AI and Machine Learning in Fraud Detection - How Does it Work?

November 04, 2020

Recently, the business world has been witnessing tremendous hype over advanced technologies such as artificial intelligence development and machine learning. Previously, these two concepts were widely used to deal with a bulk of business data and various back-office endeavours. But with the business developments occurring worldwide, the AI and machine learning service have occupied a centre-stage. In the present business scenario, AI and machine learning services can be applied in every domain including healthcare, travel, finance, etc. in the form of AI-powered chatbots, software, or apps.

“Creating insights, robust strategies, unparalleled customer base, and maximized sales”

But, in the past few years, the business world is facing the rising risk of being prone to fraud detection. In response, the AI development and machine learning services are helping the organizations to tackle the issues pertaining to security efficiently.

Artificial intelligence is an accumulation or an umbrella term that includes the process of various behavioural patterns (learning, planning, logical reasoning, etc.) that would stimulate human intelligence.

On the other hand, machine learning service is a sub-domain of artificial intelligence that enables the computer systems to study, understand, learn, and analyze the actions and data patterns. It’s one of the finest ways to formulate strong decisions without the intervening of manual/human efforts.

Traditional Fraud Detection

Previously, the fraud detection methods were depended on rule-based approaches. It involved manpower measures wherein the employees had to write down the algorithm codes depending on a specified set of rules. Also, these fraud detection methods involve a higher cost and consume excessive time. These measures also bring in numerous challenges in the process of advertising and digital marketing services. This traditional fraud detection might cause irrelevant outcomes, increased false positives, and difficult to measure the fraudulent activities.

Let’s see how do AI and machine learning service work in the determination of fraud detection?

Data Input

The involvement of enormous data will ease the practice of fraud detection. To implement supervised machine learning, it is important to categorize the input data either as ‘good data’ or ‘bad data’. The good data implies that the customers are genuine and they do not have a fraudulent history. Whereas, the bad data implies the customers have been charged with the term ‘fraudster’ in the past.

Feature Extraction

The various features can give us detail about the behaviour of customers, including their fraudulent attitudes (if you have any). The data can be categorized into five major elements – identity (personal details of customers, such as name, age, contact, etc.), number of orders/transactions made by customers, verifying the customer location, payment methods, and customer network details. These received data can be further put under training, testing, and cross-validation domains. The prepared algorithm will be tested on a certain data set and finally, the data will be measured via the cross-validation tactics.

Algorithm Training

Well, let us tell you that an algorithm is a set of rules that should be followed while solving a problem. In the business scenario, the algorithms will be using the data of customers that can describe the various features and finally, help in predicting the fraudulent activities. Generally, the data is obtained from historical records. This historical data set can be called a training set. If there exist more fraudulent encounters in this training data set, it will help the machines to learn in more depth.

Model Creation

Once the training of the data set is completed, you have to create a model that would identify the threats instantly. You should constantly keep a track of the model’s performance and make improvements (if needed) accordingly. It is essential to keep the model updated so that it will detect the fraudulent techniques in a contemporary way. The models implemented could be any of the following:

  • Decision Tree – It is a mature ML algorithm that is widely being used for solving problems related to regression predictive modelling or the data classification. The model ignores the unnecessary features and will give a fraud prediction, based on the previous records/instances.
  • Neural Networks – A neural network can create a flexible model for individual business firms, generating a higher accuracy. Being an integral aspect of cognitive computing technology, the machine imitates the human brain and keeps a close observation of the patterns. Neural networks function rapidly, ensure in detecting the patterns of the fraud-based transactions, and further develop real-time decisions.
  • Regression – The logical regression model is a statistic-based technique that is beneficial in measuring the relation existing in the structured data set. The regression model (or algorithm) will help in detecting the presence of any risk or fraudulence within a new transaction. An algorithm is developed by creating a comparison between an authenticated transaction and a fraud one.

The models will have the capability to generate a score at the time of the transaction on a scale of 1-100. If the score is higher, it signifies a higher risk of fraudulent activity.

In the present growing business scenario, there exist various kinds of internet fraud.

  • Email Phishing
  • Bank Loan Scams or Credit Card Frauds
  • Theft of Identity, such as account takeover
  • Fake account creation
  • Forgery of ID and Documents

Fraud Detection Using Machine Learning and AI

  • Supervised Learning – It is generally based on predictive data analysis and is a commonly used way for fraud detection. All you need to do is input data and tag it as ‘good’ or ‘bad’.
  • Semi-Supervised Learning – It preserves the data associated with the vital group parameters and defines a pattern that is suspected of fraudulence.
  • Unsupervised Learning – It is one of those techniques that help to identify the weird behaviour in a transaction. Once the training is done, the algorithms will detect a specified pattern in the data set.
  • Reinforcement Learning – It will help software or a machine to verify the behavior of a data set automatically. Also, it will train the machines to detect threats or fraud in the surrounding environment.

Benefits of Using ML and AI

  • Evaluating countless transactions in real-time
  • Highly effective, regardless of how huge the data set is!
  • Performing repetitive tasks with greater efficiency
  • Instant accessing of user’s behaviour
  • Bringing accurate results on your table
  • Enhanced data analysis in a shorter time

The Final Thoughts

Be it any industrial domain (healthcare, advertisement, eCommerce, etc.), the integration of machine learning service and AI development have encouraged the detection of fraudulent activities. The concept of machine learning follows a systematized approach to detect and eliminate the multiple loopholes existing in a data set. These contemporary technologies are one of the finest ways to prevent a scam or fraud. These practices will automatically identify the strange patterns in a data set that would otherwise lead to risk or fraudulence. If you seek to develop a machine learning-based app or AI-powered software to optimize your fraud detection system, feel free to drop in your queries to [email protected]