Source: Medium | January 10, 2019
Author: Swathi Young
These days, there’s so much noise about AI. And in the midst of all these, the importance of AI and how it has become a key feature in most industries can easily get buried. Yes, it makes appearances in the news almost every other day. But what are its uses in an industry as sensitive as the financial sector?
It’s unsafe to assume everyone really knows what AI is. So a quick definition is necessary at this point.
There are many definitions you will find online. One particular one describes Artificial Intelligence as a branch of computer science that aims to create intelligent machines. In other words, AI is software/algorithms that can perform tasks that are smart and perform tasks that require little human intervention. Machine learning is a subset of AI and is the most common way of solving problems using AI. Machine learning uses vast quantities of data and creates algorithms that can be used for prediction, optimization or categorization challenges.
2018 saw an increase in practical application of AI in various industries and one of them is the financial sector. There are a host of applications — market and customer analysis, credit scoring, usage-based insurance, data-driven trading, fraud detection and beyond. Various sub-sectors like banks, insurance companies, credit card and payment processing companies, asset and wealth management firms, lenders etc. led major investments in AI in the financial services that accounted to nearly $9 Billion in 2018 alone and is expected to grow at a CAGR of approximately 17% over the next three years.
Drivers of adoption of AI and machine learning in financial services:
There are a wide range of factors that have contributed to the growing use of AI and machine learning in financial sector. Some of them are A. faster processor speeds, B. lower hardware costs and C. easy access to computing (on the cloud). This means an increase in the availability of infrastructure both to analyze data as well to extract insights and develop modelling capabilities. The other factors are availability of AI and machine learning tools. Yet another factor is the proliferation of data in digital formats from multiple sources such as online search trends, viewership patterns and social media that contain financial information about markets and consumers.