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Tech Magazine 2017

Finance: When Expert Analysis & Systems Remain Key

Artificial Intelligence (AI) is one of the most captivating, exciting, and debate-provoking areas in computer science today. Despite the plethora of news coverage, AI also remains one of the least understood areas of technology.

To truly understand the value of AI tools in concrete terms, we need to cut through clever marketing, buzzwords, and misinformation. This article will look at tangible use cases in finance where AI tools are deployed today and offer a strong return on investment.

Natural Language Generation: Democratizing Data

Natural Language Generation (NLG), a subsection of Artificial Intelligence, is considered one of the hottest fields of AI in 2017 by Forrester(1). NLG is an expert system that turns structured data into written narrative in real time. People do not speak data, but instead English or their native language. And they generally prefer to read the analysis and insights from the data instead of trying to analyse it on their own. They just do not have the time to dig down into every spreadsheet or dataset. The problem is that companies cannot afford to hire enough people to write the explanations and reports from the data. That is where Artificial Intelligence and NLG technologies come into play. On Wall Street today, financial companies are using Natural Language Generation(2) tools to automatically write reports that comment on, summarise, and compare data. For example, FactSet described their work with Yseop as an alternative to costly outsourcing by saying that they “generate [today] a million business descriptions (…)”(3).

(1) Forbes Magazine, Jan. 23, 2017 Top 10 Hot Artificial Intelligence (AI) Technologies. https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/

(2) https://compose.yseop.com

(3) Forbes Magazine, Nov. 2, 2016 FactSet Uses Precisely Structured Data And Yseop Software To Write Corporate Descriptions. www.forbes.com/sites/tomgroenfeldt/2016/11/02/factset-uses-precisely-structured-data-and-yseop-software-to-write-corporate-descriptions/

Democratizing & Expanding Investment

In financial services today, there is no shortage of data, but there is a shortage of expert analysis. For example, if you want to understand the performance of a marquee fund or stock, there is a plethora of analysis, but what if you want the same level of analysis for second-tier funds or stocks? This is a problem faced by both small investors and major firms alike, as they diversify their investment portfolios to incorporate smaller or niche funds or stocks. Now managers and investors can make informed decisions about smaller funds and stocks the same way they do about the larger ones.

Expert Systems Remain Key in Finance: why Machine Learning isn’t a Panacea

While Machine Learning dominates the news media, it fails in many finance use cases because of one key element: its statistic-based approach. Fundamentally, machine learning is based on a statistic approach and works by automating average behaviours. Business users are left in the dark about why the systems are advising what they are, and this has an impact on adoption rates by front-line teams. Average levels of product knowledge, average levels of service behaviours, and average levels of compliance to regulations is not what financial companies want and need in a competitive and regulated environment. While not as much of a buzzword, expert systems continue to dominate the majority of use cases that we see in finance. An expert system is a piece of software that automates a task normally done by humans (like writing data-driven reports). It does this by applying a preapproved series of business rules, compliance rules, and best practices. The end result is a system that isn’t able to exceed its programming, and if it encounters a scenario it doesn’t know, then it doesn’t take action.

Despite the overinflated marketing, AI tools are quietly but fundamentally changing the finance industry. The key is to separate fact from fiction and to approach the market with a clear business problem. The NLG market is the area I know the best and I can tell you that the adoption rates in finance are incredibly high. This is due to the ease of deployment, quick ROI, and because the technology not only cuts costs but it expands capacity. This last point is the most important and is true when businesses successfully adopt any AI technology. The goal should be to enhance your team capacity and to automate repetitive tasks, freeing up their time and improving their work.

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