Leveraging Synthetic Data for Innovative Finance and Banking Solutions

Reduce lending risk, predict fraud attempts, and stay compliant with data privacy regulations by using synthetic data in your finance and banking operations.

Synthetic Data for Banking and Finance

Silos and Compliance Barriers Prevent Data-Driven Insights for Banks and Financial Institutions

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.

Overcoming Data Compliance and Usage Limitations of Financial Institutions

Unlocking New Possibilities in AI-Driven Projects with Synthetic Data: The Story of SIX

Learn how Syntheticus® is helping SIX with artificially generated synthetic data that mimics the original data while respecting the need for privacy, to unlock data’s full potential and create business value.

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The Syntheticus Suite Unlocks the Data Potential for Banks and Financial Institutions

Advanced data generation

The Syntheticus Suite seamlessly generates high-quality datasets that closely match the statistical properties of the original data. It gives banks and financial institutions easy access to the data they need to run predictive models, testing, and other analytical tasks while ensuring consistent results and reducing overall time to data.

Synthetic Data for Finance_Advanced data generation

Privacy-preserving features

The Syntheticus Suite leverages Differential Privacy and Trusted Execution Environment to protect sensitive financial data, allowing banks and financial institutions to share data for external collaboration safely. As AI-generated synthetic data doesn't contain any PII, banks and financial institutions collaborate with their industry partners without GDPR fines or other compliance penalties.

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Flexible integrations

Whether it's shared through APIs, batch uploads, or direct access to data warehouses, the Syntheticus Suite offers flexible integration options that make it easy for banks and financial institutions to incorporate synthetic data into their existing processes and systems. As a result, organizations streamline their data management processes, reduce costs and resource requirements, and easily access the data they need to drive strategic business decisions.​

Synthetic Data for Finance_Flexible integrations

From Stress Testing to Fraud Detection: Synthetic Data is Changing Finance

While digital transformation is a top priority for banks, it comes with challenges like privacy regulations, workforce training, and outdated legacy systems that can’t accommodate the needs of modern financial institutions.

Synthetic financial data provides a solution and offers a wide range of use cases across finance domains such as:

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Stress Testing and Scenario Analysis

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.

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Fraud Detection and Risk Management

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. 

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Credit Scoring and Loan Origination

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.

Portfolio Optimization

Portfolio Optimization

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.

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Anti-Money Laundering

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.

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Data Bias Reduction

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.

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