Banking: Machine Learning options in fraud detection

Published by FirstAlign

The banking sector is a vast and vital industry, and we cannot imagine a world without it. This extensive industry is always at the forefront of embracing technology. In turn, technology aids the banks to deal with large volumes of online transactions daily. With it, there is also an evolving threat from cybercriminals.

Banks are prone to attack in the form of money laundering, identity theft, and mobile fraud, to name a few. The most common type of attack is card fraud. Experts predict online credit card fraud will increase to US$ 32 billion in 2020.

To detect fraud, banks are using Machine Learning (ML) algorithms. In this article, we will look at how is Machine Learning used in fraud detection.

Why Machine Learning?

Machine Learning is the science of creating algorithms that can analyze cases and learn from past experiences. It uses complex sets of algorithms that iterate over large data sets and analyze the data patterns. It aids the machine to respond to different situations for which they have not been explicitly programmed.

The aim of implementing Machine Learning is to reduce significant human effort. ML teaches machines to identify and assess the importance of patterns.

Three factors that make Machine Learning apt for fraud detection are Speed, Scale, and Efficiency.

  1. Speed: Unlike a rule-based system where you need human intervention (to define what orders are acceptable), ML algorithms learn from fraud patterns and are much quicker at making decisions.
  2. Scale: ML methods become better as the data set grows. The more samples of fraudulent operations they are trained on, the more evolved the ML system becomes.
  3. Efficiency: Machines are best suited to perform repetitive tasks. ML is more efficient than humans in detecting the subtle fraudulent patterns that humans can miss easily.

How is Machine Learning used in fraud detection?

Two types of Machine Learning algorithms are used in fraud detection – supervised and unsupervised learning. Generally, the most capable and accurate detection model is a combination of both.

In a supervised learning model, algorithms learn on a labeled dataset, i.e., we know the raw input data as well as a result. An unsupervised learning model provides unlabeled data that the algorithm tries to make sense of by extracting its patterns. The prediction problem can be further divided into two tasks Classification and Regression.

Classification prediction involves predicting a label. For Example, we can classify an email of text into two classes: “spam” or “not spam.” Regression is about predicting a quantity. For Example, a house may be expected to sell in a price range of $100,000 to $200,000.

The following are the supervised learning algorithms that can be used for fraud detection;

  1. Logistic Regression: Logistic Regression measures the strength of cause and effect relationships between variables in data sets. We can use Logistic Regression to create an algorithm that predicts whether a transaction is ‘good’ or not.
  2. Decision Trees: Decision Trees create rules that train on examples of fraud to detect anomalies.
  3. Random Forests: Random Forest technique consists of many individual trees that operate as an ensemble.
  4. Neural Networks: This is a powerful technique inspired by the human brain. They are adaptive, learn patterns, and can detect fraud in real-time.

Unsupervised techniques use clustering algorithms. It groups similar data points. The algorithms used in the unsupervised approach are;

  1. K-means clustering: This algorithm divides the dataset into clusters. The algorithm assigns data points to one of the predefined number of classes (k), based on the dataset’s features. It clusters data points based on feature similarity.
  2. Local Outlier Factor: Local Outlier Factor calculates the local density of data points. It identifies regions of similar density. By using the locality concept, we can identify points with much lower density than other neighbors. These points are outliers (fraudulent transactions)
  3. One-Class SVM: One-Class SVM is used for novelty detection, i.e., to detect rare events. Since few samples are available for the occasional events, the usual way of training a classifier will not work.

Limitations of using ML in fraud detection

Although Machine Learning is efficient and quicker than traditional fraud detection methods, it is not without its limitations. Machine Learning models are best on the data they are trained. Though banks have large volumes of data, the number of fraudulent transactions is comparatively less. There is also the scenario where faulty rule logic may result in denial of valid transactions. It may lead to loss of revenue or customer dissatisfaction.

When the data set is small, ML cannot identify connections in data such as shared cards between two accounts. To prevent this, we have to enhance our models with Graph networks. Graph technique can find multiple bogus accounts through scoring. There is no telling which ML model will best suit your process without doing research and experimentation.

Final Thoughts

Machine learning has a decisive role in supporting a safe and secure payment environment. The current process of Machine Learning in fraud detection is a two-stage process, comprising automation and human intervention. It indicates there is still scope for innovation and improvement.

Machine Learning solutions are efficient and process many transactions in real-time, making fraud detection faster. A number of ML models can be applied that best suits our needs. The benefits outweigh the current limitations and have a broad scope for cost savings, where keenly and adequately managed.

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