Financial and insurance institutions are embracing AI to optimize decision-making, risk assessment, customer engagement, and operational efficiency, but face strict regulatory and privacy restrictions. Dedomena.AI's secure synthetic data and AI infraestructure facilitates the innovation, development, and optimization of data and AI solutions without barriers or friction.
The financial services industry—including banking,
capital markets, fintech, and insurance—faces common
challenges in analyzing and monetizing customer data
at enterprise scale. Data silos, labeling issues,
stringent privacy regulations, and cybersecurity
concerns obstruct access to actionable insights and
erode competitive advantage.
Synthetic data offers a powerful solution across all
these sectors, enabling personalized experiences
while upholding the highest privacy standards. By
leveraging synthetic data, organizations can break
down silos, meet compliance requirements, and
accelerate AI/ML model development, software
testing, and secure data-sharing initiatives.
Dedomena’s synthetic data platform anonymizes and
generates high-quality, statistically representative
datasets that are 100 % GDPR-compliant, flexible,
and enriched. With our infrastructure, banks,
capital-markets firms, fintechs, and insurers can
bridge the innovation gap, unlock new revenue
streams, and make confident, data-driven decisions.
Dedomena.AI helps financial and insurance institutions leverage AI to enhance decision-making and efficiency while navigating strict regulations. Its secure synthetic data platform enables innovation without compromising compliance. Below, explore several strategic use cases across banking, capital markets, fintech, and insurance.
Dedomena.AI generates synthetic transaction and credit behavior data to develop scoring models that assess customer risk and credit potential while eliminating bias and protecting privacy.
By simulating structured transaction anomalies, synthetic data allows institutions to train fraud detection systems that identify unusual behaviors without exposing real financial records.
Use synthetic behavioral and engagement datasets to predict attrition, model loyalty drivers, and tailor retention interventions.
Train AI models using enriched synthetic customer profiles and transaction data to generate accurate, compliant cross-sell and upsell recommendations.
Model synthetic claims data and historical fraud cases to automate claim approvals and flag suspicious activity for human review.
Dedomena.AI creates synthetic account, loan, and card activity logs for fintechs to test products, compliance tools, and analytics without accessing real bank data.
Generate privacy-safe synthetic applicant profiles to train risk models that power personalized underwriting decisions in health, auto, and property insurance.
Develop synthetic behavioral datasets that simulate identity theft, shell company activity, or layered transactions to test anti-money laundering models and KYC compliance workflows.
Model realistic segments based on income, product usage, life stage, and spending to drive targeted financial marketing and product design.
Dedomena.AI enables stress testing of financial systems by simulating crisis scenarios, market shifts, or policy changes across synthetic portfolios.
Create synthetic equity, FX, commodity, and macroeconomic indicators to train forecasting models, backtest algorithms, and perform risk analysis.
Build synthetic datasets to model default behavior across geographies, borrower types, and loan products for improved lending decisions.
Generate feature-level synthetic biometric metadata (keystroke, velocity, IP) to develop identity verification and fraud prevention systems.
Use synthetic customer engagement and transaction logs to estimate long-term value and inform acquisition and retention strategies.
Simulate asset allocation, return, and volatility across synthetic investor profiles to optimize portfolio strategies.
Train models on synthetic balance sheets and transaction flows to ensure compliance with Basel III, Solvency II, or IFRS 17 capital standards.
Allow data partnerships between banks, insurers, and fintechs using Dedomena.AI-generated synthetic data that preserves value while avoiding privacy violations.
Simulate surrender, claim filing, premium payment, or lapse behavior to improve actuarial models and predict product profitability.
Use structured synthetic documents (contracts, disclosures, invoices) to train AI models for document classification, summarization, and information extraction.
Enable advisors to interact with structured synthetic data and embedded models to support client onboarding, product selection, and financial planning recommendations.
Check out some of our explanatory articles or
cross-industry
use cases to know more
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