Being in the most disruptive period ever is the investment management history. But a new wave of disruptive technologies being introduced, the concept of investing is being transformed from a practice of self-driven only accessed by the affluent few to a democratized audience. This served a much broader customer base. And now the demand for digitized, convenient financial services has soared. But the opportunities to participate in exciting investment opportunities can be achieved with just a few taps of a smartphone. And this game changing technologies is nothing but Artificial intelligence. Having the capacity to transform the most and to improve the investing process. AI mostly refers to using the computers to simulate intelligent behavior that which is comparable or even superior, to that of human mind. And when one looks around, it’s clear that those computers have not only arrived but are now changing the world in dramatic ways. Be it through robotics performing life changing medical and surgical procedures, or AI powered chat bots giving out easy and instant solutions or the self-driving cars which are minimizing the road accidents and the list goes on. AI has been beneficially improving the quality of our day to day lives. And the same goes with investing too. Implementation of AI in this sector to make faster, smarter and more profitable investing decisions. “Investing is the ultimate numbers game, and smart number crunchers tend to be good at it. So, artificial intelligence as a high-capacity data processor stands a good chance at revolutionizing the investment industry.” expressed Daniel Seiler, head of the Multi Asset Boutique at Vontobel Asset Management.
An application of AI, ML involves utilizing data to learn, adapt and improve investment decisions without needing to be explicitly programmed to do so. Machine learning ( ML ) is probably the most exciting aspect of this revolution. Formulating various algorithms, exposing them to substantial volumes of relevant data such as historic market prices and transactional data ML systems can be trained to quickly identify security mispricing and market inefficiencies. But cultivating this automated environment is easier said than done. Even though the learning process can be slightly difficult but the techniques can be successful in predicting future market opportunities. ML is now beneficial in accessing the actionable real-time information from often voluminous amounts of data to better inform investors of the appropriate decisions to take.
In September 2019 a report by the CFA (Chartered Financial Analyst) Institute, which examined the trends and use cases of AI and big-data technologies in investments, included a survey designed to understand the state of adoption of different technologies in the workflows of analysts, portfolio managers and private-wealth managers. Few investment professionals are currently using programs typically utilized in ML techniques, including coding languages such as Python, R, and MATLAB and that most portfolio managers continue to rely on Excel (indicated by 95 percent of portfolio manager respondents) and desktop market data tools (three-quarters of portfolio manager respondents) for their investment strategy and processes. Sadly only 10 percent of portfolio-manager respondents were recorded to “have used AI/ML techniques in the past 12 months, and the number of respondents using linear regression in investment strategy and process outnumbers those using AI/ML techniques by almost five to one.
“Just as smartphones went from novelty to necessity over the last decade, voice interfaces like Amazon Alexa are quickly becoming more pervasive as people grow more and more comfortable using them,” Sunayna Tuteja, head of strategic partnerships and emerging technologies at TD Ameritrade, stated upon Alexa’s introduction in October 2018.
Combining technologies like artificial intelligence (AI), machine learning and voice user interface can be the smartest solution for these fintechs! AI can offer an almost entirely new perspective on the investing process.
