Exploring quantum computing use cases for financial services

Today’s financial services customers demand personalized products and services that rapidly anticipate their evolving needs and behaviors. Twenty-five percent (will open in a new window) of small- and medium-sized financial institutions lose customers due to offerings that don’t prioritize customer experience. It’s difficult to create analytical models that sift through mounds of behavioral data quickly and accurately enough to target which products are needed by specific customers in near real-time. This constrains financial institutions from providing preemptive product recommendations with optimal feature selection in an agile manner, missing opportunities to expand current customer share of wallet (will open in a new window) or reaching the 1.7 billion adults worldwide (will open in a new window) who are unbanked.

A similar problem exists in fraud detection. It is estimated that financial institutions are losing between USD 10 billion and 40 billion in revenue a year due to fraud and poor data management practices. Fraud detection systems remain highly inaccurate, returning 80 percent false positives (will open in a new window), causing financial institutions to be overly risk averse. To help ensure proper credit scoring, the customer onboarding process can take as long as 12 weeks. (will open in a new window) In today’s digital age, where 70 percent of banking takes place digitally, (will open in a new window) consumers are just not willing to wait that long. Financial institutions too slow in engaging effectively with new customers are losing them to more nimble competitors.

For customer targeting and prediction modeling, quantum computing could be a game changer. The data modeling capabilities of quantum computers are expected to prove superior in finding patterns, performing classifications, and making predictions that are not possible today because of the challenges of complex data structures.

Trading optimization

Complexity in financial markets trading activity is skyrocketing. For example, the valuation adjustments model for derivatives, the XVA umbrella, (will open in a new window) has greatly increased in complexity, now including credit (CVA), debit (DVA), funding (FVA), capital (KVA) and margin (MVA). Due to greater transparency requirements from regulations, stricter validation processes are applied to trading, impacting risk-management calculations (will open in a new window) that need to align counterparty credit exposures with creditlimit utilization of derivatives portfolios. Furthermore, significant investment frameworks and vehicles have changed. For example, bond exchange traded funds (ETFs) are projected to reach USD 2 trillion (will open in a new window) by 2024, and environmental, social, and government (ESG) investments are gaining traction, with USD 35 trillion (will open in a new window) invested in this asset taxonomy in 2019.

In this complicated trading landscape, investment managers struggle to incorporate real-life constraints, such as market volatility and customer life-event changes, into portfolio optimization. Ideally, money managers would like to simulate large numbers of potential scenarios and investment options to validate sensitivities when estimating expected returns. Currently, rebalancing investment portfolios that keep up with market movements is significantly constrained by computational limitations and transaction costs.

Quantum technology could help cut through the complexity of today’s trading environments. Quantum computing’s combinatorial optimization capabilities may enable investment managers to improve portfolio diversification, rebalance portfolio investments to more precisely respond to market conditions and investor goals, and more cost-effectively streamline trading settlement processes.

Risk profiling

Financial services institutions are under increasing pressure to balance risk, hedge positions more effectively, and perform a wider range of stress tests to comply with regulatory requirements. Liquidity management, derivatives pricing, and risk measurement can be complex and calculations difficult to perform, making it hard to properly manage the costs of risk on trades. Today, Monte Carlo simulations—the preferred technique to analyze the impact of risk and uncertainty in financial models—are limited by the scaling of the estimation error.

Looking forward, we expect continual waves of overlapping amendments to regulations, directives, and standards, such as Basel III and its revisions. (will open in a new window) They will require a much larger array of risk-management stress scenarios. As a result, compliance costs are expected to more than double (will open in a new window) in the coming years, including regulatory penalties (will open in a new window) and remediation in cases of non-compliance.

In the face of more sophisticated risk-profiling demands and rising regulatory hurdles, the data-processing capabilities of quantum computers may speed up risk scenario simulations with higher precision, while testing more outcomes.

Benefits of the Quantum Era

Quantum computing’s business value for financial services institutions result from four main scenarios:

— Enhancing investment gains

— Reducing capital requirements

— Opening new investment opportunities

— Improving the identification and management of risk and compliance