How is Machine Learning Used in Fraud Detection?
The digital advertising world witnessed a global hit of $27.2 Billion dollars (£20.7 Billion pounds) due to ad and click fraud in 2018. By 2022, it is expected to reach $44US billion dollars. This scenario is expected to worsen in the coming years and one of the widely recognized solutions to combat these ad frauds is using Artificial Intelligence (AI) and Machine Learning (ML).
In this article/
- What is AI
- What is machine learning?
- Fraud detection using rule-based approaches
- Fraud detection using ML-based approaches
- How does it work?
- Applications of ML and AI
- How we use AI to detect fraud
What is AI?
Artificial Intelligence is defined as a collection or a combination of machine processes and patterns that simulate human intelligence. This may include behavioral patterns such as planning, learning, logical reasoning, problem-solving, self-correction, predicting and appropriate use of available data and information. Based on three cognitive skills – learning, reasoning, and self-correction, AI programs and algorithms work in cohesion using data that has been turned into actionable information to perform tasks, resolve problems and make forecasts.
It is also important to note that AI is an umbrella term and there is no single method of artificial intelligence, rather an infinite number of possibilities. However, as machine learning is the most common form of AI, when AI is usually discussed, the actual technology being referred to is usually machine learning, which is a form of AI. However, not all AI uses machine learning.
What is Machine Learning?
A subset of AI, machine learning (ML) is defined as the ability of computer systems to study and learn from patterns, actions, and accessible data to improve knowledge and perform well-informed decisions without human intervention.
There are three main types of ML algorithms:
- Supervised – This type of ML is done by using labeled datasets from the past and applying those patterns to new data sets.
- Unsupervised – This type of ML involves the training of datasets that haven’t been labeled or defined, instead the system itself looks for variations and similarities in order to make calculations.
- Reinforcement Learning – This can be described as a hybrid of Supervised and Unsupervised machine learning since this involves using unlabelled datasets that are given automated feedback and revisits once the action has been performed. There might be several trial and error instances using this learning method, as the whole point is to train computer systems using known and unknown past patterns to achieve desired results.
Fraud Detection Using Rule-Based Approaches
Traditionally, experts and analysts used to rely heavily on the rule-based fraud detection approach. This methodology required anti-fraud analysts to manually write algorithms based on a certain set of rules. Due to the ever-increasing data of users globally, this method is often deemed to be time-consuming and cost-ineffective. In addition to this, the inability to process real-time data makes it very difficult to be used in digital marketing and advertising.
Fraud Detection Using ML-Based Approaches
Machine learning in fraud detection is based on algorithms that have the ability to process large quantities of datasets after self-learning through experience over the passage of time. Since the past few years, this approach has seen rapid growth and advancements due to the fact that it requires less human involvement and hence it is more cost-effective. These algorithms study and analyze the correlations between millions of patterns and behavior over a course of time, and based on that, decisions are made that assist anti-fraud companies and businesses worldwide.
Considering the number of new digital touchpoints and channels that fraudsters have access to these days, the rule-based approach is not believed to be effective anymore since it has too many limitations and restrictions, specifically with the pre-defined rules for algorithms. Machine Learning, using its automated predictive analysis based on actionable data, assists in fraud detection in several business sectors including banking, finance, online transactions, digital marketing, etc.
How Does It Work?
The core steps of machine learning algorithms for fraud detection are:
- Dataset Provision – The more data is fed to a machine for training purposes, the better the results and accuracy.
- Characteristics Analysis – This is the part where the system studies all the patterns associated with, let’s say, User X. These are all the elements that the user uses to send or receive data. For example his email address, contact number, IP address, etc.
- Train Your Dataset – Once the feature analysis stage is done, now you need to train the algorithm by providing ‘true’ and ‘false’ scenarios so that the system knows when an online data transaction is legitimate and when it’s not.
- Develop Your Model – Now your system is ready to perform in real-time and detect fraudulent activities using the trained dataset.
Applications of AI and ML
Outside of ad fraud, and mobile ad fraud, there are numerous sectors and industries worldwide have moved to AI and ML-based models in order to achieve better, secured and cost-effective business solutions. Some of the major applications are:
- Healthcare & Medicine – IBM Watson is one prime example of the usage of AI in the medical field. This is primarily based on NLP (natural language processing) and has the ability to handle medical queries, extract patient databases and develop relevant hypotheses.
- AI in Security – Security Information and Event Management (SIEM) software is what businesses are opting for these days. Due to its ability to lower actual threats by analyzing data, this kind of software is in high demand.
- Identity Theft – The past couple of years saw a sharp rise in fraudsters and scammers breaching user accounts (bank, social media, email, etc.) for either monetary or non-monetary purposes. To fight this off, smart and effective algorithms were developed based on AI & ML that gained worldwide recognition.
- Digital Advertising & Marketing – With billions of dollars being lost each year to ad frauds and other digital advertising scams, ML-based algorithms are in high demand by advertisers and publisher websites and apps in order to protect their digital ad space.
How We Use Artificial Intelligence to Detect Fraud
Whilst there are numerous approaches we use to tackling fraud, both in terms of simple deterministic rules, alongside complex probabilistic rules, we also use artificial intelligence principles. We are constantly working towards adopting new technologies and work processes in order to improve the functionality of our mobile ad fraud prevention systems. We currently have developed, tested, and implemented one working mechanism, which is enhanced by artificial intelligence principles, which is:
- Alarm iteration – we use artificial intelligence principles to test, iterate, and constantly improve the efficiency and effectiveness of our alarms. This might sound like a ‘buzzword,’ however, a process of iteration is integral to catching fraudsters that are constantly evolving. Thus our alarms must undergo constant iteration, both by our data scientists and analytics department, along with the assistance from artificial intelligence principles.
Keeping in view the constant increase in global digital ad spending and the new opportunities it gives for fraudsters to penetrate through the countless layers of security, there is still a lot to be done in the field of AI and ML to strengthen algorithms, develop foolproof models, and prevent digital ad fraud eventually.
Machine Learning is, without a doubt, one of the most detailed and logical answers to all the questions advertisers and publishers have for their security vendors. With more and more datasets being trained and studied and with anomalies being removed from existing algorithms, ML, along with new iterations of AI, will constantly bring the ad ecosystem closer to obtaining a fraud-free environment.