Synthetic Data for Insurance ML: When It Helps and When It Lies

Synthetic data — artificially generated records that mimic the statistical shape of real data — is having a moment in insurance, and for understandable reasons. It promises to solve the two constraints every insurance data team runs into: privacy restrictions on using real customer data, and not having enough examples of rare events.

It genuinely helps with some of that. It also quietly lies in ways that matter. Knowing which is which saves you from a model that performs beautifully in testing and fails on the cases you built it for.

Where synthetic data genuinely earns its place

1. Development and testing environments. The clearest win, and the least controversial. Your developers need realistic data to build against; giving them a copy of production means real customer records on laptops. Synthetic data with the same shape, distributions, and edge cases lets people build safely. If you do nothing else with synthetic data, do this.

2. Sharing beyond the boundary. Working with a vendor, a partner, or an academic team? Synthetic data lets you share something useful without exporting real PHI or PII. Enormously practical.

3. Balancing rare classes — carefully. Fraud is rare, so fraud models train on wildly imbalanced data. Generating additional synthetic fraud examples can help a model learn the boundary. Note the framing: additional, alongside real examples. Not a replacement.

4. Stress-testing systems. Want to know how your pipeline behaves with ten times the claim volume, or with a catastrophe surge? Generate it. Nobody needs real data to load-test.

Where it lies

Now the part that costs people.

1. It cannot contain information that isn't in the original. This is the fundamental limit, and it's worth stating plainly: a generative model trained on your data can only reproduce the patterns in that data. If your real data has no examples of a new fraud technique, no amount of synthetic generation will invent it. Synthetic data amplifies what you have; it doesn't discover what you don't.

Teams sometimes reach for synthetic data hoping to cover a gap. It structurally cannot.

2. It inherits — and can amplify — your biases. If your historical data under-represents a group, or encodes a historical pricing bias, the synthetic data reproduces that faithfully. Worse, it launders it: the bias is now in data that looks clean and carries no obvious provenance. In a regulated line where you must demonstrate fairness, training on synthetic data derived from biased history is a trap with a compliance failure at the end.

3. It smooths away the tails — which is where insurance lives. Generative models are good at the bulk of a distribution and weak at its extremes. But insurance is fundamentally a business about extremes: the large loss, the catastrophe, the unusual claim. Synthetic data tends to under-represent exactly the tail events your risk models most need to learn from, while looking statistically convincing in aggregate.

4. Privacy is not automatic. The comfortable assumption is that synthetic data is inherently safe to share. It isn't. Generative models can memorise and reproduce rare real records — and an outlier in your data (the one enormous claim, the one unusual customer) is precisely the record most likely to leak and most identifiable when it does. Synthetic data needs privacy testing, not privacy assumptions.

The rule that keeps you safe

A simple heuristic that holds up well:

Use synthetic data to build and test systems. Be very careful using it to train models that make real decisions about real people.

Development environments, load tests, vendor sharing, demos — excellent. Pricing models, eligibility models, fraud models that will decline someone's claim — approach with real caution, and never as a substitute for real data you could have used.

Where you do use it for training, the discipline is:

• Augment, never replace. Synthetic examples alongside real ones, with the ratio deliberate and documented.
• Always validate on real held-out data. A model that performs well on synthetic test data has proven it learned the generator, not the world.
• Test for bias explicitly, on the synthetic set as well as the real one.
• Test for privacy leakage — check whether real outlier records can be recovered.
• Document it. If a regulator asks what your model was trained on, "partly synthetic data derived from our 2019–2024 book" needs to be an answer you've prepared, with the reasoning.

And the thing it can't fix

Worth naming, because it's the most common misunderstanding: synthetic data does not fix bad data.

If your real data has a field that's 30% blank, inconsistent codes, and no reliable customer key, a generative model trained on it produces synthetic data with a field that's 30% blank, inconsistent codes, and no reliable customer key — now with the added confusion of not being real.

Synthetic data is a tool for privacy and volume. It's not a shortcut past the data foundation. If you're considering it because your real data is unusable, you've identified the wrong solution to the right problem.

We build the real data foundations — quality, identity, governance — that make synthetic data an option rather than an escape route. More at IntelliBooks.