Did you know that synthetic financial data offers benefits that go way beyond privacy protection?
Sure, being able to use your finance datasets for machine learning without worrying about disclosure risks is a major plus, but it’s only the tip of the iceberg. When you create synthetic data for finance industry AI, and advanced analytics projects, you also get extensive control and flexibility over the data generation process.
That means you’re not bound by the limitations of the original data. You can pick and choose which subsets of the “real” data are most useful to you right now, creating more of what you need and less of what you don’t. You can adjust parameters and choose whether or not you want the synthetic financial datasets to be labeled. You can even combine synthetic data generation with automated data augmentation to produce your ideal training datasets.
In fact, you may even be able to repackage the synthetic financial data into products you can sell to third parties. It’s an excellent way to monetize your data assets without falling foul of privacy regulations.
Having this freedom is great for the quality of your data and the success of your projects. That said, it also means you need to think carefully about how to approach the process of generating synthetic data for financial institutions.
Here Are Some Top Tips for Generating Synthetic Data for Financial Institutions
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Start with the customer pain points
How might the data help you answer questions or tackle problems from the customer’s point of view? Switching from focusing on your internal needs to thinking about improvements your customers might like to see will change how you use the data. In turn, that affects the types of data you need to focus on and the types of synthetic data you’ll need to generate from the original dataset.
Consider collaborations with external partners
When you’re using authentic, sensitive data, including financial data, you need to be extremely careful about how you store and share it. The likelihood is, you won’t be able to risk putting it in the Cloud, for example. You’ll be limited in what internal or external partners you can work with. Generating synthetic financial data, on the other hand, frees you up to work with developers and fintech companies, pooling your expertise to make truly game-changing products. It also gives you a training dataset that you can send out to potential partners to try them out before agreeing to partnership – without jumping through all manner of bureaucratic hoops first.
Focus on the data you need more of
Many finance organizations that are just getting started with synthetic data don’t realize that they can actually hone in on certain parts of the dataset, generating more of the data they lack for their projects. For example, let’s say you need more data on rare events, like fraud attempts, or app failures / unusual bugs that interfere with performance for QA testing. You can opt to generate synthetic datasets that scale up these parts of the data, giving you the volume you need for your models.
Augment your datasets
Data augmentation allows you to add nuance and context to your datasets. When you’re using real data, these extra details increase the risk of reidentification. Not so when you’re augmenting a synthetic financial dataset, though. Plus, the best vendors will be able to combine the processes for maximum efficiency.
Making your decisions trackable and traceable
Demonstrating to regulators exactly how you reached any given lending decision can get a lot more difficult when you start using AI algorithms to guide those decisions. It’s crucial that you get a system in place that gives you total visibility over the whole ML process – including the synthetic data generation stage.
Final thoughts: don’t trust your data with just anybody
While disclosure risk isn’t a problem with synthetic financial data, mistakes in the way it’s generated will seriously undermine quality and limit the success of your projects. If you don’t have the expertise to produce synthetic data yourself, you’ll have to rely on an external partner to do this for you.
Working with a vendor that really knows their stuff and understands the financial space is key here. They should have deep knowledge of your technical requirements, of course. But they also need to appreciate the regulatory space you need to navigate – and the specific ML applications you’re exploring that benefit from synthetic data. In the quest for top synthetic data for finance, finding the right vendor is half the battle.
