“Predictions have an expiry date. Action is needed before predictions expire.”
The Bottom Line
Data drives AI – but only if you can actually use your data for AI!
Many organizations that have painstakingly accumulated mountains of insight-rich customer data are grappling with the frustration of not being able to use that data for their machine learning projects for fear that they’ll fall on the wrong side of GDPR and other regulations.
This has ushered in a new era of artificial data generation. But while many companies understand the privacy and security benefits of synthetic financial data, fewer realize how many other perks synthetic data generation brings to the AI development process.
Switching to synthetic data speeds up model time to deployment by facilitating more efficient collaborations, reducing data preparation time, ensuring data is properly annotated and delivered in the right format, and helping organizations get more of precisely the data they need for their projects. It also creates more opportunities for monetization – and means you can keep hold of historical data for as long as you need.
Put it this way: data drives AI. Synthetic data turbocharges your AI projects.
Under the Hood
When developing a new AI project, do you look at the data you have in-house as an asset or a hindrance to your plans? Sure, it may contain all the insights you need for razor-sharp predictive insights, but can you actually use it freely and easily? Will the process of making it available and feeding it into your models be a time-consuming headache? Are you worried about compliance?
If these concerns sound familiar, you’re not alone. Most organizations that work with sensitive data are caught in a Catch-22 where they own valuable data, but struggle to put that data to work in a genuinely useful timeframe.
This is where synthetic data generation comes in. Taking control of your training data generation not only tackles your most pressing privacy and security problems – it also provides a route to supplying high-quality, perfectly-tailored data at speed, accelerating the progress of your AI efforts.
Holding onto data for longer
One significant challenge created by GDPR compliance is ensuring you have enough historical data to explore long-term trends. Machine learning models need plenty of precisely the right kind of data to draw out nuance and connect the dots over time – including from relevant date ranges. GDPR, meanwhile, dictates that you periodically delete personal data if it’s no longer being used for its initially-stated purpose.
Rather than having to go back to your original website users to seek permission all over again, synthetic data generation allows you to create whole new datasets that aren’t governed by GDPR rules. This gives you all the insight of the original dataset, but means you can hold onto the data for as long as you want. When you’re ready to embark on your AI project, you’ll have the data you need to hand rather than having to acquire this from scratch.
Creating monetization opportunities
Privacy regulations also prevent companies from selling customer data without their consent – but, again, this only applies to the authentic, original, sensitive data. If you’re creating synthetic data (even synthetic financial data), you are far freer to repackage it and use it for data science-driven products… and ultimately, to sell to third parties.
In the financial sector, you may be sitting on a wealth of data related to payment trends and patterns, locations, payment types and so on. This kind of information is likely to be incredibly valuable to a range of other industries. Using your original data for an AI-based product that delivers value without overstepping regulatory boundaries might be a lengthy, tricky process – or may be unviable altogether. A synthetic data AI strategy could help you leap over the hurdles and start creating a lucrative product much faster.
Sharing data internally and externally
If you’re just embarking on your AI journey, the chances are that you won’t have all the expertise you need in-house to develop a groundbreaking new AI product or perfectly-conceived predictive model. This is the driver behind many successful collaborations between financial institutions and external vendors – particularly fintechs. These arrangements allow you to combine your sector knowledge and data resources with an agile, creative company at the cutting edge of tech.
At least, in theory. In practice, you may find that you are prevented from ever getting a collaboration off the ground because you simply have no way of sharing your datasets with these external vendors safely. Perhaps they lack sufficiently robust security infrastructure to put your mind at rest. Perhaps privacy regulations prevent you from transferring data to their servers or to the cloud – especially if the data would need to cross national borders. Perhaps it’s going to take so many months to get internal sign-off that, by the time the project is underway, you will have missed the commercial opportunity.
These problems don’t only apply to external partners. Sometimes, it’s hard enough just to forge cross-department collaborations. These limitations present a major barrier to AI development.
Synthetic data generation bypasses these problems by providing a collaboration-ready dataset. You don’t have to fret about leaks, breaches or compliance issues because you’re not using real data. You can speed ahead with your most ambitious AI projects – without sacrificing peace of mind.
This applies, too, when you’re first choosing your collaboration partners. It’s highly unlikely you’ll want to put all your eggs in one fintech or vendor’s basket without being absolutely certain that they’re up to the job. But, of course, no one wants to wait around for 18 months to get permission to share a test dataset, only to decide that they don’t want to work with that third-party vendor after all.
Having a synthetic dataset ready to share means you can get demos, assess potential and make a decision in a fraction of the time, accelerating your AI plans.
Creating images to train machine learning models
One lesser-used but fascinating new synthesis AI application is the creation of images which are in turn used to train image-recognition algorithms. Many people are uncomfortable with their real images being used for facial recognition software – and readily available sources of images are often too limited to train a model without bias.
For example, if you are training a model to recognize faces but you only have a tiny selection of images of people from one particular race, the resulting model will do a poor job of recognizing other people’s faces from the same race. Acquiring large volumes of images to fill in the gaps in your model training can be very tricky, though. Instead, some companies are exploring the use of GANs to generate images of people (e.g. www.thispersondoesnotexist.com) but adjusting the parameters to produce more images of people from particular demographics.
It’s a very different approach to using synthetic financial data, but an interesting idea to keep in mind, depending on the kinds of AI products you’re looking to make.
Producing better-quality data for AI
A common frustration when developing any machine learning models is data quality. From missing and duplicate values to inconsistent formatting and a lack of annotation, the simplest problems can absorb a lot of time and put you in technical debt later on. The longer you spend on data preparation – cleaning, harmonizing, annotating and so on – the longer the lead time to deploying your model.
The great thing about synthetic data is that you control the production process. That means you get to decide when generating the data whether it’s labeled or unlabeled, for example. You can decide from the start the structure you want the final dataset to take and can even augment this with data from other sources. That means higher quality data to get your model off to a flying start.
Creating just the data you need
What’s more, when you’re generating your own data for AI, you get to decide which parts of the dataset you need the most for the challenge at hand. For example, if you’re modeling app failures for QA and testing purposes, you don’t need to replicate every instance of the app working perfectly, but you may need a lot more data on failure events. That means you can choose to generate much more of the type of data you need for your model, without getting overloaded in the data that isn’t relevant right now.
Final thoughts: data for every possibility
When it comes down to it, the really exciting thing about synthetic data is its flexibility. When you’re using original data, you’re bound by a myriad of rules and regulations. You can’t simply release the data you need and share or use it how you want. It’s extremely limiting.
But with synthetic data, you can generate exactly what you want, at the volume you need. You can share it with whomever you like. You can move it around, put it in the cloud, collaborate across borders. You can annotate it as it’s produced, in a way that fits perfectly with your purposes. You design your training data generation to fit with your model.
Ultimately, this is what accelerates your AI projects. Your synthetic data never constitutes a problem that you need to get over; rather, it’s the solution. If you approach it right, it alleviates your data privacy, protection, scalability and availability headaches. That frees you up to focus on developing the most effective and lucrative machine learning models to drive your business forward.