In the modern digital age, data is a commodity that is often bought, sold, and traded like any other asset. However, when it comes to financial data sets, the information they contain is often sensitive and identifiable, making it subject to strict privacy laws. Due to these regulations, the use and sharing of financial data for research purposes outside of financial institutions is highly restricted.
One possible solution to the challenges of strict privacy laws on financial data sets is the creation of artificial data. This approach involves generating fake data that mimics the characteristics of real data, protecting the confidentiality of customers’ personal information. The use of artificial data allows researchers to perform analysis and make predictions without compromising customer privacy.
A recent UK study highlights the potential of using synthetic data to overcome privacy restrictions in finance. The study examines the challenges and requirements for the use of data generation techniques and synthetic data.
The study authors identified three key requirements for generative frameworks to create synthetic financial data:
- The ability to generate multiple types of financial data, including categorical, binary, complex, and numeric data.
- The generative process must have the ability to produce arbitrary numbers of data points.
- The confidentiality of financial data sets must be finely tuned against how valuable and close to reality the data is.
The authors emphasize that the generation of synthetic financial data protects confidential customer information and can be used without compromising customer privacy. They also point out that generative techniques only learn features from real data sets, making it impossible for fraudsters to abuse the original data sets.
In addition, researchers provide several reasons for the need to generate synthetic data in finance. First, due to regulatory constraints, real-world data sets are often not available for testing and research, making synthetic data streams useful as counterfactual data. Second, privacy laws may prevent companies from sharing customer data, but synthetic data can be used to meet the research and development needs of financial institutions. Third, conventional deep learning algorithms often fail due to the problem of unbalanced class problems, which can be solved by artificial data and data imputation approaches. Additionally, the synthetic data can be used to train models through deep machine learning techniques and share data between financial institutions.
According to the article, there are two technical solutions for generating synthetic financial data: tabular data generation and artificial time series financial data. Tabular data can be generated using several methods, including conditional GAN, VAE, and PATE-GAN, while CT-GAN is suitable for coding 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 recording the returns of financial instruments and related time series models, but they do not offer guarantees of privacy.
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 collecting information about the data set, training and testing subsets of the data, and evaluating performance using various metrics such as confusion matrix, FPR, Recovery, Accuracy, F1-Score and Accuracy Rate. One study found that the random forest algorithm had the highest accuracy in detecting credit card fraud. Other techniques used in the study included artificial neural networks, tree classifiers, Naive Bayes, support vector machines, gradient-enhancing classifiers, and logistic regression approaches.
In conclusion, the use of financial data sets for research outside of financial institutions is highly restricted due to privacy laws. However, artificial data generation can help overcome these challenges by protecting customers’ personal information while enabling analysis and prediction. The study featured in this article identifies the key requirements for generative frameworks for creating synthetic financial data and emphasizes the benefits of generating synthetic data. Additionally, 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 prioritizing customer privacy.
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Mahmoud is a PhD researcher in machine learning. He also has a
bachelor’s degree in physical sciences and master’s degree in
telecommunication systems and networks. Your current areas of
the research concerns computer vision, stock market prediction and
learning. He produced several scientific articles on the relationship with the person.
identification and study of the robustness and stability of depths
networks