This article looks at the ways in which firms across the various sectors of the economy adopt Artificial Intelligence (AI) techniques. However, before we review the sectors affected it is important to note the underlying drivers that are fuelling the growth in the influence and reach of Machine Learning across the sectors of the economy will only grow as we move forwards. This is because Big Data is only getting larger, velocity of data faster, plus the availability of cheaper data storage plus the arrival of powerful Graphical Processing Units (GPUs) to enable Deep Learning algorithms to be deployed. Furthermore, new research in areas of Deep Learning and other Machine Learning areas will continue to emerge into real world production over the next few years leading to new opportunities and applications.
The DLS team strongly believe that the advent of 5G around 2021 will be a transformative and revolutionary moment in human history. The enhanced speed of 5G over 4G will enable technologies, that struggle today with latency requirements, such as virtual reality and autonomous systems, to perform with real time efficiency.
This will be a world of intelligent Internet of Things (IoT) on the edge (meaning on the device) where the data is processed at the place where it is generated and Deep Learning models can run on the device itself rather than on a remote cloud server. This will obviate the need for an autonomous agent such as a robot or vehicle to wait receiving a response from a remote server before it can take an action.
We believe that this world will lead to the creation of new opportunities and businesses that do not exist today. AI techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Deep Reinforcement Learning and Generative Adversarial Networks (GANs) will play an important role in this world alongside traditional Machine Learning methods.
Sectors of The Economy Affected by AI
Financial Services: The use cases here include applications relating to Regulatory Technology such as leveraging face detection and verification for “Know Your Customer” from documents. This is powered by CNNs.
Financial firms can also use AI for extracting text from financial documents such as company filings and documents with personal information such as driving licences and passports (Optical Character Recognition (OCR) with LSTM).
In the investment management field, AI can be applied to passive portfolio ETF Index replication and tracking (Evolutionary Genetic Algorithms). Fraud detection is powered by CNNs and Gaussian Mixture models. In addition, Financial Services firms apply Machine Learning techniques to Credit Risk for lending and other counter party risk evaluation decisions with Linear Regression, Random Forests, Gradient Boosting and Neural Networks. Trader Oversight is implemented by using Variational Autoencoders for anomaly detection. In retail sector, there is a great deal of scope to apply AI to areas such as payments with face (using a CNN).
Insurance: AI has enormous potential in this sub-sector of Financial Services. Examples include automating the claims verification process whereby for example if the policy holder has made a claim for a car that was insured under the policy, then a CNN can used to verify that the make and model is that same as that in the insurance policy. Chatbots have been used as a virtual agent to converse with the end user. Furthermore, Variational Autoencoders can also be used for checking for the presence of outliers.
Retail Sector: This sector is one that is likely to be heavily impacted by AI and Machine Learning, both on the ecommerce and physical sides of the business. The ecommerce side generates large sets of unstructured data that can be used to generate meaningful insights that in turn enable personalisation and recommendation engines.
