How Does Synthetic Data Help in Banking?
Synthetic data is annotated information generated by computer simulations or algorithms as a substitute for real-world data. Synthetic data is generated in virtual environments rather than being gathered or measured in the actual world. The banking sector has become more digitalized, and the usage of synthetic data is increasing every day.
To fulfill a range of internal business tasks, banks must use flexible, disposable, and, most importantly, privacy-compliant synthetic data products. Synthetic data is free to use, distribute, and store as it does not have any personal information. So, let’s take a look at the various ways in which synthetic data helps in banking.
Helps in innovation
Synthetic data is a great substitute for internally stored historical data. Banks can use synthetic data to test data and integrate it with external datasets without compromising confidential data from actual individuals. Banks can also scale up as needed to ensure they have enough to train machine learning models successfully.
They don’t have to wait for the collection of real-world data before testing their models. The synthetic dataset can be used to verify models and evaluate the performance of new items, services, and technologies.
Increases abnormal, rare incidents, and fraud detection model performances
A bank’s fraud detection models will function well only if they are trained on the correct data. Synthesized datasets are adaptable, simple to update, and secure, allowing them to tune their ML models as frequently as they desire.
When compared to utilizing actual unbalanced data, synthetic data consistently improves the performance of fraud detection machine learning models by 2 to 15%. A 2% increase results in a 2% drop in false positives, saving millions of dollars in investigative operations.
Improves collaboration
Banks can distribute the synthetic data for collaboration with innovative, market-changing fintech companies across national borders. It is because they don’t have to stress about regulatory constraints, data protection, or data misuse by employees.
They have the option of sharing this synthetic data with third-party partners, vendors, advisors, and other people. Synthetic data reduces privacy threats, facilitates cooperation, and speeds up project completion.
Accelerates data POCs
Sandboxes loaded with realistic and secure synthetic data minimize vendor costs and risks while accelerating innovation and product development. Artificial intelligence-generated synthetic data is a more secure, statistically equivalent, and adaptable substitute to production data.
The synthetic datasets are easily accessible to vendors, allowing them to test their solutions in a controlled, data-safe environment. As a consequence, the average period of data delivery has been reduced from 6 to 18 months to 3 weeks. The average cost of a POC has decreased to $5k, representing an 80% drop over the prior POCs’ average price. This single step has an annual savings impact of more than $10 million.
Final thoughts
Banks and financial institutions have to employ synthetic data to stay in the competition and to keep up with the ever-changing market scenario in their sector. There is no doubt that synthetic data will be applied to more fields in the banking sector in the upcoming years.
About the Author
Sunit Nandi I'm the leader of Techno FAQ. Also an engineering college student with immense interest in science and technology. Other interests include literature, coin collecting, gardening and photography. Always wish to live life like there's no tomorrow.
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