“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.”
—Ginni Rometty, former CEO of IBM
TL;DR: Synthetic data for finance delivers so much more than unbreakable privacy. With the right approach, it will elevate your machine learning projects and products.
The Bottom Line
When you think of synthetic data, what benefits spring to mind? Do you see this as predominantly a fix for data privacy issues, allowing financial institutions to experiment with ambitious new ML models without falling foul of privacy regulations?
Well – yes. Of course, data privacy is paramount. But generating synthetic data for financial institutions opens up a whole range of opportunities and improvements to your ML pipelines and model development. Many of which have nothing to do with privacy or security.
That’s because creating your own synthetic data gives you an enormous amount of control over how that data is structured, which insights it focuses on and how biased it is overall. You can focus solely on generating data you lack internally. You can zoom in on parts of the data that help you meet pressing customer needs. You can augment your data with nuance and context from external sources – leaving you with a complete, annotated, ML-ready dataset. And all the while, you have complete, compliance-friendly transparency and visibility over the process.
Your predictive insights and your AI-driven products are only ever as good as the data you put into them. If you’re switching to synthetic data, make sure you’re squeezing every bit of value out of the opportunity.
Under the Hood
The benefits of synthetic data to your AI efforts go so much further than privacy. To help you derive maximum value, here are the 5 most important factors to consider when you start generating financial data for machine learning.
Focus on Customer Needs
Before you start, you need to have established clearly in your mind how generating this synthetic financial data will ultimately benefit your customers. If the ML model you plan to build will form the basis of a new customer-focused product or application, is this something your customers actually want or need? Will it reduce their pain points? Make it easier to contact you or resolve an issue? Save them money or hassle? Or have you been thinking solely about your internal needs, such as cutting operational costs?
If asking this question makes you realize your focus has been the latter, you will need to think carefully about how you can adapt your strategy so that the end product brings clear gain to the customer. Otherwise, persuading them to use whatever you create will be a hard sell – and they’ll resent you for it.
The same goes for any ML models or AI projects you plan to use internally. What exactly are you hoping to find out, and how will this enhance the service you offer customers? For example, if you’re looking to improve fraud detection rates and KYC accuracy, have you framed this exclusively as a way to reduce your risk exposure and keep down costs for the business? Or are you thinking about this as something that will speed up onboarding for trustworthy customers, and reduce the amount of time and paperwork they need to part with before you reach a lending decision?
It’s a subtle shift in attitude, but it makes a big difference to the way you design your products and models. By extension, it changes the way you approach your data generation, for example:
- By focusing on synthetic financial data relating to customer pain points and grievances, rather than just the things that take up in-house resources, you will spot opportunities to improve user experience.
- If you’re trying to figure out more sophisticated ways to help deserving customers with nonexistent credit ratings prove their creditworthiness (rather than zooming in exclusively on preventing fraud and defaults) you may want to think about ways to eliminate data bias in your synthetic data, or how to augment your dataset with alternative sources.
Framing your ML efforts in these terms means you produce more of the data that gives you the insights you need. This leads to successful products and projects, and happier customers. In the end, that will deliver better business outcomes than if you had been preoccupied solely with your company’s needs.
Explore Strategic Collaborations
Typically, banks and financial institutions have wisdom, experience and data on their side. What they lack is the agility and skillsets of, say, a disruptive new fintech to develop fresh, innovative products. This is what makes collaborations between the two so exciting, productive – and lucrative.
In the past, scope for these kinds of partnerships was limited by data privacy concerns. Getting the sign-off to share sensitive data with external (or even internal) partners could take months or even years of bureaucracy. This made it particularly difficult to evaluate a potential fintech partner, as banks couldn’t even request a demo or commission a pilot project without excruciating delays. If a fintech was based overseas or lacked the right security infrastructure, data sharing was impossible.
Synthetic data for finance has changed all that. Now, instead of sharing your original, protected data, you can simply create synthetic financial datasets to share with external fintech. These can be shared at will and stored in the Cloud, since there is no disclosure risk to worry about. As a result, you can really start to capitalize on the most promising strategy collaborations.
If you don’t plan to generate your own finance datasets for machine learning, it’s important that you also think carefully about your choice of synthetic data partner. You need to trust this vendor to produce high-quality, relevant data that perfectly suits your purposes – and not all providers are created equal. Make sure that whoever you work with not only has the data science expertise, but also understands the specific challenges and requirements involved in generating synthetic data for financial institutions.
Create the Data You Lack
The great thing about synthetic data is that you’re not bound by the data you have. You can create as much data as you like from your source material. That includes focusing on segments of the data that you need more of in order to properly train and test your models.
For example, let’s say that you need to build an ML model that helps you accurately predict outcomes amid extreme market conditions, minority incidents (like money laundering attempts), or rare events, such as app failures. The whole point of extreme, minority, and rare events is that they are (thankfully) unusual – but that means that available data on them is thin.
However, when you’re generating synthetic data for finance, you can fill these gaps, scaling up the existing data you do have into robust datasets that are large and complete enough to train your models. This leads to better, more accurate, more useful insights.
Augment with Relevant Data Sources
Augmenting your core datasets with relevant insights from external and alternative sources is advisable in any machine learning project. It gives you the additional context and nuance you need to make sense of your data, leading to more accurate predictions.
When you’re creating synthetic datasets, there really is no excuse not to augment your datasets. A top synthetic data vendor will manage or automate much of this process for you and will deliver a final dataset that’s fully labeled, annotated and set up in the format you need to feed directly into your machine learning model with ease.
Keep it Transparent and Trackable
Regulatory compliance is a perpetual headache for financial institutions. Especially when it comes to justifying your lending decisions. When asked by a regulatory authority, you have to be able to elucidate exactly how you came to reach any given decision. This can start to get tricky when you’re using AI engines and ML algorithms to help you get there.
It’s absolutely vital that you can track and explain the complete ML development process – and that leads right back to your original data generation strategy. Try to develop a transparent, well-monitored process right from the start, and discuss with your synthetic financial data partner what documentation you will need along the way.
Final Thoughts: Getting ML Right, Right from the Start
One of the big benefits of using synthetic data is that you’re in control. You can scale it up if you want. You can adjust parameters. You can pick out just the details you need to focus on for a particular model. You can produce a dataset that’s labeled or unlabeled, depending on your needs.
This makes it so much easier to start your project on the right footing – which is vital, as any issues or oversights in your training data will get you into technical debt later on. Take the time, right at the beginning, to figure out exactly what you need from your data and to ensure you’re adhering to these 5 principles in your generation strategy. Trust us, you’ll thank us later on.