WHY FINANCIAL SERVICES COMPANIES ARE PREPARING TO TAKE THE QUANTUM LEAP

Authored by Jason Hill, Reply

Financial services companies are no strangers to complex algorithms, but even today’s most sophisticated software can only analyse a fraction of available data. However, quantum computing is about to change all that. Quantum computers will far surpass the limitations of classic computers: performing complex tasks within minutes and completing actions that were once deemed impossible. So what does this mean for financial services?

 

Use cases in quantum

The potential impact of quantum computing can hardly be overstated. Just as the development of the microprocessor in the 1970s unlocked the power of personal computers to the average end user, and the proliferation of the Internet in the 1990s revolutionised the way the world communicates; quantum computing, with its vastly superior processing power, will have a transformative impact on virtually every industry and individual.

Jason Hill

In financial services, there are a myriad of applications where it can be applied including reinforcing cyber security, targeting investments, profiling risk, optimising portfolios and liquidity management, from context-defining indicators to collateral optimisation.

In the portfolio optimisation case for example, quantum computing could be used to limit a company’s exposure by identifying a portfolio of assets with minimal correlation between them. This is particularly useful when diversifying the portfolio of securities to reduce any risk that might impact return. Furthermore, as the size of an investment portfolio increases, so does the complexity of the computational problem. Quantum computing can quickly solve problems that would take days, months or even years on traditional computers.

Quantum will ultimately help financial institutions prepare for their future and get ahead of their competition by knowing more, more quickly. For example, Reply recently worked with a credit institution to develop a quantum computing algorithm that allowed it to optimise daily collateral costs related to over-the-counter derivatives trading. This took into account non-linearities in the model and involved a dedicated simulation-based optimisation tool to plan for multiple scenarios.

 

From quantum computing to predictive analytics

One particularly interesting application in quantum computing is predictive analytics which can be used to forecast future events based on past data. Quantum computing can even help users make smart assumptions about data that doesn’t exist. For example, a bank’s cash flow can be projected using the so-called the Monte Carlo method which involves getting a clear, statistical picture based on a high number of simulations. Monte Carlo simulations are a form of predictive analytics and because they require a lot of calculations (with potentially many variables), quantum can process them much faster. This is particularly useful in portfolio management as for example, it allows an analyst to determine the size of the portfolio a client would need at critical times, such as at retirement, to support their desired lifestyle.

Financial companies aren’t at a loss for historical data sources: contracts, transactions, inquiries, and claims. These are the solution to a more certain future. By learning from past knowledge companies can make future estimations with higher accuracy.

 

Where can we go from here?

The performance ability of quantum computers far outweighs current possibilities. The range of problems that can be addressed thanks to Quantum Computing is broad: it does not stop at combinatorial optimization but, instead, crosses into other areas such as machine learning and quantum security. Quantum neural networks and quantum internet networks are just two of the more interesting ones.

Quantum machine learning (QML), makes the most of the advantages of two current themes: quantum computing and machine learning. Although QML is still in its early stages, it nevertheless offers a whole new world of opportunities, combining the new knowledge provided by machine learning with the accelerated calculation potential and the enhanced accuracy of quantum calculations.

It is not a trade secret to know that today, all major financial services companies have departments focused on Big so they can benefit from the huge amount of data collected over the decades. And with the increase in remote cloud computing power, much more complex prediction models can be employed.

The race is now on for the companies that provide quantum computing solutions to fully realise their potential but once those solutions are in place, they will have a huge advantage over their competitors. It makes sense for them to partner early with companies who have existing use cases in quantum computing. Because the companies that adopt Big Data and Machine Learning processes will build more commercially efficient offerings that will have customers lining up.

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