Back in the early days of data science, before it was even called data science, any financial applications handled by programs were called Expert Systems. These were a domain of AI that was developed using the knowledge of a “Human Expert.” The expert’s knowledge was used to create a set of programming rules to assist the algorithm with making decisions.
At its most basic level, an Expert System would look like this:
If the price of asset “A” when compared to asset “B” exceeds X%, then sell asset A (or buy asset B or do both), or:
If a prospective borrower has a credit score below 591, do not lend them anything.
Such expert systems have been successfully used in fraud detection, medical diagnosis, and even when prospecting for minerals. However, there is a major limitation to them, which is that they require full information to be provided to them as an input and this fact means that they will either perform poorly or not at all with uncertainty.
Financial applications primarily deal with the prediction of future events based on the results of past data. This is the reason that Artificial Neural Networks have become so popular in recent times, especially in the finance industry, because they have a better ability to handle uncertainty when compared to expert systems. When we consider various scenarios that involve predictions, we find a few primary areas enhanced by using artificial neural networks (ANNs):
1. Predicting the movement of the stock market, both indexes, and individual stocks
2. Predicting loan application underwriting and repayment success
3. Finding suitable credit card clients
In this article, we will explain the basics of artificial neural networks and go deeper into the applications where artificial neural networks can be the most successful and beneficial for the financial, banking, and insurance industries. Finally, we will finish with an example outline of an ANN for making credit decisions.
Read more here.