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.
- 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.
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.