The risk assessment process carried out by insurance companies has become obsolete.
Indeed, evaluating a customer for life insurance takes an average of 30 days. Moreover, this process is subjective, and different risk analysts usually give different evaluations for the same customer.
But machine learning can help to solve these problems. By analyzing the available information on the customers stored by the company, we can develop risk models that evaluate new customers faster and more accurately.
As we will see, machine learning benefits both the company and the customer by providing better services and more suited products to clients. These methods will allow insurance companies to save money and increase revenues.
The dataset used here is taken from Kaggle, one of the most important repositories of data science.
It contains information about 60.000 customers with 145 attributes.
This data set contains about 400.000 missing values, i.e., information about customers unavailable to the company.
However, machine learning can deal with incomplete data without problems.
The input variables include personal, family history, medical history, and external variables.
The target variable is the risk of that client. The risk is a subjective variable because people, specifically risk analysts grant it.
The following table shows the distribution of the risk evaluations.
It might be interesting to study how the different features affect a customer’s risk. We can calculate the correlation coefficients between all the inputs and the target.
Correlations close to 1 mean a high dependency of that target with that input. Correlations close to 0 mean that there is a low dependency. Note that, in general, the targets depend on many inputs simultaneously.
The following images show the absolute value of the linear correlations between all inputs with the target variable.
As we can see, the three most influential variables for the risk are the body mass index BMI (0.382), the weight Wt (0.351), and the medical history 23 (0.287).
The next step is to know the distribution of the target variable over its entire range. Box plots display information about the first, second, third, and fourth quartiles of a single variable in the data set. The following chart shows the box plot for the risk.
In this case, the Box plot tells us that the minimum risk is 1, the first quartile is 4, the second quartile or median is 6, the third quartile is 8, and the maximum is 8. Note that the upper quartile is missing, which means that the risk variable is not well distributed.
Histograms also give us valuable information on the distribution of a variable. The following chart shows the histogram for the risk.
The chart above shows that the most common risk value is 8 (low). The less common risk value is 3 (medium-high). We can see that this histogram does not have a normal (or Gaussian) distribution. Indeed, analysts tend to give either low or high-risk values. The 3 and 4 values have very low frequencies. The value 7 has a lower frequency than the values 6 and 8.
Therefore, the distribution of the target variable is wrong. It should be a normal distribution, but clearly, it is not. Indeed, most customers should have a medium risk. This is because some risk analysts tend to give low or high evaluations.
Our model should output risk values with a normal distribution.
The next step of our study is to build a model capable of evaluating our potential customers’ risks. This model will use the extensive information that they provide us.
For this purpose, we use a technique called neural networks. Neural networks are among the most potent methods to discover intricate relationships, recognize complex patterns or predict current trends in your data.
The following graph illustrates the neural network used in this application. Note that the neural network is too large to be plotted.
After training the network, we evaluate its performance to check if our risk model helps assess clients. The table below shows the minimums, maximums, means, and standard deviations of the neural network’s absolute, relative, and percentage errors for the testing data.
|Mean absolute error||1.534|
|Mean percentage error||21.920%|
As we can see, the mean error is 1.5 over 8. That means the model evaluates customers’ risk with quite a high precision.
Now, let’s look at the histogram of the risk variable by applying the predictive model.
The previous histogram shows that now the risk variable follows a normal distribution, so it is well distributed. Most customers have average values, and the evaluations do not focus on the extremes. This is a desirable property of our risk model.
Insurance companies need to reduce the time of evaluating customers’ risk and provide more objective evaluations to reduce costs and provide more suitable insurance policies.
In this case study, we have used machine learning to accurately assess customers’ life insurance risk. Using this model, the company will reduce costs associated with very long evaluation processes and increase its benefits by offering each client the right insurance policy.