Deep-Recurrent Neural Networks Approach for Indonesian Banks Term Deposit Interest Rates Prediction - Epatha, Leono; Aedah, Abd Rahman; Hoga, Saragih (2021)
This digital era has brought a significant impact on banking. Banks have become an intensive subject of data and must optimize data usage for more insight. Banks can explore their data to increase their productivity and sales. A study in 2019 showed XYZ Bank in Indonesia has not yet explored data and did not optimize it to understand customers and their needs better. This study tried to find the best Recurrent Neural Network (RNN) technique to predict Indonesian banks' term deposit interest rates by comparing three popular RNN variants. Those RNN techniques were Simple RNN, LSTM, and GRU, which used historical data from 22 prominent banks in Indonesia covers 2019–2021. This study found that RNN with Simple RNN technique outperformed LSTM and GRU. Simple RNN brought the smallest mean of RMSE with 1,48% RMSE reduction from LSTM and 1,69% RMSE reduction from GRU