PENERAPAN BIG DATA ANALYTICS DAN ARTIFICIAL INTELLIGENCE PADA SEKTOR PERBANKAN DAN KEUANGAN: LITERATURE REVIEW

Penulis

Kata Kunci:

Big Data Analytics, Artficial Intelligence, Banking Industries, Review

Abstrak

Penerapan Big Data Analytics (BDA) dan Artificial Intelligence (AI) pada sektor perbankan dan keuangan mengalami akselerasi cepat dalam lima tahun terakhir. Penelitian ini menyajikan Systematic Literature Review (SLR) terhadap 37 artikel penelitian empiris terpilih pada empat domain utama: penilaian risiko kredit (credit scoring), deteksi kecurangan (fraud detection), analisis churn nasabah, dan manajemen risiko portofolio. Hasil ulasan menunjukkan pergeseran dari pemodelan statistik tradisional menuju algoritma Gradient Boosting  serta Deep Learning . Studi ini juga mengidentifikasi tantangan utama terkait Explainable AI (XAI) untuk kepatuhan regulasi serta strategi penanganan ketidakseimbangan data (data imbalance) pada transaksi finansial real-time

Unduhan

Data unduhan tidak tersedia.

Referensi

Ala'raj, S., Majdalawieh, M. F., & Nishad, K. (2021). A hybrid ensemble classifier for credit scoring in commercial banking. Expert Systems with Applications, 172, Article 114656.

Alibeigi, M., Hashemi, S., & Hamzeh, A. (2022). A hybrid autoencoder and XGBoost model for addressing extreme class imbalance in credit card fraud detection. Expert Systems with Applications, 190, Article 116160.

Amin, A., Anwar, S., & Adnan, M. (2021). Distance-based sampling for imbalanced customer churn prediction in financial institutions. Data & Knowledge Engineering, 131, Article 101868.

Awoyemi, B. S., Adetunmbi, A. O., & Ali, U. A. (2022). Comparative analysis of machine learning algorithms for credit card fraud detection. Journal of Financial Crime, 29(4), 1301–1314.

Basel Committee on Banking Supervision. (2017). Basel III: Finalising post-crisis reforms (Technical Report). Bank for International Settlements (BIS).

Bilgic, P., Erdem, M., & Kaya, A. (2022). Customer churn prediction in retail banking using XGBoost classifier. International Journal of Bank Marketing, 40(5), 981–999.

Bussmann, N., Giudici, P., Marinelli, S., & Papenbrock, L. (2021). Explainable AI in Fintech credit risk management. Finance Research Letters, 42, Article 101939.

Chen, X., & Liu, Y. (2025). Real-time distributed anomaly detection in banking Big Data streams using Apache Spark. IEEE Access, 13, 10245–10258.

De Caigny, A., Coussement, K., & De Bock, K. W. (2021). A new hybrid classification algorithm for customer churn prediction based on text reviews and transactional data. European Journal of Operational Research, 290(3), 1182–1194.

F. Ranja, E. B. Nababan and A. Candra, "Synthetic Data Generation Using Time-Generative Adversarial Network (Time-GAN) to Predict Cash ATM," 2023 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Bandung, Indonesia, 2023, pp. 418-423

Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Rossi, F. (2022). Using Generative Adversarial Networks to overcome class imbalance in financial fraud detection. Information Sciences, 584, 312–327.

Forough, J., & Momtazi, S. (2021). Sequential transaction log modeling using Bi-LSTM and attention mechanism for credit card fraud detection. Journal of Information Security and Applications, 60, Article 102862.

Gu, S., Kelly, B., & Xiu, D. (2021). Empirical asset pricing via machine learning. The Review of Financial Studies, 34(5), 2223–2277.

Hernandez, M., Lopez, C., & Ramirez, G. (2023). Credit card customer segmentation using hybrid K-Means and DBSCAN clustering. Expert Systems, 40(4), Article e13210.

I. S. Putra, O. S. Sitompul and E. B. Nababan, "Customer Churn Prediction using Confident Learning and XGBoost," 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Bandung, Indonesia, 2024, pp. 315-318

Jiang, Z., Xu, D., & Liang, J. (2022). Financial portfolio management with deep reinforcement learning: A PPO-based empirical evaluation. Quantitative Finance, 22(8), 1501–1515.

Kim, J., Park, N., & Lee, K. (2022). Systemic risk forecasting in banking networks using LSTM with attention mechanism. Journal of Financial Stability, 60, Article 101005.

Kim, S., & So, H. (2023). Customer lifetime value estimation in retail banking using recurrent neural networks. Journal of Retailing and Consumer Services, 71, Article 103210.

Kumar, S., Sharma, R., & Verma, A. (2023). SME credit scoring using a stacking ensemble of LightGBM and CatBoost. Journal of Financial Data Science, 5(2), 45–61.

Li, H., Wang, Y., & Zhang, T. (2024). Aspect-based sentiment analysis of mobile banking application reviews using fine-tuned BERT. Computers in Human Behavior, 151, Article 107980.

Liu, Y., Zhang, W., & Meng, Z. (2024). Risk-sensitive reinforcement learning for dynamic portfolio optimization. IEEE Transactions on Neural Networks and Learning Systems, 35(8), 10890–10902.

Lucas, Y., Portier, P. E., Laporte, L., He-Guelton, L., Caelen, O., & Granitzer, M. (2021). Multiple perspective Hidden Markov Models for credit card fraud detection. Pattern Recognition Letters, 145, 120–127.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Dalam Advances in Neural Information Processing Systems (NeurIPS 2017) (vol. 30, hlm. 4765–4774). Neural Information Processing Systems.

Nguyen, T., Tran, H., & Le, D. (2026). Low-latency transaction fraud detection via self-supervised vision transformers on tabular embeddings. IEEE Transactions on Information Forensics and Security, 21, 405–418.

Niu, K., Zhang, J., & Wang, L. (2023). TabNet for retail credit risk assessment: An empirical evaluation on tabular banking data. IEEE Transactions on Reliability, 72(3), 1102–1114.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71.

Patel, V., & Shah, M. (2021). Foreign exchange rate forecasting using hybrid ARIMA-LSTM model. Journal of King Saud University - Computer and Information Sciences, 33(9), 1135–1146.

Prasetyo, B., & Raharjo, S. (2024). Predicting loan default of unbanked population using mobile digital footprints and multi-layer perceptron. Asian Journal of Information Systems, 12(1), 78–92.

Rahman, M., Islam, T., & Ahmed, R. (2024). Hybrid recommendation system for banking product cross-selling using matrix factorization. Decision Support Systems, 176, Article 114050.

Rossi, E., Ferrari, M., & Conti, A. (2024). Bayesian neural networks for bank balance sheet stress testing under macroeconomic shocks. Journal of Econometrics, 240(2), Article 105680.

Santos, L., Ribeiro, R., & Silva, F. (2022). Random survival forests for customer retention analysis in commercial banking. Journal of Business Research, 142, 512–525.

Silva, R., Santos, M., & Oliveira, J. (2025). Federated learning for cross-institutional credit scoring without privacy leakage. Future Generation Computer Systems, 162, 201–214.

Tripathy, N., Ray, A. K., & Mishra, S. C. (2022). Credit card default prediction using deep neural networks with dropout regularization. Journal of Banking and Financial Technology, 6(1), 23–35.

Wang, H., & Zhou, Y. (2023). Modeling stock market correlation networks using Graph Convolutional Networks. Knowledge-Based Systems, 268, Article 110480.

Wang, Y., Zhang, C., & Zhao, X. (2023). Temporal graph convolutional networks for financial anti-money laundering detection. IEEE Transactions on Knowledge and Data Engineering, 35(9), 9110–9123.

Wu, C., Chen, T., & Huang, L. (2025). High-frequency trading execution strategy using Deep Q-Networks on level-2 limit order book data. Financial Innovation, 11(1), Article 14.

Zhang, L., Chen, X., & Wang, M. (2022). Interpretable credit scoring model using XGBoost and SHAP in peer-to-peer lending. Decision Support Systems, 158, Article 113780.

Zhang, R., & Tan, K. (2023). Stock market prediction using Temporal Fusion Transformers: An empirical study on high-frequency time series. Neural Computing and Applications, 35(14), 10425–10440.

Zhao, X., Lin, Y., & Chen, H. (2025). Multi-modal Large Language Models for automated customer service in commercial banks. Computers in Industry, 164, Article 104180.

Zheng, P., Yuan, S., & Wu, X. (2024). Graph attention networks for fraud detection in online banking session logs. ACM Transactions on Knowledge Discovery from Data, 18(2), 1–19.

Unduhan

Diterbitkan

2026-07-20

Terbitan

Vol 1 No 1 (2026): Regular Issue – JULI 2026

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