How would you like the ability to accurately predict financial crimes and prevent them before losing a penny?
No, this isn’t the plot of a sci-fi thriller or a superpower we can grant you. Artificial intelligence (AI) is changing the finance industry in ways that couldn’t be imagined even a few years ago. We haven’t quite attained Azmovian levels of prescience yet, but we’re moving closer every day.
Understanding How AI Works
Artificial intelligence has not quite reached complete independence from human input; our machines are still only as smart as their programming. There are four types of AI, which can generally be divided into two categories. One is narrowly defined to be intuitive, but only within the context of its environment and purpose. The other type is the stuff from which sci-fi fantasies are made. It’s able to learn, evolve, and make decisions over a range of applications completely independent of human input or interference. This type of AI has the potential to attain self-awareness some day.
Most current cybersecurity software falls into the first category. It can analyze behavior, use that analysis to identify patterns and make predictions or recommendations based on its findings. It can also develop a set of criteria to define fraud, matching customer behavior against that backdrop to detect potential financial crimes such as fraud and do it faster than a team of human analysts.
This is similar to how AI and big data improve other organizational models by analyzing previous outcomes or running simulations during negotiations training. Such evaluations and “safe-mode” environments help develop critical business skills and streamline functions without directly impacting companies in a negative way.
There are five main applications for big data and machine learning in the finance sector: assessing risk, detecting and preventing fraud, providing advisory services, facilitating trades, and financial management. Of these, the most critical are the first two.
Revolutionizing Cyber Security and Predicting Financial Fraud
Machine learning is a component of AI that relates to designing and applying algorithms in a contextual environment. It allows machines to read, parse, and analyze data at rates that are impossible for humans to replicate, and it does it with unmatched accuracy. Implementing AI for fraud detection allows financial institutions and other organizations to devise comprehensive and precise security protocols that can run in the background without interference or oversight.
Machine learning programs are created to imitate the mental agility of humans while removing distractibility, mental fatigue, poor eyesight, and other human failings. This enables a level of speed, accuracy, and incorruptibility that makes manual reviews obsolete. Traditional software is certainly capable of detecting and flagging unusual behavior, but it takes human input to change or update the program if something is altered erroneously. AI has the ability to learn from its mistakes, to use the term loosely, and self-correct, refining it analytical capabilities to create more sophisticated and nuanced responses independent of human correction or input.
One example of how machine learning is used to detect fraud is in the area of banking transactions. The program gathers information and segments it into predetermined metrics using historical data as a model. This can be previous customer activity or any other relevant data set. Using this model, the algorithm is able to perceive anomalies in future behavior patterns, detect fraudulent behavior, and prevent the loss of financial data. Due to the speed and capability of machine learning environments, data points can be as numerous, complex, and varied as needed for the size and requirements of the company without affecting the speed or processing time needed to perform its function.
Cyber attacks cost an estimated 360 – 600 billion dollars per year. The rise in financial crimes parallels the rise in online banking, investment, and other financial transactions, but fraud isn’t limited to cyberspace. Every industry or organization that generates or processes revenue has a built-in potential for fraudulent financial activity, whether it’s on-site or online.
Technology is advancing faster than the average business can keep up with, but implementing just a portion of what’s possible is an investment in financial stability.
