In brief

As developments in artificial intelligence continue to benefit our wider global society, there are also opportunities for the finance industry to improve efficiency, create deeper client relationships, and better protect the stability of the sector.

What is AI?

Though there is much debate in academia around the definition of Artificial Intelligence (AI), there is no question that advances in computing power have led to AI becoming increasingly influential on our day-to-day lives.

Historically, the term AI has been variously attributed to a range of reactions and conclusions, including being originally ascribed when a machine displayed an ability to perform certain functions or tasks as well as or better than humans. As the field has developed, definitions and expectations of AI have shifted, judging not only a machine’s ability to achieve a desired goal or outcome, but also its ability to learn and improve its capability as a result of its own experiences and the learnings from them.

In this video we speak to local experts including Digital Jersey as well as several Member firms on how they define the term and how it is helping their business to create efficiencies and why Jersey has all the components for artificial intelligence industry to thrive here.

They discuss how AI implementations in financial institutions, such as machine learning and Robotic Process Automation (RPA) are being applied in cybersecurity, analytics, fraud detection, decision-making, and customer service.

Watch our video to learn more.

Video: Artificial Intelligence: An Open World | Est time to watch: 6m | Sound: Yes | Mute all videos

Special Report:

Artificial Intelligence: An Introduction

We discover how the use of Artificial Intelligence in financial services is turning science fiction into everyday reality in our paper ‘Step Into the Forward-Thinking World of AI’. It covers:

  • a history of AI – from humanoids to harmonisation, how AI has developed from the initial robots of the 1960s through to virtual assistants, chatbots and deep-learning capabilities
  • how we can define AI – based on tasks performed, ability and intelligence, or the capability to think and feel like a human
  • why now for AI? – looking at the factors which have led to this being an ideal time for AI to thrive such as greater automation, a continual increase in computational power, and advances in machine learning
  • how can the finance industry take advantage of AI – discussing automation and computation applications and a number of existing and potential uses of AI in the finance industry, along with the challenges faced by firms looking to take advantage of AI technology
  • benefits of Robotic Process Automation (RPA) and, beyond automation, how machine learning presents significant opportunities for the finance industry to improve service, productivity, and risk management
  • overcoming myths, assumptions and key challenges in order to realise the benefits of using AI-based solutions

In producing this publication, Jersey Finance has been pleased to work with the next generation advisory committee of the Jersey Bankers’ Association (JBA), known as JBA 2.0.

Read the report

Artificial Intelligence: Revolutionising Back-Office Productivity

Many of the technology innovations seen in financial services today do one (or more) of three things: they mitigate risk, they create greater efficiencies and they reduce costs. This is equally true when it comes to AI. In this report, we look at the quantitative elements of operational efficiencies and regulatory opportunities for financial services firms by exploring the potential for AI in back and middle-office functions.

Briefly it covers:

  • the potential cost savings from AI applications and comparisons between front, middle and back offices
  • a comparison of the maturity of AI applications across the financial services industry
  • how Intelligent Automation is streamlining financial services and how we can start to identify some of the ideal processes to automate in compliance and fraud detection, reconciliations and validations, and data entry and capture across multiple systems
  • how AI can play a part in forward-thinking regtech solutions and offer vital protection against cybercrime
  • examples of AI being used by financial services firms in Jersey

In producing this publication, Jersey Finance has been pleased to work with Sionic, a global consulting firm specialising in financial services with a longstanding presence here in Jersey.

Read the report

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Glossary of Key AI Terms

Artificial general intelligence (often shortened to general intelligence)

The ability to accomplish virtually any goal or cognitive task including learning equivalent to human intelligence without input

Artificial intelligence

Non-biological intelligence

Backpropagation

The algorithms that enable artificial neural networks to learn, through a process of incrementally reducing the error between known outcomes and model predictions during training cycles

Deep learning

A concept loosely based on the brain that recognises patterns in data to gain insight beyond the ability of humans; for example, to distinguish between the sonar acoustic profiles of submarines, mines and other sea life, a deep learning system doesn’t require human programming to tell it what a certain profile is, but it does need large amounts of data

Deep neural network

Uses sophisticated mathematical modelling to process data in complex ways, through a greater number of layers than a neural network

Generative models

Existing data is used to generate new information; for example, predictive text looks at past data to predict the next word in a sequence

Intelligence

The ability to achieve complex goals

Narrow intelligence

The ability to achieve a narrow set of goals, such as playing chess

Natural language processing (NLP)

When a computer interprets and understands human language and the way and context in which it’s spoken or written; the aim is to deliver more human-like outputs or responses

Neural network

A group of interconnected ‘neurons’ that have the ability to influence each other’s behaviour

Machine learning

The ability of a machine to learn without being programmed; the algorithms used improve through experience, either predictively using historic data or generatively using new data

Predictive analytics and models

Similar to machine learning but narrower in scope, predictive analytics has a very specific purpose, which is to use historical data to predict the likelihood of a future outcome; for example, risk-based models on when a stock may fall

Reinforcement learning

A type of machine learning technique that enables an AI system to learn in an interactive environment by trial and error using feedback from its own actions and experiences

Robotic process automation (RPA)

Software that’s built to automate a sequence of primarily graphical repetitive tasks

Super-intelligence

General intelligence far beyond human level

Supervised learning

You have historical data inputs (X) and outputs (Y) that can be trained to understand the relationship between the inputs and outputs; for example, using data held to assess a mortgage application Unsupervised learning You have the input data (X), and are looking for a pattern in the data