Privacy fears and compliance headaches turn valuable data into a liability for banks and financial institutions. With growing cybersecurity concerns, money laundering, increased legislative pressure, and restricted access to transaction data, financial institutions face false positive rates, increased costs, and delays in lending decisions.
Synthetic data helps banks and financial institutions overcome compliance concerns, decrease false positive rates, and generate new revenue streams by providing access to AI-generated datasets with the same statistical properties as their original data.
Want to learn more about Synthetic Data, its use cases, and benefits? Check out our comprehensive guide.
Create hypothetical scenarios and simulate how a financial instrument would perform under those conditions. With synthetic data, organizations generate a diverse range of scenarios that are difficult or impossible to obtain from real-world data.
Improve fraud detection models and reduce the number of false positives with synthetic data. With it, organizations can simulate different risk scenarios and fine-tune their risk management strategies.
Synthetic data allows financial institutions to generate digital twins of customers and simulate their credit scores, enabling lenders to make more accurate loan origination decisions and better understand the creditworthiness of their clients.
With synthetic data, institutions generate data on different investment scenarios and evaluate the performance of various portfolios. This helps them identify the most profitable portfolios, leading to better returns for their clients.
Organizations train and test their anti-money laundering (AML) models by generating large sets of synthetic transactions. Patterns of criminal activity can still be seen in the synthetic data, allowing them to stay ahead of new criminal tactics.
Synthetic data is not immune to bias but helps reduce the risk of data being used to perpetuate prejudices. By creating datasets more representative of the entire population, institutions will ensure their models are not based on faulty data.