High frequency trading
Out of the many hundreds of algorithms that exist, there are a few that are specifically and maybe even exclusively relevant for the financial services sector, since they address operations related to core business processes of financial institutions. Some algorithms are able to solve large systems of coupled linear equations much faster than when using classical techniques. Generally if a classical computer requires N calculations to arrive at a solution, quantum algorithms would require logarithmic-ally fewer calculations to achieve the same result. This makes them particularly useful for image processing, video processing, signal processing, robot control, weather modelling, genetic analysis, and population analysis. One particularly interesting application for banking is algorithmic trading. This uses algorithms to automatically initiate stock trades according to pre-defined strategies. Becoming proficient in running these algorithms for high-frequency trading can offer a significant advantage over those without such a capability.
Fraud detection is most often reliant on pattern recognition — this is done expertly via neural networks and machine learning. Machine learning is a discipline that is rapidly developing and is being invested in heavily by Google
and Microsoft. The goal of machine learning algorithms is to dramatically accelerate the learning rate of artificial neural networks — using classical techniques it is very difficult to train a neural network in big-data applications.
Particularly in the complicated mathematical world of the banking and insurance sectors, having fast learning neural networks will provide levels of insight and understanding which were previously inconceivable. Pattern
recognition algorithms can be effectively used to spot fraudulent activities, automated attacks on clients and reduce data breaches. Additionally, patterns in other complicated forms of attacks can be more effectively detected than is humanly possible.
Development of Algorithms for the financial institutions
Because fully functional quantum computers are not yet available in the coming 5 years, the development of algorithms is often overlooked. This is actually rather strange since even without quantum computing capability, algorithms can offer significant advantages for many IT processes. Hiring a few mathematicians and letting them work on algorithm development is a relatively cheap investment that can have very significant business benefit. However, good mathematicians with the necessary skills and backgrounds are generally hard to come by, leading forward thinking companies to hire teams of general mathematicians in order to train them on the job.