The role of AI in FinTech as well as of stock prediction software in finance is huge, still training a neural network is challenging and exhausting since there are a lot of parameters involved. Engineers need to know precisely which algorithms and optimization methods work best for financial predictions in general and for time series or alternative data prediction in particular.
Selecting the proper input parameters, deploying the network, adjusting it to ever-changing conditions, using several networks at once, combining networks with the general classical trading approach these are all tasks for deep learning professionals.
Despite that, meaningful applications of machine learning in particular are already common. We see machine learning having an impact in everything from how recruiters parse stacks of resumes to how businesses analyze subtle trends in customer behavior; from improving user experience with everything from how web pages are served and products are recommended to intelligent chat features. And developments go far beyond business. Deep learning techniques produced a recent breakthrough in protein folding, which has applications in developing effective medical treatments, using enzymes to break down industrial waste, and more. It represents a considerable advance in AI development.
As we see machine learning adopted by more organizations, for more purposes, there are three innovations that I am keeping an eye out for in the near future:
- ML models that continually learn — with minimal supervision. The current pattern of humans building and deploying models simply does not scale. Models that can learn with fewer human-provided labels and more unstructured data may not appear in 2021, but we’ll see them in the next few years.
- Advances in adversarial learning and explainability. AI/ML is only as good as the data it learns from, and it’s possible to poison a data set. You can foil the image recognition systems that guide driverless cars. You can certainly turn a chatbot racist. Part of better governance is understanding why the model is behaving in the way it does. That explainability, coupled with solutions to withstand bad data or sabotage, will be a significant development.
- Ethical AI strategies. In the next few years, there will be further development of frameworks and practices for preventing bias in the algorithms that increasingly affect our daily lives. Part of the solution is ethics training, as is bringing in outside experts to consider the effects of an algorithm. For instance, if you’re going to use machine learning to help your bank determine who qualifies for a mortgage, consult with economists, urban planners and experts in the biases that have been seen in the real estate market and society in general. A parallel strategy: slowing down to consider the effects of our algorithms. Silicon Valley’s mantra of “move fast and break things” should apply to how software functions, not society.
Sometimes, markets move in mysterious ways under the influence of the economy, politics, news events, and human judgment. The ground is always shifting, and any tool that offers an edge on competitors is priceless. Big players in the FinTech market are investing significant funds in AI technologies, which have become the next-generation tools for detecting subtle patterns that are invisible to other technical analysis methods.
The extensive use of AI for financial market predictions will require asset managers to catch up; otherwise, they won’t be able to compete with the united power of human and artificial intelligence.
