for Finance, Banking & Insurance
AI, quantum techniques and other Big ComputeTM promise to unlock new opportunities in risk management, pricing, reducing transaction costs and overhead, and more accurate forecasting — to the tune of at least $42B-$67B in additional operating income over the next several decades, according to BCG.
Optimizing investment portfolios is a problem of great interest in quantitative finance.
Investors seek the optimal allocation of capital among assets to maximize returns and reduce risk, while respecting some investment restrictions. Today, sophisticated investors use automated algorithms to optimize the trading of assets within a portfolio, but current models can only account for so many variables before running up against the limits of classical computing.
Zapata is applying quantum and quantum-inspired methods to optimize capital allocation to achieve the minimum risk for a specified portfolio return level. Our research demonstrated that a quantum-inspired generative algorithm can improve upon results generated by state-of-the-art classical algorithms alone. Using real stock market data, we demonstrated that our proprietary quantum-inspired model could propose new combinations of portfolios with the same level of return but with lower levels of risk than the combinations generated by classical solvers alone.
Our Generator-Enhanced Optimization (GEO) strategy can be flexibly used with any generative model, including fully quantum generative models, making it a candidate for the earliest practical advantage from quantum computers. GEO could also be applied as a solution to other optimization problems in the finance industry.Read the Research
Applications for Finance, Banking and Insurance
Trading and Portfolio Management
Trading and Portfolio Management
Capital Allocation Optimization
Optimize how capital is allocated to achieve risk and return goals.
Loan Portfolio Optimization
Optimize for risk across lending product portfolios.
Reduce tracking errors in portfolios that replicate the performance of financial indices such as the S&P 500.
Determine the optimal execution strategy for high-frequency trading to increase risk-adjusted returns.
Dynamically hedge existing investments with balanced assets, such as bonds, to minimize risk.
Generate synthetic data to augment sparse datasets used to train forecasting models to better predict rare financial scenarios, such as defaults and market crashes.
Tax, Credit, and Transfer Fraud Detection
Improve graph clustering analysis and other methods to detect fraudulent activity.
Credit and Insurance Risk Scoring
Identify key features in customer data to more accurately score credit risk and insurance risk to more accurately price insurance premiums.
Accelerate Monte Carlo simulations for Credit Valuation Adjustments (CVAs), Fundamental Review of the Trading Book (FRTB), Incremental Risk Charge (IRC) and Value at Risk (VaR) calculations.
Accelerate numerical simulations via machine learning and/or quantum methods to improve precision and computational time for pricing financial derivatives such as Interest Rate Swaps, Swaptions, Path-dependent Options, and Basket Options.
Analyze time series data to identify price moves and forecast trends to build more profitable trading strategies.
Product Price Analysis
Generate synthetic data to augment sparse datasets to more appropriately price new products.
Use Case Timeline (est.)
Orquestra® Benefits for Finance, Banking and Insurance
Orchestration Across Environments
Leverage the heterogenous compute resources best suited for your tasks, without getting locked into any one hardware platform. Deploy across hybrid backends at enterprise scale.
Data Management & Velocity
Store, retrieve, and analyze large datasets. Streamline data management from ingestion to export to accelerate data velocity.
Keep what works now. Integrate existing and future solutions with a framework optimized for extensibility, interoperability, and innovation.
Workflow Development and Deployment
One unified platform to go from research to development to deployment with extensible, scalable, modular workflows.
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