Data: The Raw Material of Both Computing and Finance
This was the case with the advent of ERP (allowing greater oversight of a company’s finances), Big Data (most notably in the context of risk management), and recently Blockchain tech (enabling a raft of new online financial transactions hitherto impossible). The burgeoning potential of Artificial Intelligence (AI) looks set to continue this trend.
The reason for this is that the nature of finance is quantitative, and advances in computing readily lend themselves to number crunching and data analysis, more so than in other business domains. As such, any time there is a leap in the capabilities of what computers can handle, the practical use-cases in finance are never far behind this expanding frontier. Raw data is the fundamental resource of both finance and computer science – while the more eye-catching applications in AI (like autonomous military agents) are a long way from being practical, AI applications in finance will have a substantial impact in the next 1-3 years.
Before looking at how AI will change finance, it is worth reviewing where these possibilities have come from. In the past decade, major advances in computing fields (most notably machine learning) have been brought to maturation as well as made more practicable for developers. These advances revolve around bridging the gap between the clunky manner in which computers “think” (compared to humans) – self-awareness, learning, self-iteration, and the ability to process opaque data sets are the core characteristics of these new programs.
These solutions leverage advanced algorithms that in essence make programs closer to the human conception of self-aware intelligence versus the blind static input-output processing of traditional computing.
AI offers both quantitative and qualitative advances for financial services. On the more basic level, companies can reduce the cost of their workforces by automating lower-level functions (like customer service) with chatbots among other things. With computer being able to do more and more tasks that humans were required for before, this leads to direct and obvious savings.
But in terms of revenue generation, AI will also offer qualitative advances in what the finance function can achieve. For example, AI can make auditing of financial transaction much more precise, detecting errors or fraud that humans would struggle to spot. The volume of documentation involved in financial deals and research will also benefit from AI, with tools using Natural Language Processing enabling a new level of efficiency and detail in analysis.
And of course, the holy grail of fintech – a program that will pick the right stocks for you – is also on the radar, with firms such as Sentient Investment Management and Cerebellum Capital developing their own solutions that look promising in this regard.
