Finance and Computing Function
Through the Inside of AI
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.
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.
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.
One area in which AI will make a large impact is the nascent field of Regulation Technology (RegTech). There are different ways that companies will use AI to keep in line with increasing regulation. One way is by using AI to develop complex algorithm-based programs capable of detecting non-compliant behaviour of employees before it becomes too much of a problem.
Another use-case is for auditing and the documentation to be sent to authorities – AI can trawl through vast quantities of data and identify irregularities that a human would miss. And there is also the use of AI for smart psychological profiling of employees to evaluate their risk-aversion, among other traits.
Insurance is similarly quantitative, which of course makes it a fruitful area for AI. The industry suffers from the lengthy but necessary steps involved in fraud prevention and risk calculation, both of which can be alleviated by smart programs. Even simple efficiency gains in sorting between which claims are likely to be fraudulent vs which should be fast-tracked will enable a significant amount of cost savings.
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