8 Amazing Banking and Insurance Challenges in Kaggle

Here are some amazing competitions in Kaggle that allows you to work with close to real data and find out for yourself what happens in the actual industry. A brief description from the competition page is provided here, and if it interests you, click on the following link to visit the competitions. You may also join one of our teams here.

Also Read 12 Amazing Marketing and Sales Challenges in Kaggle

1. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. Visit the Competition Page

2. This competition asks you to determine whether a loan will default, as well as the loss incurred if it does default. Unlike traditional finance-based approaches to this problem, where one distinguishes between good or bad counterparties in a binary way, it seeks to anticipate and incorporate both the default and the severity of the losses that result. Visit the Competition Page

3. Improve credit risk models by predicting the probability of default on a consumer credit product in the next 18 months. Visit the Competition Page

4. Deep understanding of different risk factors helps predict the likelihood and cost of insurance claims. The goal of this competition is to better predict Bodily Injury Liability Insurance claim payments based on the characteristics of the insured customer’s vehicle. Visit the Competition Page

5. An important part of succeeding as an insurance company is having a good understanding of which of the company’s current customers will be with the company into the future. The goal of this competition is to predict which current customers will still be with the company in 6 months, given many of the customer’s characteristics. Visit the
Competition Page

6. Using an anonymized database of information on customer and sales activity, including property and coverage information, Homesite is challenging you to predict which customers will purchase a given quote. Accurately predicting conversion would help Homesite better understand the impact of proposed pricing changes and maintain an ideal portfolio of customer segments. Visit the Competition Page

7. Within the business insurance industry, fire losses account for a significant portion of total property losses. High severity and low frequency, fire losses are inherently volatile, which makes modeling them difficult. In this challenge, your task is to predict the target, a transformed ratio of loss to total insured value, using the provided information. This will enable more accurate identification of each policyholder’s risk exposure and the ability to tailor the insurance coverage for their specific operation. Visit the
Competition Page

8. In a one-click shopping world with on-demand everything, the life insurance application process is antiquated. Customers provide extensive information to identify risk classification and eligibility, including scheduling medical exams, a process that takes an average of 30 days.

The result? People are turned off. That’s why only 40% of U.S. households own individual life insurance. Prudential wants to make it quicker and less labor intensive for new and existing customers to get a quote while maintaining privacy boundaries. Visit the Competition Page

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