The Impact of Quantum Computing in Financial Services

Quantum computing is currently leading in swiftness and efficiency. Full-scale application of the technology is yet to take shape, but significant steps are being made toward its adoption. Banks and other institutions already predict it will tremendously transform the finance industry. Finance services, including pricing securities and optimization of portfolios, need the capability to evaluate a range of outcomes. To effect this, banks employ algorithms and models to calculate statistical probabilities. To do this, banks use algorithms and models that calculate statistical probabilities. These methods are effective but may fail, as evidenced in the 2008 financial crisis when low-probability events ensued more often than expected.

In a world that relies heavily on data, powerful computers are required to calculate probabilities accurately. Quantum computing in banking has resulted in a new era of processors that leverage the guides of quantum physics to crunch extensive amounts of information at lightning speed. The Sycamore quantum processor, owned by Google, can process a task that would take supercomputers thousands of years to complete in approximately three minutes.

Quibits

Quantum computing is a branch of quantum physics, which shows the amazing fact that distinct properties of particles can occur in two states, or a mixture of the two states at equal times. While traditional computers use dualistic processing systems founded on 1 and 0s, quantum systems can concurrently be 1 and 0 or a combination of 1 and 0. Superposition frees processing from binary restrictions and facilitates the exploration of massive computational opportunities. Qubits form the fundamentals of how these computers operate.

Use Cases Of Quantum Computing in Financial Services

We classify them into three categories: Prediction and targeting, optimization of trades, and profiling risks. We will take a dive deeper into the use cases in each category.

Prediction and Targeting

Most customers accessing financial services want personalized services and products to anticipate their changing behavior and needs. Around 25% of both medium and small-sized financial institutions lose clients to competitors because their services don't prioritize the customer experience. It is difficult to create analytic models that can deliver behavioral change quickly and accurately. However, the evolution of quantum computing may be a game changer in creating prediction models. It is way superior in terms of; performing classification, finding patterns, and predicting events that are unimaginable today.

Optimization of Trades

The complexity involved in trading the financial markets is skyrocketing by the day. Regulation firms in the markets ensure transparency. As a result, the validation processes become stricter, impacting the calculation of risk management. In such complex environments, investment managers find it difficult to include real-life problems such as volatility, portfolio optimization, and life-event changes for the customer. 

The combinatorial optimization capabilities allow investment managers to rebalance portfolio investments, respond perfectly to market conditions, and diversify investor portfolios, among other quantum computing benefits.

Profiling of Risks

Balancing risk and managing large positions are essential to comply with regulatory requirements. The pricing of derivatives, risk management, and liquidity management can be difficult to calculate and perform, rendering it hard to manage the amount of risk per trade. Soon, we expect continuous amendments to the regulations, derivatives, and standards. This will need an array of risk management scenarios. Because of this, compliance costs will likely double going forward and introducing regulatory penalties in case of non-compliance.