Machine Learning Approach to Predict and Evaluate Banking’s Business Performance and Bankruptcy Bambang Siswoyo 1,2), Nanna Suryana 3), Zuraida Abbas 4.

Presentasi berjudul: "Machine Learning Approach to Predict and Evaluate Banking’s Business Performance and Bankruptcy Bambang Siswoyo 1,2), Nanna Suryana 3), Zuraida Abbas 4."— Transcript presentasi:

1 Machine Learning Approach to Predict and Evaluate Banking’s Business Performance and Bankruptcy Bambang Siswoyo 1,2), Nanna Suryana 3), Zuraida Abbas 4 1,2) Departement Information System, Maksoem University Indonesia. 3,4) Universiti Teknikal Malaysia Melaka (UTeM), {nsuryana, zuraida}

2 Paper Outline Introduction Related Work Proposed ensemble based Prdict Bankcrupt –Extraction of Features –Ensemble Model Experimental Results and Discussion Conclusion

3 INTRODUCTION Machine Learning Approach to Predict and Evaluate Banking’s Business Performance and Bankruptcy

4 INTRODUCTION  The prediction of the bankruptcy of a company, especially the prediction of bankruptcy of the banking industry in Indonesia is very important.  Bank Indonesia (BI) as a central bank, is able to make policies and develop a health banking system, whether the bank will be assisted by Indonesian Central Bank Liquidity Assistance, BLBI or merge with a healthier bank.  Machine learning is an interesting field to be applied in the banking industry. This research has been developed to build a model that able to predict and evaluate the bankruptcy of the banking industry.  Machine Learning Approach to Predict and Evaluate banking’s Business Performance and Bankruptcy model based on classification Two-Class Boosted Decesion Tree  Compare our proposed model with existing approaches which are based Twoclass Boosted Decision Tree and Multiclass Decesion Forest.

5 INTRODUCTION  The Machine learning is a part of artificial intelligence, which learns pattern recognition to get the optimal solution.  Machine learning allows the computer to find the optimal solution of data automatically.  Machine learning has been proven to solve many tasks. Machine learning to predict previously been used in the context of predicting bankruptcy with Robust Logistic Regression by Richard P. Hauser and David Booth [1], this model has its limitations.  Machine Learning is a scientific field that contains about learning machine to be Intelligent. To have an intelligence, machine must be able to learn. Machines can learn under different situation, namely supervised, Unsupervised and Reinforcement

6 INTRODUCTION There are a variety of different algorithms based Machine learning has been used for Predict, for example – k-Nearest Neighbour (k-NN Algorithm) – Bayesian algorithm – Support Vector Machine (SVM) – Neural Networks – Decision Trees – And others

7 Related work Machine Learning Approach to Predict and Evaluate Banking’s Business Performance and Bankruptcy

8 Related work Richard P Hauser et. al [1] Machine learning to predict previously been used in the context of predicting bankruptcy with Robust Logistic Regression by Richard P. Hauser and David Booth [1] Wen-Kuei Hsieh et. al [53] The use of backpropagation Hybrid Neural Network Bankruptcy Prediction: An Integration of Financial Ratios, Intellectual Capital Ratios, MDA, and Neural Network Learning. Department of Finance, De Lin Institute of Technology, Taipei 236, Taiwan

9 Related work Smotgomery at.all (44) Coordinate Failure? A Cross- Country Bank Failure Prediction Model. ADB Institute Discussion Paper No. 32. Chihil Hung at. Al [33] Hybrid Probability Based Ensambles For Bankruptcy Prediction. Hybrid Probability Based Ensembles For Bankruptcy Prediction. Department of Management Information Systems, Chung Yuan Christian University, Taiwan ROC, School of Computing and Technology, University of Sunderland, UK,2016

10 Research Methode Predict Bankcruptcy Machine Learning Approach to Predict and Evaluate Banking’s Business Performance and Bankruptcy

11 Variabel Reseach and Methode  The time series data of the five bank financial ratio variables published in Bank Indonesia from 2011 to 2015 have been collected and analyzed.  The input data consists of five variables: working capital to total asset, retained erning to total asset, earning before interest and taxed to total asset, market value of equity to book value of total liabilities, sales to total assets. While the output is classification of bankrupt, gray area, non bankcrupt.  The following summarizes the description of the data used in this study.

12 The Methode Used In The Clasification  The methods used in the bankruptcy classification of banks are the methods of Machine Learning (Multiclass Decision Forest Tree, Twoclass Boosted Decesion Tree).  In modeling Machine Learning algorithm, the author uses Microsoft Azure application.  Microsof Azure is a Machine Learning application that can help solve various modeling problems, pattern recognition, predictions that are often encountered in the community.

13 Variabel and Data Used

14 Work flow diagram of the Hybrid Approach

15 Experiment Two-class Boosted Decision Tree

16 Experiment Multiclass Decision Forest

17 Ensamble Learning Method The Decision Forest algorithm is an ensemble learning method for classification. This algorithm works by building several decision trees and then choosing the most popular output class. Voting is a form of aggregation, where each tree in the forest decision clasification produces a non-normalized label frequency histogram. The aggregation process sums up this histogram and normalizes the results to get a "probability" for each label. Trees that have high predictive trust have greater weight in the ensemble's final decision. Decision Tree in general is a non-parametric model, which means they support data with varied distributions. In each tree, a simple test sequence is run for each class, increasing the level of tree structure until the leaf node (decision) is reached.

18 Ensemble Learning Archtectur  Learning Ensemble is an algorithm in machine learning that is commonly used to search for the best prediction solutions.  Learning Ensembles build training data and learning dataset and combine it, Ensemble Learning can also invite learning-based or learning-based committees to several classification systems.

19 Predict Classification. The Ensamble model output, which is a predicted label, is used to set and classify output into label values ​​ (Bankrupt, Gray Area or Non-Bankrupt). The threshold values ​​ used to classify the output predicted to one of the three classes are selected based on experimental observations that give us the greatest accuracy, precision and AUC.

20 Experimental Results and Discussion Improving Text Summarization Using ANFIS

21 Experimental Results ModelAcuracyPrecisionRecall MultiClass Decision Forest 80%87%80% Twoclass Boosted Decision Tree 95% 100% AUC 87% 90%

22 Classification performance using average precision, average recall, and average F-measure Multi-Class Decision Forest

23 Twoclass Boosted Decision Tree

24 Multiclass Decesion Forest

25 Result Overall, the results of this study indicate that the area under the curve (AUC) for the Twoclass Boosted Decision Tree is 90%, (Acuracy90%, Precision95%, Recall 100%) and for the Multiclass Decision Forest 0.89, (Acuracy 80% Precision 87% Recall 80 %)

26 Discussion The twoclass boosted decision tree ensemble approach is influenced by learning where the second tree justifies the first tree error, the third tree justifies the 1st, 2nd and 4th errors. Prediction is based on all ensembles of all trees to make predictions. Decision Forest, this classification method works by building several decision trees and then choosing the most popular output class.

27 CONCLUSION Improving Text Summarization Using ANFIS

28 Conclusion Results show that the Two-Class Boosting Decision Tree has a better performance than the Multiclass Decision Forest in predicting the final outcome of evaluating banking performance on accuracy, precision, recall and AUC. Using the Twoclass Boosting Decision Tree to predict the evaluation and performance of the banking industry can provide more accurate management decisions.

29 REFERENCE Improving Text Summarization Using ANFIS

30 REFERENCE 1.Kumar, Y. J., Goh, O. S., Halizah, B., Ngo, H. C., & Puspalata, C.: A Review On Automatic Text Summarization Approaches. Journal of Computer Science, vol. 12, no. 4, pp. 178-190. (2016) 2.Khan, A., Salim, N., & Kumar, Y. J.: Genetic semantic graph approach for multi-document abstractive summarization. In Fifth International Conference on Digital Information Processing and Communications (ICDIPC), pp. 173-181. (2015). 3.Keyan, M. and Srinivasagan, K.: Multi-Document and Multi-Lingual Summarization using Neural Networks. International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C) pp. 11-14. (2012) 4.Patil, M. P. D., & Kulkarni, N. J.: Text Summarization Using Fuzzy Logic. International Journal of Innovative Research in Advanced Engineering (IJIRAE), vol. 1, no. 3, pp. 42-45. (2014) 5.Rucha, S. and Apte, S.: Improvement of Text Summarization using Fuzzy Logic Based Method OSR Journal of Computer Engineering (IOSRJCE), vol. 5, no. 6, pp. 5-10. (2012) 6.Megala, S. S., Kavitha, A., & Marimuthu, A.: Enriching Text Summarization using Fuzzy Logic. International Journal of Computer Science and Information Technology, vol. 5, pp. 863- 867. (2014)

31 REFERENCE Sarda, A. and Kulkarni, A.: Text Summarization Using Neural Network and Rhetorical Structure Theory. International Journal of Advanced Research in Computer and Communication Engineering, IJARCCE, vol. 4, no. 6, pp. 49-52. (2015) Suanmali, L., Salim, N., & Binwahlan, M. S.: Fuzzy logic based method for improving text summarization. Int. J. Comput. Sci. Inf. Secur., vol. 2, no. 1. (2009) Kumar, Y. J., Salim, N., Abuobieda, A., & Albaham, A. T.: Multi document summarization based on news components using fuzzy cross-document relations. Applied Soft Computing, vol. 21, pp. 265-279. (2014) Babar, S. A., & Patil, P. D.: Improving Performance of Text Summarization. Procedia Computer Science, vol. 46, pp. 354-363. (2015) Albertos, P., & Sala, A.: Fuzzy logic controllers. Advantages and drawbacks. In VIII International Congress of Automatic Control, vol. 3, pp. 833-844. (1998) Fattah, M. A., & Ren, F.: Automatic text summarization. World Academy of Science, Engineering and Technology, vol. 13, pp. 192-195. (2008)

32 REFERENCE Loganathan, C., & Girija, V.: Investigations on Hybrid Learning in ANFIS. International Journal of Engineering Research & Applications, vol. 4, no. 10, pp. 31-37. (2014) Jiang, J. J., & Conrath, D. W.: Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference Research on Computational Linguistics, pp. 1-15. (1997) Lim, E. A., & Jayakumar, Y.: A study of neuro-fuzzy system in approximation-based problems. Matematika, vol. 24, pp. 113-130. (2008) Moh'd Arikat, Y.: Subtractive Neuro-Fuzzy modeling techniques applied to short essay auto-grading problem. In International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 889-895. (2012)

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