From algorithmic trading to customer retention, big players in finance sector are using AI to gain competitive advantage. AI can bring value across automated portfolio management, products recommendations, risk assessment, fraud detection, image recognition, and much more.
The term artificial intelligence was coined in 1955 by John McCarthy, a math professor at Dartmouth. Due to its evocative name, this field has produced a wide array of hype and claims. Nonetheless, data science is becoming increasingly recognized as the motive power steering the leading industries to the future.
Faster processor speeds, lower hardware costs, and better access to computing power have given rise to a growing number of FinTech companies. There has also been a rapid growth of high quality datasets for learning and prediction owing to increased digitisation and the adoption of web-based services.
In the highly competitive financial sector, artificial intelligence is at a rapidly evolving phase, with new use cases and algorithms uncovered in a matter of days rather than years. The availability of AI-powered systems lies heavily on the existing data and infrastructure, and the fundamental demands of financial regulation.
A recent study pointed out that the rise of data science in the finance sector is driven by five key factors: the general advancement of technology, factors particular to the financial sector, potential for increased profitability, competition on the market, and regulatory compliance.
Machine learning can help companies to reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Intelligent algorithms are able to spot anomalies and fraudulent information in a matter of seconds.
AI use cases holding most value to the financial industry include: