machine learning and fraud prevention
Already at the beginning of the millennium, computer programs have been used to detect fraud. However, a brave new world is coming to 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. Today, however, the efforts of banks and other financial institutions to identify and prevent fraud now rely 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 the LMA works
So let’s say the nation of Cuba is on the sanctions list and actor Cuba Gooding Jr. wants to open a checking account at a bank. Immediately, due to his name, the new account will be marked as fraudulent.
As you can see, detecting true fraud is a very complex and time-consuming task that can result in false positives, creating a lot of problems for the person falsely identified, as well as for 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, saving banks and other financial institutions hundreds of millions of dollars in the work required to fix the problem and 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 software on a computer to create algorithms based on historical transaction data as well as information from authentic customer transactions. 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 to help cognitive automation create the appropriate algorithm for a specific task. For example:
- Logistic regression it is a statistical model that analyzes a retailer’s good transactions and compares them to their chargebacks. The result is the creation of an algorithm that can predict whether a new transaction is likely to become a chargeback.
- decision tree is a model that uses rules to perform classifications.
- RandomForest is a model that uses multiple decision trees. It prevents errors that can occur if only one decision tree is used.
- neural network it is a model that tries to simulate how the human brain learns and how it sees patterns.
Why machine learning is the best way to manage fraud
Analyzing large data sets has become a common way to detect fraud. Software that employs machine learning 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 leading 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.