ارائه مدل تركيبي الگوريتم مورچگان باينري و ماشين بردار پشتيبان (BACO-SVM) براي انتخاب ويژگي و طبقه‌بندي مشتريان بانكي به همراه مطالعه موردي

Hybrid Model Binary ant Colony Algorithm and Support Vector Machine (BACO-SVM) for Feature Selection and Classification of Bank Customers with Case Study

ﯾﮑﯽ از ﻣﻬﻢﺗﺮﯾﻦ ﻣﺴﺎﺋﻠﯽ ﮐﻪ ﻫﻤﻮاره ﺑﺎﻧﮏﻫﺎ و ﻣﺆﺳﺴﺎت ﻣﺎﻟﯽ ﺑﺎ آن ﻣﻮاﺟﻪ ﻫﺴﺘﻨﺪ، ﻣﺴﺌﻠﻪ رﯾﺴﮏ اﻋﺘﺒﺎري ﻣﯽﺑﺎﺷﺪ. رﻗﻢ ﻗﺎﺑﻞﺗﻮﺟﻪ ﻣﻄﺎﻟﺒﺎت ﻣﻌﻮق ﺑﺎﻧﮏﻫﺎ در ﺳﺮاﺳﺮ ﺟﻬﺎن ﻧﺸﺎندﻫﻨﺪه اﻫﻤﯿﺖ اﯾﻦ ﻣﻮﺿﻮع و ﻟﺰوم ﺗﻮﺟﻪ ﺑﻪ آن ﻣﯽﺑﺎﺷﺪ. ازاﯾﻦرو ﺗﺎﮐﻨﻮن ﺗﻼشﻫﺎي ﺑﺴﯿﺎري ﺑﻪﻣﻨﻈﻮر اراﺋﻪ ﻣﺪﻟﯽ ﮐﺎرا ﺟﻬﺖ ارزﯾﺎﺑﯽ و ﻃﺒﻘﻪﺑﻨﺪي ﻫﺮ ﭼﻪ دﻗﯿﻖﺗﺮ ﻣﺘﻘﺎﺿﯿﺎن ﺗﺴﻬﯿﻼت اﻋﺘﺒﺎري ﺻﻮرت ﮔﺮﻓﺘﻪ اﺳﺖ. در اﯾﻦ راﺳﺘﺎ، ﭘﮋوﻫﺶ ﺣﺎﺿﺮ ﺳﻌﯽ در اراﺋﻪ روﯾﮑﺮدي ﻧﻮ ﺑﺮاي ارزﯾﺎﺑﯽ رﯾﺴﮏ اﻋﺘﺒﺎري ﻣﺸﺘﺮﯾﺎن ﺑﺎﻧﮑﯽ دارد. روش ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن )SVM( ﺑﻪﻋﻨﻮان ﻃﺒﻘﻪﺑﻨﺪي ﮐﻨﻨﺪهي اﺻﻠﯽ ﺑﺎ ﯾﮏ روش اﻧﺘﺨﺎب وﯾﮋﮔﯽ ﺑﻪ ﻧﺎم اﻟﮕﻮرﯾﺘﻢ ﻣﻮرﭼﮕﺎن ﺑﺎﯾﻨﺮي )BACO-SVM( ﺗﺮﮐﯿﺐ ﻣﯽﮔﺮدد. ﺑﻪﻣﻨﻈﻮر ﻧﺸﺎن دادن اﺛﺮﺑﺨﺸﯽ روش ﭘﯿﺸﻨﻬﺎدي از دادهﻫﺎي ﻣﺮﺑﻮط ﺑﻪ 85 ﺷﺮﮐﺖ از ﺗﺴﻬﯿﻼت ﮔﯿﺮﻧﺪﮔﺎن ﺣﻘﻮﻗﯽ ﯾﮏ ﺑﺎﻧﮏ اﯾﺮاﻧﯽ در ﯾﮏ ﺑﺎزهي 5 ﺳﺎﻟﻪ )1393-1389( ﺑﻪ ﻫﻤﺮاه 16 وﯾﮋﮔﯽ ﻣﺮﺑﻮط ﺑﻪ ﻫﺮ ﯾﮏ از آنﻫﺎ اﺳﺘﻔﺎده ﻧﻤﻮدهاﯾﻢ. ﻧﺘﺎﯾﺞ روش BACO-SVM ﺑﺎ روش GA-SVM ،PSO-SVM و روش SVM ﺑﻪﺗﻨﻬﺎﯾﯽ ﻣﻘﺎﯾﺴﻪ ﮔﺮدﯾﺪه اﺳﺖ. ﯾﺎﻓﺘﻪﻫﺎي ﭘﮋوﻫﺶ دﻻﻟﺖ ﺑﺮ آن داﺷﺘﻪ ﮐﻪ در ارزﯾﺎﺑﯽ رﯾﺴﮏ اﻋﺘﺒﺎري، ﻣﺪل BACO-SVM ﻧﺴﺒﺖ ﺑﻪ روشﻫﺎي دﯾﮕﺮ از ﻋﻤﻠﮑﺮد ﺧﻮﺑﯽ ﺑﺮﺧﻮردار اﺳﺖ. درﻧﺘﯿﺠﻪ ﺑﺎ اﺳﺘﻔﺎده از روش BACO-SVM ﺑﻪ ﻃﺒﻘﻪﺑﻨﺪي ﻣﺸﺘﺮﯾﺎن ﺑﻪ دو ﮔﺮوه ﻣﺸﺘﺮﯾﺎن ﺧﻮشﺣﺴﺎب و ﺑﺪﺣﺴﺎب ﻣﯽ ﭘﺮدازﯾﻢ؛ و درﻧﻬﺎﯾﺖ ﺟﻬﺖ اﻓﺰاﯾﺶ اﻧﻌﻄﺎفﭘﺬﯾﺮي در ﺗﺼﻤﯿﻢﮔﯿﺮي، ﻣﺸﺘﺮﯾﺎن ﺧﻮشﺣﺴﺎب را ﺑﺎ اﺳﺘﻔﺎده از روش VIKOR رﺗﺒﻪﺑﻨﺪي ﻣﯽﮐﻨﯿﻢ. اﯾﻦ رﺗﺒﻪﺑﻨﺪي ﻣﻨﺠﺮ ﺑﻪ آن ﻣﯽ ﺷﻮد ﮐﻪ ﻗﻀﺎوت دﻗﯿﻖ ﺗﺮي درﺑﺎرهي وﺿﻌﯿﺖ رﯾﺴﮏ اﻋﺘﺒﺎري ﻣﺘﻘﺎﺿﯿﺎن ﺧﻮشﺣﺴﺎب ﺻﻮرت ﮔﯿﺮد.

One of the most important issues faced by banks and financial institutions is the issue of credit risk. The significant amount of deferred bank claims around the world indicates the importance of this issue and the need to pay attention to it. So far, many efforts have been made to provide an effective model for evaluating and classification credit applicants as accurately as possible. In this regard, the present study attempts to provide a new approach for assessing the credit risk of bank customers. The support vector machine(SVM) method is combined as the main classifier of banking customers, with a feature selection method called the Binary Ant Colony Optimization Algorithm(BACO-SVM). In order to demonstrate the effectiveness of the proposed method, we used data from 85 companies from legal recipients of facilities of an Iranian bank in a 5 year interval (1393-1893) along with 16 characteristics related to each of them. The results of the BACO-SVM method have been compared with the PSO-SVM, GA-SVM, and SVM method. The results of the research indicated that BACO-SVM model has better performance in assessing credit risk rather than other methods. As the result, using the BACO-SVM method, we classify customers into two groups of good and bad account customers. Finally, in order to increase the flexibility in decision making, we will rank our good account customers with the VIKOR method. This rating will lead to a more accurate assessment of the credit risk situation of good account applicants.