AI Solutions
for Finance, Banking & Insurance

AI, quantum techniques and other Big Compute^TM 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.

Featured Use Case

Portfolio Optimization

Challenge

Approach

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.

Use Cases

Applications for Finance, Banking and Insurance

Trading and Portfolio Management

Risk Management

Pricing

Trading and Portfolio Management

Risk Management

Pricing

Key Challenges

Dynamic optimization of capital allocation
Maximizing returns while minimizing risk
Obtaining solutions beyond mean-variance optimization
Estimating exposure associated with price movements in correlated securities
Implementing constraints imposed by investors and regulators
Optimizing portfolios with exceptionally large numbers of assets
Timing transactions to balance market impact versus exposure to volatility
Minimizing transaction fees and commissions

Zapata Solutions

Capital Allocation Optimization

Optimize how capital is allocated to achieve risk and return goals.

Loan Portfolio Optimization

Optimize for risk across lending product portfolios.

Index Tracking

Reduce tracking errors in portfolios that replicate the performance of financial indices such as the S&P 500.

Algorithmic Trading

Determine the optimal execution strategy for high-frequency trading to increase risk-adjusted returns.

Auto-Hedging

Dynamically hedge existing investments with balanced assets, such as bonds, to minimize risk.

Key Challenges

Achieving better tail risk estimates
Performing accurate stress tests to fulfill regulatory requirements (CCAR, Dodd-Frank, etc.)
Simulating market returns for financial crisis forecasting
Identifying potential fraud by detecting outlier transactions

Zapata Solutions

Risk Estimation

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.

Stress Testing

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.

Key Challenges

Costly and time-consuming Monte Carlo simulations for pricing exotic derivatives
Identify mispricing and arbitrage in illiquid markets
Price pattern and trend recognition

Zapata Solutions

Derivative Pricing

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.

Price Forecasting

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.