Artificial Intelligence (AI) has been a hot topic for several years. With an increasing amount of data, the need to understand the patterns within this data is growing. Everyone wants to get involved, but a lot of companies have limited understanding of what AI is, how it differs or overlaps with Machine Learning (ML) and Deep Learning, and what its applications are. This blog aims to provide a clear understanding of the concept of AI, as well as its use cases in the Financial Sector.
What is Artificial Intelligence?
As the name suggests, AI encompasses anything that can be classified as artificial and intelligent. This means anything we consider intelligent, and that is exhibited by a computer, robot, or other machine. This leads to the more philosophical and psychological question of what intelligence is. Could we, for example, classify a washing machine as AI since it is a machine and does a task that humans can also perform?
AI encompasses anything that can be classified as artificial and intelligent.
We could dedicate a whole blog to this question alone, but for the sake of simplicity, for now we will consider any form of human cognition as intelligence. This refers to things that are naturally and automatically done by humans, like thinking, knowing, remembering, judging, and problem-solving. Therefore, given that a washing machine needs to be told what to do beforehand, we can conclude that a washing machine is not intelligent.
What is Machine Learning?
AI encompasses several different forms of intelligence, but one that has been studied extensively within AI is learning, or the ‘process of acquiring new knowledge’. This is known within the community as ML, which is a branch of AI that uses data and algorithms to develop machines that imitate the way that humans learn.
One of the most widely used ML algorithms is known as Neural Networks, which are directly based on the biological system of intelligence within our brains. Just like human neurons, these artificial neurons receive an input, and if the input is strong enough, they send an output. The strength of a connection in human neurons is increased by the number of neurotransmitters. If the connection is made more often, more neurotransmitters are formed. For the artificial neurons in Neural Networks, this is translated as a certain weight that is increased or decreased based on the importance of the connection. When we talk about Deep Learning, this refers to a Neural Network with many ‘layers’ of neurons, often making the predictions more accurate but also harder to trace back, increasing the complexity of these systems.
So, how do these Neural Networks learn? To keep things simple, let’s imagine a child learning about different animals who might see a dog and refer to it as cat. When an adult corrects them, the connections between the neurons that lead to their original conclusion will weaken. If the child makes a correct classification, this connection strengthens. This is the same process that occurs within ML, only in the machine world the data available is larger, the learning speed is faster, and the problem is usually much more complicated than a simple binary classification problem like the one described.
How can Artificial Intelligence and Machine Learning be applied in the Financial Sector?
The volume of data held within each financial services organisation is growing by the day; just think of all the complex data on customers, transactions, insurances, stocks, and more. Both AI and ML can be leveraged to make the most out of this data and enhance business processes in a variety of ways, which are outlined below.
- Risk Assessment, Prediction and Management – in these examples, ML algorithms are used as an effective solution for handling large amounts of historical data and solving classification problems.
- Fraud Detection and Prevention – in more sensitive classifications, like fraud detection, it is important to note that an ML algorithm only raises a ‘red flag’ when a suspicious transaction occurs, and the final decision is often still left to the interpretation of a human.
- Automated Financial Advice and Insurance Claims – these examples make use of a weaker system within AI called a Knowledge System, or expert system. The idea is that it holds all possible knowledge in a specific area and can therefore make the most educated decisions. As such, a system like this in theory is not learning anything new, e.g. if new regulations arise it will not automatically update, so is classed as AI but not ML.
- Digitalising or Summarising Paper Records – digitalising paper records can be performed by handwriting recognition or optical character recognition algorithms. These algorithms need to learn to recognise handwriting and characters and therefore fall within the ML scope.
Most of the techniques mentioned above could also just be performed by a human, so what is the added benefit of bringing AI and ML into play? Simply put, a machine can execute these processes more accurately, more precisely, and more quickly than any human could. There are no human errors, no need to account for availability, and decisions are not based on emotions or caution.
Simply put, a machine can execute these processes more accurately, more precisely, and more quickly than any human could.
AutoML – lending a helping hand
The Financial Sector has a growing need for AI talent. Due to rapid global digitalisation, customers now often find it more convenient to take care of their finances online rather than physically visiting a bank, so firms will fall behind if they do not adopt AI.
Unfortunately, skilled AI workers are hard to find, but AutoML tools like DataIku provide methods and processes to help make ML more accessible to those with limited knowledge in this area. With a user-friendly web interface, these tools automate the time-consuming, iterative tasks of machine learning and simplify the process of building an entire data flow. The flow produced can be used in a variety of projects, which enhances the adaptability and efficiency of the process. Not only this, but AutoML is also able to select the best performing model and algorithm to suit the specific situation, hence providing a simple and effective solution for incorporating ML into a business without the need for a highly skilled specialist.
If you would like to know more about Artificial Intelligence and how it can be applied within your organisation, get in touch with our team of experts here.
About Projective Group
Established in 2006, Projective Group is a leading Financial Services change specialist.
We are recognised within the industry as a complete solutions provider, partnering with clients in Financial Services to provide resolutions that are both holistic and pragmatic. We have evolved to become a trusted partner for companies that want to thrive and prosper in an ever-changing Financial Services landscape.