The past few weeks have been eventful for AI, as many researchers around the world have come together to sign an open letter to suspend AI experiments they claim could pose serious risks to humanity. While some consider these arguments overblown, it is clear that AI and machine learning in general present interesting opportunities to manage complex challenges in computing.
Rich data is the driving force behind machine learning models. The financial industry is generally data intensive, and apart from the tsunami of ideas and opinions coming from social media networks, the number of data sources is increasing by the day. It’s important to digest this data to provide timely insights to decision makers. The industry is also highly regulated, with an emphasis on compliance and very well defined processes. These aspects provide fertile soil for intelligent models to work.
Many software systems are already in use, but administrators still struggle with lack of application assistance. The need for AI to act like a smart assistant that can access rich, pre-processed data, understand the needs of decision makers, and make timely recommendations is now in demand. We need a system that can come up with smart suggestions, and ask the system questions to check the rationale behind these suggestions the same way an executive would do to his assistant. must be able to This helps the model learn and also helps the manager validate the suggestions presented by the system. This facilitates bi-directional learning where humans can learn from the system and models can learn based on human feedback.
We live in a highly interconnected world, where shocks travel instantly from one region to another. Unlike before, data from companies is analyzed by several agencies, both formal and informal influencers, and these are broadcast in real time. To run, you need a robust model.
When it comes to investor risk management, current systems are designed to assess investor risk at the beginning of an investment journey, and risk is assessed on a regular basis. The environment is highly dynamic, requiring continuous analysis of portfolio and investor situations to maximize opportunities and make timely decisions. Machine learning models can be very helpful here as they can analyze not only our experience but also our experience in similar scenarios and guide investors every step of the way. Again, the model can continuously assess risk based on performance, market dynamics, and user preferences. Models can likewise prompt users at critical times to ensure they avoid costly mistakes. Systems can likewise come up with intelligent suggestions to manage and mitigate risk in more innovative ways.
Chat bots are effective at interacting with users in natural language in multi-turn conversations, but they are used more in the direction of answering questions. You need a system that allows you to ask intelligent, leading questions that help users learn. Users can get stuck in their own thoughts, and models can play an eye-opening role by asking and answering questions. AI assistants can help you win the trust of your master, manage mundane tasks, avoid common mistakes in execution, point out interesting opportunities, and deliver extraordinary results.
Some say that AI can kill humanity, but the reality is that the ATM has to answer every time whether the account is a savings account or a checking account. Tech companies have always been ahead of society with their visions, but we now see that the general public has some ideas and tech companies are lagging behind in execution, pausing research. We are in a unique moment as we are lobbying to do so.
We see a lot of data and spend a lot of time on our devices. Even if AI could help manage just a fraction of this complexity, it would produce amazing results. It’s time to look for incremental improvements before dreaming of radical changes that can solve humanity’s problems.
(This article was written by Jayaram Srinivasan, CTO of moolaah.com and the views expressed in this article are his own)
