with it an increased risk of fraud. In addition, cybercriminals are also getting more sophisticated and advanced in their attacks. As a result, banking and financial institutions need to be on guard to protect themselves and their customers against these attacks and financial losses. Here, machine learning is an invaluable tool they can use to identify and prevent fraudulent transactions.

But how does machine learning work in fraud detection, and what are its benefits? In this post, we’ll look at this question in more detail.

Why Is Machine Learning Suited To Fraud Detection?

Before looking at why machine learning is so well suited to fraud detection, it’s necessary to look at traditional fraud detection systems. These systems relied on rules alone to identify and block fraudulent transactions. Although these systems are somewhat effective and rules still play an essential part in modern fraud detection systems, they had several drawbacks.

For one, rules-based systems produce a significant number of false positives. As a result, it would often block legitimate transactions as fraudulent. This, in turn, means that businesses can possibly lose many genuine customers and transactions. And it’s simple, lost transactions mean lost revenue.

Another drawback with rules-based systems is that they have a fixed outcome. This means that these systems don’t offer the ability to judge outcomes. As a result, this contributes to false positives. It also means that rules become invalid as prices and business requirements change.

For example, if the system is set up to flag transactions above $1,000 as fraudulent, an increase in prices could lead to an increasing number of transactions being flagged.

Finally, these systems are also inefficient and challenging to scale. This means that the rules have to be adjusted and constantly expanded to accommodate the ever-increasing sophistication criminals use to perpetrate fraud. As a result, the system is slow, inefficient, and places a heavier burden on the fraud analyst team.

Fortunately, machine learning offers solutions to all these problems. Here, it improves fraud detection in the following ways:

  • Faster. It’s simple, for the most effective fraud detection system, businesses need results almost in real-time. Machine learning gives them this ability because it analyses vast amounts of data to identify possible fraudulent transactions. It can also analyze user behavior in real-time and identify any anomalies in their behavior, and, in doing so, minimizes risk and improves
  • More accurate. Because machine learning algorithms can analyze and make predictions based on massive amounts of data, it’s able to predict fraudulent transactions much faster and far more accurately compared to rules-based and manual fraud detection systems. This also reduces the number of false positives in the process.
  • Scalable. Machine learning models work by being trained on large datasets. Based on this data and being trained on which transactions are fraudulent and which are genuine, it can then identify suspicious transactions. These models also improve as more data is fed into the system. As a result, these systems are far more scalable compared to traditional fraud detection systems.
  • Cost-effective. Because it’s so efficient in analyzing large amounts of data and identifying suspicious transactions, it’s more cost-efficient compared to other systems. It, for example, allows a fraud detection team to shift from manually monitoring transactions to monitoring and optimizing the machine learning algorithm.

How Does A Machine Learning System Work?

Typically, a machine-learning algorithm works through a process of:

  • Inputting data.
  • Feature extraction.
  • Model creation.

When it comes to fraud detection, the system will use input data consisting of previous transactions. The transactions in this dataset should be labeled as genuine transactions or fraudulent transactions.

For feature extraction, it’s necessary to extract the features like customer behavior and fraudulent behaviors that would indicate a possible fraudulent transaction. This can include anything from a customer’s email address and age of their account to the number of orders, customer locations, and payment methods.

Based on the data and the extracted features, the model can then be trained to make predictions in respect of fraudulent transactions. Here, the data is split into a training set and a testing set, and the predictions are measured against actual transactions to determine the accuracy of the predictions. Based on this, it’s possible to optimize the model to make the predictions more accurate.

Once the training is complete, businesses end up with a model that’s capable of detecting possible fraudulent transactions in a matter of milliseconds. When the model is implemented, it’s also essential to constantly test and monitor the model to make sure it performs as it should.

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Fraud Scenarios And Their Detection

With the above in mind, we’ll now look at how machine learning is used for fraud detection in various industries.

Insurance Claims Analysis & Fraud Detection

Although insurance companies put a lot of time and effort into assessing claims, the industry is still plagued by scams and fake claims. Machine learning can, for instance, be used to analyze structured and unstructured data. As such, it can analyze files written by insurance agents, police officers, and clients and detect any inconsistencies in the evidence.

These inconsistencies are difficult to pick up for rules-based systems, and analysts can easily miss clues in the files. Because of this, machine learning models are more efficient and accurate at identifying possible fraudulent claims.

These algorithms are also able to identify cases of duplicate claims and overstated repair costs. Here, the algorithms can identify correlations between claim records, repair services, clients, and the behavior of insurance agents.

Anti-Fraud Solutions For Medical Claims And Healthcare

Because healthcare and medical insurance require many approvals, verifications, and paperwork, it’s often a victim of fraud. Here, the most common forms of fraud are fake claims, duplicated claims, inflation of claims, and fake diagnosis. Here, machine learning can help health insurance companies prevent these fraudulent transactions.

For one, it can help insurance companies to find suspicious links between doctors and patients by analyzing the sequence of receipts to prevent providers overstating the total limitation for some drugs. It can also help insurance companies to perform regular bill reconciliations of bills which serves to guard against fake totals.

Image recognition algorithms can also be used at the identify verification phase by using face and fingerprint recognition.

Fraud Prevention Solutions In Ecommerce

It’s no secret that eCommerce has the ability to be a breeding ground for fraud simply because of the sheer number of transactions daily. Here, typical scams are identity theft and merchant scams.

Identity theft is where a criminal breaches a customer’s user account, changes their personal details and assumes this identity to buy goods from a retailer. Machine learning models analyze user behavior compared to historical user behavior to find inconsistencies and identify possible suspicious behavior.

Merchant scams happen when fraudulent companies and merchants operate through online marketplaces. Here, some can even use fake reviews to drive customers to the store. Machine learning models can conduct sentimental and behavioral analysis to detect suspicious activities and prevent this.

Fraud Detection In Banking And Credit Card Payments

Because payments are the most digitalized part of the financial industry, they are especially vulnerable to fraudulent activities. Also, in the course of improving the customer experience, many banks have reduced the number of verification stages which, in turn, makes a rule-based approach to fraud detection very inefficient.

By analyzing customer behavior, machine learning models can detect and prevent fraudulent transactions. For example, if a customer spends on average $50 a day, the system will flag a transaction as suspicious when a customer suddenly spends a few hundred dollars in one transaction.

Another place where machine learning can be helpful in eCommerce is duplicate transactions. This happens when, for example, companies try to charge twice for the same product. It could, however, also occur when a customer accidentally presses the submit button twice. So, an efficient fraud detection system should be able to detect between suspicious activities and accidental double charges.

Preventing Loan Application Fraud

The lending industry is especially at risk of fraud and scams. In particular, identity theft poses a significant risk because, nowadays, criminals can get access to customer IDs, photos, addresses, and mobile phone numbers far easier than before. This is simply because a lot of this data can be found on social networks or the Internet.

One of the first ways in which criminals make use of incorrect personal identification details is to provide false information to obtain credit. When they default on the credit, the inaccurate information makes the debt challenging to collect.

Machine learning can solve this problem by providing real-time credit scoring and fraud probability. Based on these calculations and analysis, credit applications are classified into groups, each with a relative credit risk score.

Historical customer behavior can also be used by banks to pick up suspicious behavior where a customer suddenly applies for credit under circumstances where the customer hasn’t applied for credit for some time. This sudden behavior change will then require approval by the customer to proceed.

Machine Learning For Anti-Money Laundering

Banks, investment firms, and other financial services businesses are obligated in terms of law and regulations to have anti-money money laundering systems in place to detect and prevent suspicious activities.

By using a machine-learning algorithm that’s trained on a dataset of historical transactions conducted by criminals, the model can predict suspicious activities. This, in turn, enables banks and financial institutions to prevent these transactions.

Common And Advanced Fraud Detection Systems

Now that we’ve seen some fraud scenarios and how machine learning is used to detect and prevent fraud, let’s look at some of the ways these systems are created.

One of the most common approaches in machine learning is anomaly detection. It’s based on classifying every piece of data in a dataset into two groups. The one is normal data, and the other is outliers. Typically, it’s these outliers that are then considered potentially fraudulent transactions.

This approach is often the simplest to implement because it provides a simple classification of the transaction as genuine or fraudulent. As a result, if a transaction appears to be fraudulent, the user can be required to provide additional verification steps.

The drawback with this approach is that it doesn’t allow financial institutions to expose fraud. To solve this problem, there are several advanced fraud detection systems that use machine learning. Here, the most common ones are unsupervised and supervised machine learning. These two systems can be used independently or combined to build sophisticated anomaly detection algorithms.

Supervised learning is based on the principle that the algorithm is trained using labeled data. In other words, the target variables in the data set are already marked and identified. For example, variables can include things like the user’s location, size of the transaction, the user’s total sales, and more. Based on these variables, the model can then predict suspicious transactions.

In contrast, unsupervised models rely on unlabeled data and are classified into different clusters to find correlations between the data and, in doing so, detect suspicious transactions. This is done by grouping data based on the presence of certain qualities.

Supervised Fraud Detection Methods

Considering the above, let’s look at some commonly used types of machine learning algorithms used in fraud detection systems. Keep in mind, though, that these are all supervised learning methods.

These include:

  • Random forests. A random forest algorithm builds decision trees to classify data, and it does this by selecting a variable that enables the best splitting of data into groupings.
  • Support vector machine. This model uses a non-probabilistic binary linear classification to group records in a data set. This simply means that the algorithm defines the data into two specific categories with no overlap between the two.
  • K-nearest neighbors. This algorithm classifies data on its similarity based on the distance of its nearest neighbors in multidimensional space.
  • Neural networks and deep neural networks. These models determine non-linear relationships between data points. The structure of these algorithms is based on principles that resemble the human brain. The difference between the two is that deep neural networks provide much more layers and functions than a typical neural network. This allows it to provide more accurate results.

Conclusion

Machine learning has a vital role to play in fraud detection systems, both now and in the future. They can effectively identify and predict possible suspicious transactions, which can allow banks and other financial institutions to prevent financial loss for them and their customers.

In addition, it offers the following benefits:

  • It’s faster.
  • It’s more accurate.
  • It’s scalable.
  • It’s cost-effective.

As a result, financial institutions must implement the necessary machine learning systems to improve the overall fraud prevention strategy.

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