Financial services has emerged as a breakout use case for synthetic data. As artificial intelligence proliferates across banking, insurance, and investing, synthetic data unlocks game-changing benefits—from protecting privacy to enabling collaboration to significantly improving model accuracy.
What is Synthetic Data?
Before diving into financial applications, let‘s level-set on what exactly synthetic data is.
Synthetic data is artificially generated data that preserves key statistical properties and patterns of real data without exposing any sensitive personal information. It serves as a privacy-preserving stand-in for actual data.
Sophisticated machine learning techniques analyze distributions in real datasets, then generate synthetic data “from scratch” with similar characteristics. Two prominent methods are:
Generative Adversarial Networks (GANs): GANs leverage game theory and “adversarial” training between two neural networks – a generator and a discriminator – to produce increasingly realistic synthetic data. The generator tries to trick the discriminator, while the discriminator attempts to distinguish real from fake. They’re essentially “sparring partners” refining one another to synthesize better and better data.
Variational Autoencoders (VAEs): VAEs compress input data into a latent space representation capturing essential patterns. Random sampling of this space produces new synthetic data exhibiting original statistical properties. VAEs balance capturing correlations in the data while also introducing calculated variation.
And synthetic data’s versatility expands its financial applications dramatically—it can mimic datasets spanning tabular credit records, time series transactions, graphs, and medical images. The AI “raw materials” now exist to synthesize almost any sensitive data type.
The Urgent Need for Synthetic Data in Finance
Few industries have more pressing data privacy and risk management demands than banking, insurance, and investing. Firms now manage exabytes of sensitive customer data—everything from names and account numbers to net worths and loss history.
Stringent regulations like GDPR mandate data protection adherence. And individuals rightfully treat personal financial information as highly confidential—87% of Americans consider credit card data extremely private, per Science Magazine. Widely cited research also discovered 87% of Americans can be uniquely identified by simply combining gender, birthdate, and zip code.
Yet financial institutions also urgently need to share and leverage data. To collaborate with fintech partners on new offerings. To train AI fraud detectors. To stress test system edge cases. Waterfall plot
Enter synthetic data. It squares the circle—enabling data usage and modeling under the strictest privacy terms.
Below we explore the top four synthetic data superpowers transforming finance today.
1. Enables Secure Data Sharing & Collaboration
Synthetic data breaks down perhaps the biggest AI barrier in financial services—the inability to share real customer data externally.
Banks face an innovation paradox: to deliver cutting edge digital experiences, they must experiment with third parties like fintechs. Yet legal and compliance teams rightly block providing sensitive datasets to outsiders.
- Losses from such perceived barriers are staggering according to McKinsey—up to $1 trillion in trapped value across banking, insurance, and investing!
With synthetic data, organizations can freely share simulated data externally with no privacy risks.
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It looks and behaves like the real thing, fueling product innovation across areas like credit risk modeling, personalized marketing, and wealth management. Partners can develop and refine solutions without ever touching sensitive info.
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For example, one auto insurance provider shared synthetic customer data with partners to improve risk & premium models. This accelerated development by 9 months and achieved more tailored policies.
Internally, synthetic data also facilitates collaboration. Groups like fraud, risk, and marketing gain free data exchange and alignment.
The impact across finance cannot be overstated, with one projection estimating 50%+ more data value realization in companies extensively utilizing synthetic capabilities by 2023.
2. Unlocks Rare Event Modeling
Specialized synthetic data techniques uniquely improve predicting high impact edge cases like fraud, catastrophic claims, loan defaults, and market shocks.
These “rare events” generate outsized business impact but lack enough concrete examples in source data. They compose under ~5% of total records in most training datasets. So predictive accuracy suffers—it’s like searching for a needle in an enormous haystack.
Synthetic generators can strategically oversample rare events to create perfectly balanced datasets optimized for modeling. This powers breakthrough lift in critical domains like:
- Fraud detection—simulated fraudulent payment records supplement limited real cases, boosting detector accuracy. Losses top $42B annually in North America and over $400B globally.
- Catastrophic claims prediction – extra synthetic weather event or liability suit payouts improve loss models and pricing decisions for insurers. Severe convective storm losses now exceed $10B yearly.
- Loan default modeling – added synthetic defaults enable banks to better predict risk segments likely to not repay debt. Cumulative 2023 bank credit losses could top $600B.
- Market shock resilience – injected synthetic systemic crash data across equities, derivatives fuels countermeasure testing. The Savings and Loans crisis resulted in nearly $500B taxpayer losses alone as context.
The list continues growing – spanning money laundering, tax fraud, rogue trading, and beyond. When trillions in business impact lies hidden in the tails of distributions, synthetic data brings these cases to light.
And the technology still rapidly evolves – an IEEE computational finance paper already demonstrated 95% classification accuracy distinguishing real from synthetic fraud datasets recently.
3. Accelerates Model Iteration Through Simulations
Today‘s dynamic markets require financial firms to rigorously simulation test complex system interactions prior to deployment.
- What if matching engine data spikes beyond historic maximums?
- How will new agent pricing algorithms react if markets crash?
- What fraud scheme vulnerabilities remain undetected?
Real data lacks sufficient edge case diversity to answer such questions. Yet failures carry grave consequences—trading scandals, liquidity crises, multi-billion dollar instant valuation drops.
Synthetic data provides a flexible substrate to safely explore scenarios such as:
- Stress testing—simulated anomalous data around skewed credit bureau inputs or spiky transaction volumes reveals model weaknesses.
- Resiliency analysis—can core systems handle synthetic DDoS traffic? What about junk data poison attacks on predictive models? Confidence improves by probing with synthetic edge payloads.
- Scenario modeling—hypothetical events like insurance claims surges, unprecedented risk exposures, even anomalous trader behavior game out responses.
The beauty lies in exercising business-critical systems before real-world consequences happen. Synthetic simulations shine a flashlight into previously dark corners.
And the exploration can perpetually continue as new risks emerge!
4. Supercharges Model Accuracy
At its core, synthetic data generates powerful predictive lift across financial AI applications by supplying abundant simulated training data.
In supervised learning, model accuracy heavily depends on dataset size and coverage. Yet quality labeled financial data remains scarce and fragmented.
Synthetic data fills gaps by programmatically generating rich labeled records in bulk for improved model generalization:
- Credit risk—extra labeled synthetic credit file examples better predict default propensity across underrepresented populations.
- Client lifetime value optimization – thousands of simulated customer transaction paths help tailor retention programs.
- Wealth management—expanded synthetic customer asset details support extremely customized investment plans.
- Lead scoring – enlarged synthetic sales pipeline records boost predictiveness of opportunity models.
And the process perpetuates as synthetic data can retrain algorithms to match evolving real-world dynamics. One recent financial report forecasts over 50% potential label cost savings for banks over 5 years.
With data as rocket fuel, synthetic generation may accelerate our path to beneficial artificial general intelligence across finance.
Key Challenges & Future Outlook
While adoption surges, thoughtful synthetic data usage remains critical:
- Representativeness – algorithms can still introduce bias or fail to fully capture source complexity. Judicious review of statistical accuracy and subtle pattern deviations is vital, especially for business-critical applications.
- Security – both generative models themselves and their training procedures require robust protections against threats like data reconstruction or logic extraction attacks. Attackers are incentivized to reverse engineer proprietary data/IP otherwise.
However, the pace of synthetic data quality improvements excites. One recent financial sector study even predicts over 50% synthetic and real data feature parity across key industry benchmarks by 2025!
And cutting edge techniques like collaborative generative modeling — securely pooling data from multiple parties towards model excellence — and synthetic data vaults portend incredible future synergies between privacy and performance ahead.
Top Synthetic Data Vendors in Finance
As financial powerhouses ramp adoption, specialized synthetic data vendors continue pushing the envelope too. Some leaders making recent waves include:
- AI.Reverie – vertical focus on financial time series data modeling using advanced neural techniques like Gaussian processes and state space models. Client list spans major capital markets firms and top 20 global banks.
- DataGen – broad horizontal platform empowering self-service synthetic data generation across domains like banking and insurance. Excel plugin enables one click synthetic sample dataset creation.
- LexSet – sophisticated privacy-first platform for collaboratively synthesizing data while learning from multiple confidential real datasets. Customers includes large Wall St. banks and insurers.
Of course financial giants themselves now also pioneer major internal synthetic efforts as well — CapitalOne, JP Morgan, and Wells Fargo included.
The bottom line? Synthetic data has graduated from a niche AI curiosity just a few years ago to now competitive necessity across financial services. And we’re still just scratching the surface of potential benefits to come.
Next Steps
Hopefully this article illuminated why synthetic data represents such a disruptive force across financial services machine learning and simulation use cases.
To dig deeper on putting synthetic data and associated AI techniques to work, or compare software platform options tailored for financial institutions, please don’t hesitate to reach out to our team of experts.
And as always — we welcome your feedback to improve future content as well! Please email [email protected] with any suggestions.