Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in the personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes.

This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning on the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to find out more about ML, about the types and magnitude of fraud evidenced in the modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.

In a nutshell, one may treat ML as one of the applications of AI as it is based on pattern recognition and computer learning without their deliberate programming for doing that. Techniques that make ML happen are Bayesian methods, neural networks, inductive logic programming. Other approaches currently used to induce ML are decision trees, explanation-based, natural language processing, and reinforcement learning.

The potential of ML is vast today; some vivid examples of how they work in everyday human lives include the use of Alexa, traveling with Uber, and dealing with digital education. A vivid example of how far the ML process can go is the AlphaGo Zero machine that learned to play checkers by playing with itself and soon surpassed the top human talent in playing this game. These examples show that machines can learn infinitely, with the ML applications limited only by human imagination.

With so many human activities transferring online, crime and fraud have also adapted to the digitization trend. Cyber-attacks are reported to be the fastest-growing crime in the USA, with increases in magnitude, sophistication, and cost to businesses and individuals. Some notable examples of large-scale fraud include the 2017 Yahoo hack of 3 billion accounts and the hack of Equifax compromising the data of 145.5 million customers. The volume of cybercrime-related damage is projected to rise to $6 trillion by 2021 globally, which is twice more than in 2015 ($3 trillion).

Cyberattacks are varied in manifestations, commonly including attacks with ransomware and malware, identity theft, violation of privacy, weapons and drug sales online, and data theft, leakage, and intellectual property hacks. Most cybercrime is conducted on social media, giving $3.25+ billion in revenues to criminals every year. In 2019 alone, 85% of business organizations reported the detection of phishing or social engineering threats, while another 75% of organizations are afraid of insider threats as a significant fraud risk.

The most alarming about fraud is that it may take too long to detect it. In the financial institutions conducting most of their operations offline, fraud detection may take as much as 40+ days, leaving zero chances for criminals' identification and funds' recovery.

The logic underlying the use of AI for fraud detection is simple; while machines are known to be capable learners, they may be taught based on the historical fraud protocols to identify suspicious user behavior suggesting fraud and anticipate fraud efforts before the actual attack takes place. In other words, ML helps data scientists determine potentially fraudulent transactions, thus helping to minimize the number of successful attacks. The benefit of ML for this purpose is its ability to discover fraud patterns across huge masses of streaming transactions in an automated way, without human guidance. Moreover, with more data becoming available, machines learn to make subtle distinctions and adopt more sensitive and sophisticated fraud detection algorithms.

Before the emergence of ML fraud detection tools, the rule-based approach was a dominant fraud identification and prevention method. It presupposes the use of 300+ explicit scenarios for detecting evident fraud signals and issue alerts to block such transactions. Despite the broad spectrum of fraud detection scenarios, it results in user dissatisfaction with numerous verification steps, still being unable to process volumes masses of data in real time.

In contrast to the rule-based method that works quite rigidly in terms of fraud detection and analysis, ML techniques introduce quicker, automated processing of a much larger number of fraud scenarios. Besides advanced computational speed and real-time processing of big data, ML systems enable better user experiences with smaller verification steps and learn to identify even hidden or, implicit fraud signals. Thus, ML systems are much better equipped to work with ambiguous data that rule-based algorithms will not detect.

Understanding of how ML enables a fraud detection dataset is impossible without learning the fraud-specific data science techniques. Otherwise, ML may go in the wrong direction, failing the initial task of fraud detection and letting fraudulent activities pass unnoticed. Here are some popular approaches to designing ML-enhanced fraud detection tools.

• Cohesive integration of supervised and unsupervised AI models into an ML fraud detection algorithm. Supervised models learn efficiently if they have a mass of tagged transactions based on which they tag new transactions as malicious or non-malicious. In cases with non-existent or scarce tagged data, unsupervised models are useful because of their profound self-learning ability.
  
  
• Application of behavioral profiling and analytics in the ML systems for fraud detection. Behavioral analytics is a helpful approach to fraud prevention and detection. ML systems store and analyze data about transaction participants' behaviors, including merchants, individuals, accounts, and devices. The behavioral profiles of each are updated upon each successive transaction, enriching the database and making the prediction of fraud more precise.
  
  
• Use of large datasets for AI model development. Evidence proves that the volume of data available for machines to initiate sufficient ML is the most crucial success factor. Thus, by feeding more data into the system, you can increase its predictive accuracy. The process works similarly to the physical training of people; the more they are exposed to identical exercises, the more endurance and precision they exhibit each new time. The same goes for machines able to refine their fraud detection alerts and become more sensitive to detect even non-evident, new kinds of fraud.

• Integration of self-learning AI with the help of an adaptive analytics setup. The bonus of adaptive analytics is that of refining the algorithms upon every successful fraud detection case. In such a way, the AI system becomes more complex and dynamic, evolving together with the changing fraud patterns and approaches. As a result of self-learning and adaptation, ML enables machines to make more precise decisions in marginal cases.

Fraud identification has become imperative not only in traditional commercial spheres such as e-commerce or banking. It has gone far beyond economics, reaching such aspects of human lives as medical care, insurance, and personal data. In the times when personal data becomes the most valuable asset, fraud detection with sophisticated (and continually improving) algorithms is imperative to meet the challenge of the coming years – the rise of cybercrime. Here are some industry experiences regarding fraud detection and failures within the past years in the healthcare, banking, and e-commerce sectors – the ones most exposed to cybercriminal activities.

Manipulations with healthcare insurance are the most common type of fraud in this sector, mainly because of the bureaucracy and complexity of the healthcare system. Criminals can steal money by:

1. Making false claims
   
2. Sending duplicate claims
   
3. Exaggerating the cost of medical coverage
   
4. Including unnecessary tests or a wrong diagnosis into the claim
   
   

ML systems can help detect fraud in the medical/healthcare sector through:

• Identifying the upcoding of procedures by automatic identification of unexpected digits in the datasets
  
• Automatic reconciliation of bills for fake bill total prevention
  
• Improving personal identity verification procedures with the help of smart image recognition techniques
  

While customers strive to greater mobility and accessibility of payments, such simplified verification inevitably causes greater vulnerability to cyber-fraud. ML systems offer several intelligent solutions for the banking industry, such as:

• Automated data credibility assessment by comparing historical transaction data with each new transaction's elements.
  
• Elimination of duplicate transactions.
  
• Blockage of account theft attempts.
  
• Alerts about potentially fraudulent, suspicious activities.
  

Ecommerce fraud prevention has a long history of development, given that this sector is directly linked to financial transactions. Here, the two most popular fraudulent schemes include identity theft and scams. In both cases, the customer is a victim of fraud as their personal data are compromised, and money is stolen, either via a fraudulent merchant scheme or directly from a bank account.

Ecommerce fraud prevention techniques are sensitive to the type of offense. For instance, identity theft is prevented via ML with the help of behavior analytics. ML systems develop smart algorithms for dubious activity identification and compare historical and current data during any transaction's initiation, completing it only after the positive comparative outcome is obtained. Merchant scams can also be identified with the help of behavior analytics that locates suspicious activities and alerts users about the merchant's doubtful reputation.

As you might see, ML is transforming how fraud is detected and blocked. Greater efficiency and accuracy of fraud detection is guaranteed with the higher computational ability of AI systems and their practical work with ambiguous data. Smart analytical solutions adapt to user behavior, enriching the system with new knowledge once every other case of fraud is detected and analyzed. Thus, ML may be regarded as a robust alternative to rule-based fraud detection, enabling smarter and quicker analytics for greater security of industries vulnerable to cyberattacks and dealing with money or private user data.