Protecting Financial Data Privacy: Exploring Synthetic …

In the modern digital age, data is a commodity often bought, sold, and traded like any other asset. However, when it comes to financial datasets, the information contained within is often sensitive and identifiable, making it subject to strict privacy laws. Due to these regulations, the usage and distribution of financial data for research purposes outside of financial institutions are heavily restricted.

One potential solution to the challenges of strict privacy laws on financial datasets is creating artificial data. This approach involves generating fake data that mimics the characteristics of real data, protecting the confidentiality of customers’ personal information. Using artificial data allows researchers to conduct analyses and make predictions without compromising customers’ privacy. 

A recent study from the UK highlights the potential of using synthetic data to overcome privacy constraints in finance. The study examines the challenges and requirements for using data generative techniques and synthetic data.

The authors of the study identified three key requirements for generative frameworks to create synthetic financial data:

  1. The ability to generate multiple types of financial data, including categorical, binary, complex, and numeric data.
  2. The generative process should have the ability to produce arbitrary numbers of data points.
  3. The confidentiality of financial datasets should be accurately tuned against how valuable and close to real the data is.

The authors emphasize that synthetic financial data generation protects sensitive customer information and can be utilized without compromising customer privacy. They also note that generative techniques only learn characteristics of real datasets, making it impossible for fraudsters to abuse the original datasets.

In addition, the researchers provide several reasons for the need to generate synthetic data in finance. Firstly, due to regulatory restrictions, real-world datasets are often unavailable for testing and research, making synthetic data streams helpful as counterfactual data. Secondly, privacy laws may prevent companies from sharing customer data, but synthetic data can be used to fulfill the needs of financial institutions for research and development. Thirdly, conventional deep learning algorithms often fail due to the issue of imbalanced class problems, which can be solved through artificial data and data imputation approaches. Additionally, synthetic data can be used to train models through deep machine learning techniques and share data among financial institutions.

According to the article, there are two technical solutions to generate synthetic financial data: tabular data generation and artificial time series financial data. Tabular data can be generated using various methods, including conditional GANs, VAEs, and PATE-GAN, while CT-GAN is suitable for encoding continuous and discontinuous variables. However, these methods only partially address privacy concerns. Regarding artificial time series financial data, scholars have proposed Quant-GAN and CGAN for time series forecasting and modeling. These models are useful for log returns of financial instruments and related time series models but do not offer privacy guarantees.

The techniques for synthetic data generation cited in the paper include supervised and unsupervised machine learning methods and hybrid techniques. These techniques can be used for credit card fraud detection and involve gathering information about the dataset, training and testing data subsets, and evaluating performance using various metrics such as confusion matrix, FPR, recall, accuracy, F1-Score, and precision rate. One study found that the random forest algorithm had the highest credit card fraud detection accuracy. Other techniques used in the study included artificial neural networks, tree classifiers, Naive Bayes, supporting vector machines, gradient boosting classifiers, and logistic regression approaches. 

In conclusion, using financial datasets for research outside financial institutions is heavily restricted due to privacy laws. However, generating artificial data can help overcome these challenges by protecting customers’ personal information while allowing analyses and predictions. The study highlighted in this article identifies the key requirements for generative frameworks to create synthetic financial data and emphasizes the benefits of synthetic data generation. Furthermore, the article explores the different techniques and methods used to generate and evaluate synthetic financial data, such as supervised and unsupervised machine learning approaches. The use of synthetic data in finance has the potential to revolutionize the industry and facilitate research and development while still prioritizing customer privacy.

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Mahmoud is a PhD researcher in machine learning. He also holds a
bachelor’s degree in physical science and a master’s degree in
telecommunications and networking systems. His current areas of
research concern computer vision, stock market prediction and deep
learning. He produced several scientific articles about person re-
identification and the study of the robustness and stability of deep

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