Abstract
In recent times, machine learning is finding many important use cases in the financial services industry. Financial datasets usually pose significant statistical challenges, and hence, traditional econometric methods fail in many practical applications. Though several approaches are suggested and being used, to predict whether a borrower would default on the loan availed, it is still an open challenge. In this work, two approaches are proposed based on LSTM (Long Short Term Memory) along with a hybrid neural network architecture to understand the context between financial transactions and loan defaults. The novelty of the proposed methods is in how they handle structured data and the associated temporal data. Further, the performance of the proposed approaches using debit card transactions and loan application information of customers to predict default is demonstrated. The experiments provided promising accuracies. Bidirectional LSTMs with a hybrid architecture achieve an accuracy of 94%. Hybrid neural network architectures used in this work provide an appropriate direction for making an early warning system through online loan default prediction.
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Kandappan, V.A., Rekha, A.G. (2021). Machine Learning in Finance: Towards Online Prediction of Loan Defaults Using Sequential Data with LSTMs. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-1696-9_5
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