As early as the beginning of the Millennium, computer software has been used to detect fraud. However, a happy world is coming for financial trading. It’s called artificial intelligence or machine learning, and the software will revolutionize the way banking institutions detect and deal with fraud.

Everyone knows that fraud is a major problem in banking and financial services. It has been that way for a long time. However, today the effort of banks and other financial institutions to identify and prevent fraud now relies on a centralized method of regulation known as the Anti-Money Laundering (AML) database.

AML identifies individuals involved in financial transactions who are on sanctions lists or individuals or businesses that have been flagged as criminals or high risk individuals.

How AML Works

So suppose that the nation of Cuba is on the sanctions lists and actor Cuba Gooding Jr. wants to open a checking account at a bank. Immediately, due to your name, the new account will be marked as fraudulent.

As you can see, detecting true fraud is a very complex and time-consuming task and can lead to false positives, creating many problems for both the falsely identified person and the financial institution that made the false identification.

This is where machine learning or artificial intelligence comes in. Machine learning can prevent this unfortunate false positive identification, and banks and other financial institutions save hundreds of millions of dollars in the work required to fix the problem, as well as the resulting fines.

How Machine Learning Can Prevent False Positives

The problem for banks and other financial institutions is that fraudulent transactions have more attributes than legitimate transactions. Machine learning allows computer software to create algorithms based on historical transaction data as well as authentic customer transaction information. The algorithms then detect patterns and trends that are too complex for a human fraud analyst or some other type of automated technique to detect.

Four different models are used that help cognitive automation to create the appropriate algorithm for a specific task. For example:

1. Logistic regression is a statistical model that analyzes a retailer’s good transactions and compares them to its chargebacks. The result is the creation of an algorithm that can predict whether a new transaction is likely to become a chargeback.
2. Decision tree it is a model that uses rules to perform classifications.
3. Random forest it is a model that uses multiple decision trees. Prevents errors that can occur if only one decision tree is used.
4. Neural network is a model that attempts to simulate how the human brain learns and sees patterns.

Why Machine Learning is the Best Way to Manage Fraud

Analyzing large data sets has become a common way to detect fraud. Employee machine learning software is the only method to properly analyze the multitude of data. The ability to analyze so much data, drill down into it, and make specific predictions for large volumes of transactions is why machine learning is a primary method of detecting and preventing fraud.

The process results in faster determinations, allows for a more efficient approach when using larger data sets, and provides algorithms to do all the work.