Machine learning and fraud prevention

Early in the beginning of the Millennium Computer program it was used to detect fraud. However, a brave new world approaching financial trade. Called artificial intelligence or machine learning, the program will revolutionize the way banking institutions discover and deal with fraud.

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

AML identifies individuals who engage in financial transactions listed in the sanctions lists or individuals or companies who have been marked as criminals or persons at high risk.

How AML works

Therefore, let us assume that the Cuban nation is included in the sanctions lists and that the representative, Cuba Gooding Jr., wants to open a checking account with a bank. Immediately, due to his name, the new account will be marked as fraudulent.

As you can see, detecting real fraud is a very complex, time consuming and potentially false positives task, which causes a lot of problems to the person who has been falsely identified as well as to 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 that banks and other financial institutions save hundreds of millions of dollars in the work necessary to solve the problem in addition to the resulting fines.

How machine learning can prevent false positives

The problem for banks and other financial institutions is that fraudulent transactions have more features than legitimate transactions. Computerized learning allows a computer program to create algorithms based on historical transaction data as well as information from original customer transactions. The algorithms then discover very complex patterns and trends that a human fraud analyst or any other type of automated technology cannot detect.

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

  1. Logistic regression It is a statistical model that looks at good retailer transactions and compares them with payments. The result is an algorithm that can predict if a new transaction is likely to become a cost.
  2. Decision tree A model that uses rules to perform classifications.
  3. Random forest It is a model that uses multiple decision trees. It prevents errors that can occur if only one decision tree is used.
  4. Neural network It is a model that attempts to simulate how the human brain learns and how it sees patterns.

Why machine learning is the best way to manage fraud

Analysis of large data sets has become a common method of fraud detection. Programs that use machine learning are the only way to adequately analyze an adequate number of data. The ability to analyze a lot of data, see it in depth, and set specific expectations for large amounts of transactions, is the reason why machine learning is an essential means to detect and prevent fraud.

The process leads to faster selections and allows for a more efficient approach to using larger data sets and algorithms to do all the work.


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