Fintech offers many exciting techniques and approaches to training machine learning models. Such applications as modeling borrowers, anticipating future cash flows, and allocating surplusses to optimized investment avenues require broad behavioral data sets. For example, some lenders mine data collected from smartphone apps, including social media activities and natural language analytics. Additionally, algorithms for high-frequency trading (HFT) are an area that can help build systems that require high reliability and real-time response to address real-time client needs. It is also worth noting that writing efficient code in an unmanaged memory setting is not in focus in data analytics recently (with a few notable exceptions).
https://scholarship.law.vanderbilt.edu/faculty-publications/1084/
#HFT #fintech #highperformacecode #optimization #behavioralmodeling
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