Scientists and engineers believe that artificial intelligence and machine learning are potential solutions to all current and future problems faced by mankind. Various types of fraud have become one of the most burning and dangerous problems with devastating effects. This threat constantly evolves and can get many different shapes, from scam emails to deepfake videos of global leaders.
It is evident that human capabilities are not enough to fight this problem, so people need a more resourceful companion, like AI and ML technologies. They have shown great potential in detecting behavior patterns and specific features that are characteristic of fraud. The global investments in AI in fraud management are growing every year and are expected to exceed $57 billion in 2033.
Understanding Machine Learning and AI in Fraud Detection
Artificial intelligence is a general concept of simulating human thinking processes with computer technologies. Within this concept, there are several aspects, and machine learning is one of them. ML focuses on training AI based on given data sets without manual reprogramming. Usually, machine learning is performed using one of the three approaches: supervised, unsupervised, and reinforcement.
Deep learning is a subset of ML that focuses on improving automation by creating a neural network that resembles the structure of a human brain. As opposed to traditional ML approaches, deep learning algorithms can process unstructured information and improve their accuracy using such processes as backpropagation and gradient descent.
AI trained on specific historical data can become a powerful instrument for such complex tasks as fraud detection. Given that many schemes of online fraud rely on bots, the automated detection provided by AI and ML technologies is a very fitting solution with excellent capabilities of scaling and improving its performance.
Principal Fraud Types Detected by Artificial Intelligence
Defining even the approximate number of fraud schemes and tactics across all industries is challenging since new variants appear every day. Here are the three most popular categories of fraud associated with online activities.
Account Botting: Creating Fake Accounts and Bot Networks
Fake accounts are the modern-day plague of the Internet. Billions of fake profiles are automatically created and linked together, forming massive networks on popular social media and other relevant websites. For example, every quarter, Facebook removes from 1.3 to 2.2 billion fake profiles. Thanks to proactive measures, such as using AI for fraud detection, LinkedIn was able to detect and remove 44.7 million fake profiles during registration in the second half of 2022.
Online Payment Fraud: Card-Related Crimes
Bot networks are often used to perform brute-force attacks. One of the purposes of such attacks is to test stolen credit card information and determine whether it is still valid. This activity is usually characterized by large amounts of low-value orders. Machine learning fraud detection can identify and prevent such automated purchase attempts before they harm businesses and cardholders.
Identity Theft: Account Hijacking, Automated Credential “Guessing”
As another implementation of bot networks, account theft can be performed using brute-force methods similar to those described in the context of credit card fraud. Bots can try to input credentials from stolen databases or pick passwords based on the vocabularies of the most common variants, and so on. Again, AI solutions are innately good at identifying automated routines, and they can quickly adapt to new variants of such fraud.
Usually, the types of fraud mentioned above are the initial steps of complex schemes that involve other criminal activities, such as money laundering, investment scams, insurance scams, and so on.
How to Use Machine Learning for Fraud Detection?
Implementing Machine Learning for fraud detection can be broken down into a sequence of steps. To describe this process, we will use financial fraud as an example since it is one of the most popular types of scams encountered by businesses. However, with minimal adjustments, this scheme can be applied to fraud detection in other industries. For the sake of clarity, we will mostly refer to supervised learning as an example of using ML for fraud detection.
1. Providing Source Data
Teaching an AI involves feeding input information into a machine learning system to create a basic functional model that will be continuously improved. For supervised learning, source data must be pre-processed and structured. Specifically, it must be labeled as good when it refers to legitimate operations, and bad when it is related to known fraud. Unsupervised learning can discover patterns in unstructured information.
2. Extracting Relevant Parameters
In the context of financial fraud detection, the most common parameters of source data are:
- number and frequency of successive transactions;
- value of each transaction;
- payment system and other information related to credit cards;
- type of purchased product or service.
For example, when a popular fraud scheme involves ordering a specific product and then demanding a refund or chargeback due to alleged quality issues or failed deliveries, the known instances of such fraud will be broken down into individual parameters and analyzed as input data.
Another useful information for fraud detection with machine learning is user-related data, which includes:
- IP address ranges;
- detected use of VPN or other proxy services;
- hardware IDs, such as MAC addresses;
- various software-related data, such as name and version of OS, web browser, etc.
More input data requires more resources for processing but leads to a more accurate model capable of detecting more types of fraud.
3. Performing Rule Management
Source data analysis results in specific rules that define the fraud determination method. Basic rules involve one parameter: if it matches, an action will be marked as a potential fraud. For example, if a specific IP range is related to previous fraud, a rule will mark it as potentially unwanted. As a result, any IP address that belongs to the said range can be blocked automatically or flagged as a potential source of fraudulent activity.
However, most fraud detection algorithms use complex rules based on several parameters, which improves accuracy and reduces the probability of false positive triggering. Every parameter can be associated with a potential accuracy value and certain thresholds for triggering a relevant rule. In supervised learning, they can be reviewed and tuned by engineers after a predetermined number of cycles.
4. Training, Evaluating, and Tuning the Model
A fraud detection model can run multiple times on the relevant historical data with the chosen set of rules to make decisions. More runs or cycles improve the accuracy of the model and make it find more patterns. Testing the “real-world” performance of the model on previously unknown data is the vital stage of creating an AI-powered fraud detection solution. It allows engineers to review and finalize the rules and customize the model according to the specific features of the particular business and industry. Then, specialists must seamlessly integrate the ML model as a custom solution into the client’s digital infrastructure.
Benefits of Fraud Detection Using Machine Learning
Computers are excellent for processing and analyzing large amounts of information, so they are incredibly efficient in fraud detection. Here are a few advantages of using AI and ML for this purpose:
- Improved speed and efficiency. AI uses hardware capable of performing billions of instructions per second, which is far beyond human capabilities. With a sufficient amount of source data, artificial intelligence quickly learns to detect patterns characteristic of most frauds. This enables real-time detection and immediate response to fraud threats.
- Less human involvement. For such a responsible task as fraud detection, the decisions made by AI have to be double-checked and finalized by experts. Still, computers provide significant time savings by going through all the hard work and presenting results for manual verification. This benefit allows managers to relieve staff from monotonous and tedious activities and direct specialists to more creative and skill-demanding tasks.
- Better accuracy. Thanks to the ability to process large datasets, AI and ML solutions can reach conclusions with higher degrees of certainty. Further learning under the supervision of human experts additionally enhances fraud detection by increasing the probability rate and reducing false positives.
- Higher learning potential. Again, AI’s excellence in processing information results in more advantages compared to human analysts. Data scientists just have to keep feeding new data to machine learning systems to enhance machine learning fraud detection models.
- Cost savings. Since AI and ML greatly outperform the capabilities of human staff, these technologies present a much more cost-effective solution to the fraud problem. Additionally, they have excellent scalability to conform to the changing demands of the business. Compared to staff management, increasing the scale of an AI solution is cheaper and involves less hassle than finding and hiring a new specialist for fraud detection.
- Round-the-clock availability. Unlike human staff, computers can work 24/7 and do not require holidays or weekends. Sure, the AI infrastructure is relatively complex and needs periodic maintenance, as well as hardware upgrades and software updates. However, these operations can be specifically planned and performed in stages to minimize downtime and ensure uninterrupted fraud detection with machine learning.
Though AI-based solutions require financial investments and the skills of many specialists to build and train learning models, all those efforts and resources eventually pay off. As AI learns and evolves, its beneficial effects become more powerful, and it gains more advantages over manual fraud detection. For example, artificial intelligence becomes more accurate and capable of functioning with less supervision.
Drawbacks of Machine Learning in Fraud Prevention
Though machine learning has proven its high efficiency in such demanding tasks as fraud detection, it also has several drawbacks. In some cases, they can have such a negative impact on the accuracy of conclusions made by Artificial Intelligence that manual analysis and human opinion would be preferable.
- The complexity of implementation. Teaching an AI-based solution, just like teaching students, requires time, skills, and knowledge of experts. The unsupervised approach to ML that relies on deep learning algorithms has gained popularity over the years due to its autonomous nature and drastically improved accuracy. However, many experts still root for a supervised approach that requires constant human involvement.
- Chance of malfunctioning and false triggering. When using AI-based analysis, there is always a possibility of a false positive result. In the context of fraud detection, this means that a legitimate activity is marked as illegal, which may result in restrictive actions against an innocent client. If an ML system is not aware that its decision is wrong, it can set off a chain reaction of further false detections that reduce accuracy and make AI choices increasingly unreliable.
- Some models lack transparency and control. Depending on the type, ML models may be more or less automated and accurate. For example, black box models generally offer better automation and accuracy but do not offer many options for specialists to interpret their results and tweak their rules. On the contrary, white box models are more transparent and linear, so they provide clearly interpreted relations between input variables, fraud detection rules, and output results.
ML and AI Use Cases in Fraud Detection
Banking and Finance
JPMorgan Chase. Being among the global industry leaders, JPMC has been actively using artificial intelligence for years. The company has incorporated ML into its processes aimed at anomaly detection. Their AI-based security system that comprises deep learning algorithms and big data allows detecting malicious payloads like phishing emails or Trojan activities from both external and internal sources aimed at employees.
Elevance Health. This health insurance company, formerly known as Anthem Inc., uses Google Cloud’s data analytics capabilities and AI-driven offerings to detect possible fraudulent claims. They use statistical models and algorithms for generating petabytes of synthetic data, including datasets of healthcare claims, medical histories, etc.
Shopify uses and promotes a NoFraud system – an AI-driven decision engine that can analyze multiple data points on each transaction to eliminate fraudulent activity related to every step of order processing. The system examines numerous order details like IP address, device history, email longevity, social media, and others to identify the individual making a transaction and the possibility of fraudulence.
Frauds make their schemes smarter and more sophisticated every few months. So, to always be at least one step ahead of them, you need to engage artificial intelligence and machine learning in your fraud detection and prevention systems. The advantages these technologies bring to the fraud detection field are enormous – enhanced accuracy, total availability, unlimited learning potential, speed and efficiency, and a lot more. Despite a few drawbacks, using machine learning and artificial intelligence for fraud detection helps to save confidential information, money, time, and reputation for companies and their clients.
Intellectsoft is a custom software development company that has been delivering complex digital solutions for over 15 years. We offer a wide range of services and solutions, including enterprise artificial intelligence software that includes but is not limited to:
– Neural networks and deep learning;
– Custom machine learning models;
– Cloud-based AI-models;
– AI chatbots and applications;
– Face and voice recognition;
– Data analysis management;
– Raw data management;
– Data generation and augmentation, and more.
Over the years, we have delivered more than 600 bespoke solutions for businesses of all sizes – from early-stage startups to Fortune 500 enterprises from many countries across the globe. Last but not least, we have an engineering workforce in 21 countries, and almost 30% of our employees are females.
So, if you’re looking for a tech team that can build a solid fraud detection solution for your business using machine learning and artificial intelligence, don’t hesitate to contact us. As soon as our team gets your requirements, we will analyze your information and offer you the most effective solution or recommendations, depending on what you need. Just get in touch with us, and let’s make your product 100% fraudproof.