- There are industries where the prediction of an individual’s behavior plays a key role (e.g. credit and insurance).
- There is now an abundance of digital data which can be analyzed, uncovering new insights to predict behaviors.
- Artificial Intelligence is able to analyze these sets of big data in an automated, cheap, and efficient way.
- Due to these new behavioral insights, there is a lot of room for improvement in these industries.
- Privacy concerns based on this data remain in a gray area and could pose problems.
- Hackers and cyber-security firms race to create the most sophisticated AI algorithms.
Basics There has been an influx of startups entering the credit industry, leveraging AI tech to offer more efficient and personalized offerings. But how exactly does AI work in order to do this? There are many aspects to AI. We’ll cover the 3 basic concepts: Machine Learning, Deep Learning, and Neural Networks.
Machine Learning (ML)
In the simplest terms, Machine Learning is about teaching computers to learn from data and make intelligent decisions or act based upon what they’ve learned. This is done by supplying them with a large amount of data, a set goal and indicators on how to achieve that goal. Let’s take facial recognition as an example. You could teach a computer to learn what an image of a human face is, by providing large data sets of pictures with and without human faces, then by labelling each picture ‘face’ and ‘no face’, the algorithm will be able to learn what a face looks like. With each trial, it will adapt and improve its strategy for identifying a face. Once it has learned this, you can give it a new set of pictures so that it can predict whether any contain faces or not. If a mistake is made, a human can intervene and correct it — whereas deep learning is when the computer will correct itself by analyzing the predictions against a vast amount of other pictures with faces.
Deep Learning is applying the skills learned from ML in order to make improvements on new data sets. Therefore, when the computer is predicting if the picture has a face, it can then reinforce its decision or reduce its uncertainty via Deep Learning. The Deep Learning algorithm performs a task repeatedly, each time tweaking it a little to improve the outcome. It does this by continuously assessing data via layers of artificial Neural Networks that mimic the decision-making processes in our human brains. In effect, the computer is correcting and improving its strategy. Deep Learning requires big data in order to work, a low supply of data will not be sufficient.
An artificial Neural Network tries to simulate the processes of densely interconnected brain cells. Instead of being built from biology, these neurons, or nodes, are built from code. They are designed to recognize patterns, this is done via three basic layers in the Neural Network; input, hidden and output (there can also be multiple layers for each). Each of these layers contains thousands or millions of nodes. The patterns that are recognized from the data are numerical. All bits of data — whether it is sound, image, text or time related — are converted into numerical vectors so that the algorithm can read it. Neural Networks cluster and classify the inputted data as they spot these numerical correlations. By grouping unlabeled data based upon similarities which human analytics may not have considered, new insights can be uncovered.
The typical way in which the credit industry issues loans is via credit scores. Credit reference agencies gather information such as your repayment history, bills, mortgage and address. Then based on this info, you’re given a credit score. This is the score which banks and credit providers base their decision-making on. However, this doesn’t show the whole picture of an individual. Furthermore, human bias can play a role in this process.
Stay tuned for part 2 next week…