Classifying Bank Churn With ML Algorithms

Banks are categorically known for being long-standing businesses. A typical bank headquarters looks more like a government building than a startup, and customer retention is of utmost importance to their success.

That's because customers who stay with a bank for long periods of time are the most profitable—so understanding and preventing customer churn is an essential part of banking success. 

However, even the most successful banks still experience customer attrition. In fact, the average bank or financial institution loses 25 to 30 percent of its customers every year. This is a significant drain on resources, so it's important for banks to understand why customers leave and how to prevent it. 

In this article, we'll explore the concept of bank churn and how you can leverage machine learning algorithms to predict and prevent customer attrition.

What is customer churn and why is it important for a bank or financial institution to reduce churn?

Customer churn, in this context, refers to the percentage of customers who have stopped doing business with a bank or other financial institution. At their core, banks are considered lenders, who make money by borrowing money from depositors and then lending it out to borrowers. But in order to make money, they need to attract and retain customers.

When customers churn, they leave behind fewer deposits and fewer opportunities for loans. There are also specific types of churn, like when an active member cancels their credit card, or when new customers never get around to using the financial services offered.

This lost revenue could have been used to fund new services, hire more staff, and increase their customer base. Instead, competitors who pick up those churned customers will use their revenue boost to increase their competitive edge.

That's why reducing the customer churn rate is so important for a bank or financial institution. It helps them remain competitive and profitable in a rapidly changing financial landscape. There are many potential causes of customer churn, such as poor customer service, financial instability, or a lack of competitive rates.

For instance, financial institutions like Robinhood offer interest rates of up to 4% on some of their accounts, which may be more appealing than traditional banks’ sub-one-percent interest rates. Furthermore, many digital banks offer always-on customer service and attractive features such as no-fee checking and savings accounts. Banks that lack these features may find themselves losing customers to competitors.

Regardless of the cause, it's important to understand why customers are leaving and how to prevent them from doing so. The flip side is that there are many potential avenues to reduce churn, such as providing additional contact points, sending out personalized offers, or even offering loyalty rewards to customers who stay with the bank.

What is customer churn prediction and how does ML for it work? 

Customer churn prediction is the process of using machine learning models to identify customers who are likely to leave in the near future. ML algorithms analyze existing customer data, such as credit score and estimated salary, to identify patterns of customer behavior and make predictions about upcoming churn rates. This type of analysis is often used in the financial services industry and retail banking to optimize customer retention rates.

To understand how ML works for customer churn prediction, it’s important to first understand the techniques involved. Bank customer churn prediction is fundamentally a classification problem where we build a classifier to predict whether or not a customer will churn. The target variable of this classification problem is categorical (churn vs. no churn), while the high-level analysis is numerical (the churn rate).

Data preprocessing is an essential step before building ML models. Data preprocessing involves cleaning the data, dealing with missing values, and removing outliers. Feature engineering is also important in order to create useful features that can be used in ML models. Typically, the data used for customer churn prediction includes customerID, categorical variables such as country and numerical variables such as credit score.

Once the data is preprocessed, ML models can be used to build the classifier for customer churn prediction. For example, the Python library SKlearn has an implementation of RandomForestClassifier which can be used for customer churn prediction. Random forest is a type of supervised learning algorithm that builds multiple decision trees and combines them to make a more accurate and stable prediction. 

To evaluate the performance of a classifier, we use metrics such as confusion matrix, F1 score, AUC, and ROC curve. The confusion matrix helps to evaluate the number of false positives, false negatives, true positives, and true negatives. The F1 score is a metric that evaluates the accuracy of the model. The AUC (area under the curve) and ROC (Receiver Operating Characteristic) curve are also used to evaluate the performance of the ML model.

Cross-validation and optimization techniques are used to further improve the model performance. Cross-validation is used to split the data into training, test, and validation sets. Then, the model is optimized by tuning the hyperparameters, such as learning rate and maximum depth.

How does an ML classification algorithm work?

ML classification algorithms use supervised learning to classify data points into categories. The algorithm starts by labeling a set of data points with the correct category. It then uses this dataset to "learn" how to classify new data points. 

The most common classification algorithms include support vector machines (SVM), k-nearest neighbors (KNN), decision trees, artificial neural networks (ANN), logistic regression, naive Bayes, linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA). 

Support vector machines (SVM) are a type of supervised machine learning algorithm that can be used for both classification and regression. The algorithm works by creating a hyperplane that separates the data points into two categories. It does this by finding the maximum margin between the two categories. 

K-nearest neighbors (KNN) is a type of lazy learning algorithm that classifies data points based on their proximity to other data points in the dataset. It works by calculating the distance between the data points and finding the K nearest neighbors. It then assigns the majority label of the K nearest neighbors as the label for the data point. 

Decision trees can be used for both classification and regression. The algorithm creates a tree-like structure that can be used to classify data points. It works by splitting the dataset into smaller subsets and then making decisions based on the characteristics of these subsets. 

Artificial neural networks (ANN) are a type of supervised learning algorithm that is inspired by the biological neural networks of the human brain. The algorithm works by connecting nodes together in a network and adjusting the weights of each node based on the input data. This allows the algorithm to "learn" how to classify data points. 

Logistic regression works by creating a linear boundary between the two classes of data points. It then uses a logistic function to calculate the probability that a data point belongs to one of the two classes. 

Finally, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) work by creating a linear or quadratic boundary, respectively, between the two classes of data points. 

What are the different elements in churn analysis?

In order to accurately predict customer churn, banks must use the right ML classification algorithm for the task. Each algorithm has its own strengths and weaknesses, and it is important to understand which algorithm is best suited for the task at hand. 

In any ML classification algorithm for churn analysis, there are several key elements that must be taken into account. These include the raw data inputs, the appropriate statistical techniques, the model selection criteria, and the evaluation techniques. 

Raw data inputs are the data points that the algorithm will use to make its predictions. This could include customer transaction history, customer satisfaction surveys, and past interactions with the bank. 

The appropriate statistical techniques must be used in order to accurately process the data and make an accurate prediction. This could include regression analysis, classification analysis, clustering analysis, or a combination of these techniques. 

The model selection criteria is used to determine which algorithm is best suited for the task. This will depend on the data available, the accuracy of the model, the speed of the model, and the number of resources available. 

Finally, the evaluation techniques are used to determine how accurate the model is. These could include precision, recall, and accuracy metrics.

How can you apply ML algorithms to a banking business or financial institution?

In the current changing economic landscape, financial institutions are searching for ways to improve their operations and drive performance. This has led to a surge in the use of artificial intelligence to generate insights, automate processes, and more.

However, many businesses, particularly those in the banking industry, lack the necessary coding and data science expertise to apply ML algorithms to their operations. Fortunately, there is a solution. 

Akkio's easy machine learning platform enables businesses to build models effortlessly from any data source and deploy them anywhere. It's used for everything from lead scoring and customer segmentation to churn prediction and credit risk assessment. 

When it comes to churn prediction, Akkio makes it easier to identify and classify bank churn. The platform provides an intuitive user interface and automated processes that allow users to quickly build models and get predictive results. 

First, you connect a data source to the platform and select the customer churn column. Then, you simply hit predict and Akkio automatically builds a model to predict the probability of customer churn.

You then have several deployment options, such as an API, a web model, or direct integrations with Salesforce, Snowflake, Google Sheets, and any Zapier-enabled system.

Try a churn prediction AI model 

Below, you can see a churn prediction model deployed as a web model. It allows you to enter sample customer data and get predictive results in real time.

By using Akkio, businesses can build the best model for churn prediction and easily deploy it to their operations. This means businesses don't need to invest in expensive coding or data science experts to build and maintain ML models. 

Conclusion

Businesses of all sizes are navigating a volatile economic landscape. Layoffs populate major news headlines, and customers are increasingly discerning about where they will give their money. This makes customer churn a major threat to financial institutions around the world. 

Fortunately, banks and financial institutions can use machine learning algorithms to predict customer churn and take proactive steps to retain them. By leveraging the right ML algorithms, banks can identify high-risk customers and take steps to retain them. 

Akkio’s no-code AI platform makes it easier than ever to build and deploy ML models, including models for customer churn.  With its intuitive user interface and automated processes, Akkio allows businesses to identify customer churn in an easy and cost-effective way. There are also many other use cases use cases for ML in finance, such as predicting a customer’s propensity to buy.

Start predicting customer churn today with Akkio. Sign up for a free trial and revolutionize your customer retention strategy.