Machine Learning And Fraud Prevention

Machine Learning And Fraud Prevention

As early as the beginning of the Millennium computer software has been used to detect fraud. However, a brave new world is coming to the financial trade. 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 meaningful problem in banking and financial sets. It has been so for a long time. However, today the effort of edges and other financial institutions to clarify and prevent fraud now depends on a centralized method of regulations known as the Anti-Money Laundering (AML) database.

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

How AML Works

So let’s assume that the nation of Cuba is on the sanction lists and actor Cuba Gooding Jr. wants to open a checking account at a bank. closest, due to his name, the new account will be flagged as fraudulent.

As you can see, detecting true fraud is a very complicate and time-consuming task and can consequence in false positives, which causes a whole lot of problems for the person falsely identified in addition as for the financial institution that did the false identification.

This is where machine learning or artificial intelligence comes in. Machine learning can prevent this unfortunate false positive identification and edges and other financial institutions save hundreds of millions of dollars in work necessary to fix the issue in addition as resulting fines.

How Machine Learning Can Prevent False Positives

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

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

  1. Logistic regression is a statistical form that looks at a retailer’s good transactions and compares them to its chargebacks. The consequence is the creation of an algorithm that can forecast if a new transaction is likely to become a chargeback.
  2. Decision tree is a form that uses rules to perform classifications.
  3. Random Forest is a form that uses multiple decision trees. It prevents errors that can occur if only one decision tree is used.
  4. Neural network is a form that attempts 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 shared way to detect fraud. Software that employs machine learning is the only method to adequately analyze the multitude of data. The ability to analyze so much data, to see thorough into it, and to make specific predictions for large volumes of transactions is why machine learning is a dominant method of detecting and preventing fraud.

the time of action results in faster determinations, allows for a more efficient approach when using larger datasets and provides algorithms to do all of the work.

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