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- W4383670001 abstract "Large financial institutions have a lot of products for earning money, but a major source of their income is from lending money. This income mainly depends on the ability of the loan taker to repay the loan. Every year these institutions incur huge losses due to loan defaulters. Through this research, we will try to predict the possibility of an individual defaulting on a loan using various features in our dataset. Then, will try to explain our results using the novel concept of explainable AI. This research uses a random forest classifier to predict if the individual will have difficulties in repayment of loans. The dataset is sourced from Kaggle. This research has followed a machine learning pipeline wherein we have pre-processed dataset, trained model, predicted outcomes using a model trained, and explained results using explainable ai. Random Forest and XGBoost algorithms are used. The models predicted results with accuracy as high as 97%. For the explainable part, we have used the Shapash library. In our study, we have explained the factor that contributes most to global as well as individual predictions. The results of these predictions can be used by financial institutions to enforce rules and restrictions surrounding this parameter to avoid losses. Furthermore, the explanations for the individual predictions can be used for personalized loan profile improvement recommendations." @default.
- W4383670001 created "2023-07-09" @default.
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- W4383670001 date "2023-01-01" @default.
- W4383670001 modified "2023-10-11" @default.
- W4383670001 title "Predictive Analytics in Financial Services Using Explainable AI" @default.
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- W4383670001 doi "https://doi.org/10.1007/978-981-99-0483-9_35" @default.
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