Swedish philosopher Nick Bostrom, in the book Superintelligence said, "Machine learning is the last invention that humanity will ever need to make." From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century. AI has made its presence felt in all walks of life due to its ability to help the user innovate. It has also enabled users to make faster and more informed decisions with an increased amount of efficiency. Of late, the banking sector is becoming an active adapter of artificial intelligence--exploring and implementing this technology in new ways.
Parts of the financial sector, notably wealth management and equity trading, have been among the first movers in the commercial use of AI. The scope of AI-led innovation is now widening: banks and insurers are actively applying AI techniques in the front and back office, developing innovative customer-facing services and automating operations such as payments, risk modelling and fraud detection. Technology companies--notably small fintechs--have provided the impetus for much of the AI-led innovation. Some established financial sector players have responded energetically themselves, co-opting fintechs' AI expertise and innovations. But Andrei Kirilenko of Imperial College London maintains that the variety of services traditional banks and insurers offered remains limited.
General purpose technology is a term economists reserve for technologies that spur protracted economic growth and societal advancements, revolutionizing the operations of households and corporations alike. A sample general purpose technology is electricity. Electricity spawned a multitude of products and sectors, including refrigerators, washing machines, trains and, of course, computers. The advent of electricity radically transformed the world. A recent Harvard Business Review article designates artificial intelligence (AI) as the most important general purpose technology of our era. A car that can parallel park itself. Devices that respond with tomorrow's weather when we ask.
The pace at which companies are investing in artificial intelligence (AI) continues to gain momentum and the financial sector is not immune to this trend. According to research by global management consultancy Accenture, banks that invest in AI and human-machine collaboration tools could boost their revenue by over a third (34 per cent) by 2022. AI is considered one of the most important disruptive technologies for today's banks, with a recent PwC survey revealing that 72 per cent of senior management see AI and machine learning (ML) as key sources of competitive advantage. The survey also highlighted that 52 per cent of companies in the financial services sector are already making substantial commitments to AI, with 66 per cent projecting significant investments by 2020. The finance sector has been using AI in very specific areas for some time, but we're now seeing a rapid growth in take-up due to increasing market competition, the need to reduce overheads and the benefits of harnessing increasing volumes of data.
Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions.