Can machine learning detect fraud?

Can machine learning detect fraud?

The potential of using Machine Learning to uncover financial fraud 

In the financial industry, fraudulent activities have been a significant concern worldwide and have negatively impacted market efficiency. With the development of machine learning, Machine Learning generates good performance in fraudulent activity detection, especially credit card fraud. Considering the applications of machine learners, I believe ML has great potential for uncovering financial statement fraud. 

The process of identifying financial fraud really falls into the bucket of a classification problem. On the one hand, it is a binary classification problem that distinguishes short-selling targets from non-fraud samples.

On the other hand, viewable as a multiclass classification with different labeling for each fraud type. As machine learning becomes proven effective and accurate in classifying performance, we can also use machine learning models to detect financial statement fraud. 

Nowadays, many hybrid model pipelines perform well in detecting fraud activities. Researchers first calculate financial characteristics according to Freudian theories. Then, they use those features to train machine learning models to classify fraud samples, like Random Forest, XGBoost, and so on.

By combining human-designed financial fraud detection rules with machine learning models, hybrid models can get high accuracy.  

However, we cannot guarantee the manually collected financial features can fully and effectively present the financial statement. Instead, deep learning can discover complex underlying patterns in the data and help explore the information thoroughly. While some financial characteristics can become calculated based on a series of financial fraud theories, many other implied features potentially then, one overlooks.

Moreover, Deep neural networks consist of multiple layers and rich parameters, so DL models can extract sophisticated features from the original financial statement dataset. For example, we can take time series into consideration, and use Transformer to classify fraud activities. The financial statement in recent years can be organized in time sequence and served as input to the model.

With attention techniques and RNN encoders, the Transformer model can be trained to extract implicit features and apply different weights to each feature. Then we can use decoders to determine the possibility of fraudulent activity.  

Moreover, in practice, most datasets find themselves unlabeled, so supervised learning is useless. Meanwhile, for the multi-classification problem, it is difficult to label each sample with all possible fraud types. Because fraud techniques are changing day by the day. Therefore, instead of relying entirely on supervised learning, we may try to combine it with unsupervised learning models to detect anomalous samples. 

To sum up, in most cases, traditional supervised machine learning models serve as an excellent solution for detecting financial fraud, while the use of DL and unsupervised machine learning techniques may help to improve performance in complex situations. 

Written by Yiwen Song

Can machine learning detect fraud?