Data science's relevance in the financial industry

Look into how data science is used in the most common way it is used in the banking industry: to spot fraud. The data science institute gives a good understanding of all the concepts in this field through data science courses.

The banking industry is one of the lucky ones that usually have a lot of structured data. It was also one of the first industries to start using the technology that goes along with data science.

What role does data science play in the world of finance?

The information we gather quickly becomes the most valuable thing in our field. Data science course is a must-have for banks if they want to compete, get more customers, keep their current customers more loyal, make better decisions based on data, give their business more power, improve operational efficiency, improve existing services and products and come up with new ones, increase security, and make more money. It shouldn't surprise that most data scientists work in the banking industry. So take up data scientist training to get into the world of finance.

How Data Science is Used to Find Fraud in Banking

Fraudulent actions are not only a problem in the banking industry but also in the government, the insurance industry, the public sector, sales, and healthcare. Fraud can happen to any business that does a lot of business online. Examples of financial crimes include fraudulent credit card transactions, fake bank checks, money laundering, cyber attacks, fake identities, and scams.

Currently, there is no machine learning method for spotting fraud that is standardized and reliable. Instead, real-world data science certification often look into several different techniques or combinations of strategies, figure out how accurate the model is expected to be, and then chooses the best one.

The most basic problem with fraud detection systems is that they must adapt quickly to the constantly changing patterns and methods fraudsters use. They also have to find new and more complicated schemes quickly. Fraud is always done by a small number of people and is carefully hidden in legal business transactions.

Putting the Dataset Together

By definition, there will only be a very small number of fraud cases in any group of transactions. On the other hand, machine learning algorithms usually work best when there is about the same number of examples from each class in the dataset. Aside from that, there isn't much else to help us figure out what's happening. This kind of thing is called a class imbalance.

Fraud in the dataset led to mistakes in the calculations

Now that we have more information, we can confirm that the number of fraudulent transactions is not very high and that there is a class imbalance in this situation. To stop it, we might try a method called "synthetic minority oversampling" to rebalance our data (SMOTE). SMOTE is a much more complicated method than random oversampling because it doesn't just copy the same data over and over.

The Smote Method

Because of this, the SMOTE method completely balanced our data, and the sizes of the majority class and the minority class are now the same.

The goal is to set threshold values based on common statistics, like the mean value of observations, and then use those thresholds on our attributes to spot fraud.

Logistic Regression is the method that is used

We will now use a simple logistic regression classification method on the available data to find cases of credit card fraud, and the results will be shown on a confusion matrix:

Conclusion

We could change some of the algorithm's settings to make the logistic regression model work more accurately. As an alternative to just splitting the dataset in half, you could look into K-fold cross-validation.

You can look over the Fraud Detection in Python course materials to learn more about the theoretical and practical parts of building a fraud detection model.