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Artificial Intelligence in Finance
With technological developments moving at an ever quicker pace it isn’t surprising Artificial Intelligence in finance is leading the pack. AI is being incorporated in more and more industries but there seems a lack of take up in finance, investing, accounting and regulatory compliance in particular.
This article will discuss why this is and why there is a dire need for the application of Artificial Intelligence in the Financial Sector.
The need to understand, even on a basic level, the technology controlling a business is a fundamental requirement in any line of business. It is obvious, in particular with regulatory compliance (Anti Money Laundering / Terrorist Financing), that the financial sector would be reticent with the uptake of the tech.
If the need arose to explain to a regulator how a transaction wasn’t flagged or how a customer was on-boarded, and the institute can’t track back then there is a regulatory problem. Audit of decision-making and process is a fundamental aspect of any AML program.
Artificial Intelligence is not understood well in the finance world. Here is a quick 101 on Artificial Intelligence.
- Artificial Intelligence is not an algorithm.
- An algorithm is just a process or set of rules to be followed in calculations or other problem-solving operation, especially by a computer. It is ‘hard coded’ and cannot deviate from the ‘input, calculation, output’ process.
- Artificial Intelligence is using algorithms to learn from previous outcomes without the need for external human intervention. It is not programmed to perform a task, but programmed to learn to perform a task.
- AI is faster and more able than a human to do huge assessments in a matter of seconds.
- Through artificial intelligence in regulatory compliance, a program could learn the typologies of criminals and learn ways to discover them in action; re-iterating the outcome with new data to repeatedly test the assumption uncovered.
- This would reduce false positive AML flags and on-board financial customers much faster and more accurately than a human ever could.
- AI relies on good data-sets, the larger and more relevant the better.
- AI can weight the relevance of input through understanding the output from previous iterations.
- Imagine an AML investigator working off output provided by an AI program, inputting fresh data to improve the output. The AI program would consider that new input when calculating in the future. So control is maintained and results improved as more data is added.
- AI can do this by learning to weight the relevance of input to the output resulting.
- It can be supervised, unsupervised or a reinforcement program.
- Supervised AI is a program that learns from a labeled data-set.
- Un-supervised Artificial Intelligence has unlabeled data and looks for patterns in that data that it tries to make sense of.
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- It’s the difference between having an instruction manual and the finished product identified when you’re building ac flatpack, to just having the pieces and trying to make sense of which piece goes where to build something you don’t know what it is until you finish it.
- Clearly in a regulated service industry like Finance, accounting or investments, finance and accounting professionals need to understand why a particular decision was made to enable audit of that decision when something goes wrong. It is for this reason Supervised AI is a better model to utilise.
- An additional consideration is to have both supervised and unsupervised programs assessing the data to uncover facets of unconsidered results that could inform and improve the supervised program. Especially when data input is disparate, sometimes conflicting and frequently unstructured.
The application of Artificial Intelligence in finance, accounting and investing service industries is critical to improving performance and compliance.
CYW are building a regulatory compliance intelligence tool to improve the ability to detect and deter money laundering, terrorist financing and fraud in the industry.