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Quantum Computing for Financial Services: Improving Efficiencies and Preventing Fraud

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"Quantum Computing for Financial Services: Improving Efficiencies and Preventing Fraud"

Should you be benefiting from emerging quantum computing solutions, which can support use cases including targeting and prediction, trading optimization and risk profiling?

Practical quantum computing is often quoted as being ten years away, but financial services is one of the sectors already demonstrating benefits from its early adoption.

Quantum computing use cases for financial organizations involve solving complex problems that have too many variables for classical computers to be able to return a result on a realistic timescale. These include making accurate predictions of markets, predicting trading signals in financial markets, calculating credit-decision outcomes, portfolio optimization, risk mitigation and identifying fraudulent activity.

Current quantum computers have a limited number of qubits – a measure of quantum computing power – are unwieldy and expensive, and existing code libraries are limited. Other barriers to adoption include a skills gap, poor return on investment and a limited supporting ecosystem.

To get around this, most businesses access quantum computing resources from major quantum computing suppliers via the cloud and work in partnership with vendors to develop algorithms. There are four main types of quantum processors. Of these, trapped ion technology has been demonstrated in risk management scenarios, and annealing quantum computers for simulation, fraud management and customer loyalty use cases, among others. However, it is unclear which technology will prove best across the board for financial services in the long term!

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