Artificial intelligence has a diverse set applications in financial services from process automation to chatbots and fraud detection. Reviews show that the combined potential cost savings for banks from artificial intelligence applications would be $ 447 billion by 2023.
However, some of these applications have limitations because financial information is one of the most sensitive and personally identifiable types of information. To illustrate, 87% of Americans consider credit card information is reasonably or very private. The same figure is 68% for health and genetic data and 62% for location data.
Financial institutions can take advantage of synthetic dataor artificially data based on real data to overcome privacy (and other) challenges and provide innovative products and services to their customers.
Below you will see the cases / benefits of using synthetic data in financial institutions:
Enables information sharing, collaboration and innovation
Provisions such as the GDPR and CRPA can prevent the sharing of financial information both within a company and between institutions. This can prevent valuable collaboration between financial institutions and fintech partners or between teams within the institution. Granting access to third parties can take months of bureaucratic procedures or may not even be possible. This makes it difficult for a financial institution to evaluate potential partners before developing new products.
Anonymous sensitive information with traditional data camouflage techniques may be vulnerable to linking attacks before sharing. The purpose of these attacks is to re-identify individuals from an anonymous set of information, often by linking it to other publicly available material. According to the year 2000, generally quoted research, 87% of the U.S. population can be identified by combining their gender, birthday, and zip code.
Synthetic data can eliminate the risks of sharing. Instead of original data, financial institutions can share synthetic information that preserves important properties of the original data. Synthetic knowledge production techniques can be applied to a wide variety of data types from tables to time series and artificial images.
Producing mandatory synthetic economic time series is significantly more difficult than tabular data. This is because in a time series file, all observations depend on a series of previous observations. Hazy provided synthetic customer transaction data to a banking customer who wanted to collaborate with fintech companies. The synthetic data set includes statistics and relationships to banking transaction data, but does not include data on actual customers.
Allows you to predict rare events (e.g., fraud)
Detecting fraudulent events is one of the most important applications of machine learning in finance. However, the fraudulent banking file is unbalanced: fraudulent activity accounts for a small percentage of all activity. It is challenging for the ML model to learn from this type of material to detect new cases of fraud because a small amount of data can lead to inaccurate results.
The subsample and the subsample are two techniques for handling unbalanced data sets. Subsampling includes the removal of non-fraud findings to balance the data set. It requires a large data set, as deleting observations can cause prejudice.
The oversampling, on the other hand, produces new cases of artificial fraud that resemble real fraud. The ML model can then be trained in a balanced data set for more accurate results. Synthetic data generation techniques can be used to create artificial fraud cases to obtain a balanced data set.
Sometimes financial institutions may want to test strategies in extreme conditions, such as market crashes or application failures. Instead of having an unbalanced set of data on such events, they may lack data due to these circumstances. Synthetic data can be used to fill these gaps and can help organizations develop strategies against such events.
Improves the accuracy of supervised in-depth learning models
Most machine learning models, and in particular in-depth learning models, are information-hungry. Although a financial institution does not lack data to train the ML model, the accuracy of ML models depends significantly on the size of the data. Synthetic data can be used to increase the size of the data.
In addition to the magnified data size, the tagged data is another advantage of synthetic data for model accuracy. This is especially important for supervised learning applications because these types of models learn from tagged data. Marking of information is a labor-intensive process and manual entries are prone to errors that can cause model inaccuracies. Synthetic data have the correct notations for observations, it eliminates the need for data notation and allows for more accurate ML models.
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