عنوان مقاله :
شناسايي عوامل موثر بر مطالبات غيرجاري بانك‌ها با استفاده از شبكه‌هاي عصبي و الگوريتم ماشين بردار پشتيبان
عنوان به زبان ديگر :
Identifying Factors Affecting Non-curent Debts of Banks Using Neural Networks and Support Vector Machine Algorithm
پديد آورندگان :
كردمنجيري، سجاد دانشگاه آزاد اسلامي واحد بابل - گروه حسابداري , داداشي، ايمان دانشگاه آزاد اسلامي واحد بابل - گروه حسابداري , خوشنود، زهرا ﺑﺎﻧﮏ ﻣﺮﮐﺰي - ﭘﮋوﻫﺸﮑﺪه ﭘﻮﻟﯽ و ﺑﺎﻧﮑﯽ - ﮔﺮوه ﺑﺎﻧﮑﺪاري , غلام نيا روشن، حميدرضا دانشگاه آزاد اسلامي واحد بابل - گروه حسابداري
كليدواژه :
تسهيلات بانكي , مطالبات غيرجاري , ماشين بردار پشتيبان , شبكه عصبي
چكيده فارسي :
هدف اﯾﻦ ﻣﻘﺎﻟﻪ ﺷﻨﺎﺳﺎﯾﯽ ﻋﻮاﻣﻞ ﺗﺎﺛﯿﺮﮔﺬار ﺑﺮ اﯾﺠﺎد و اﻓﺰاﯾﺶ ﻣﻄﺎﻟﺒﺎت ﻏﯿﺮﺟﺎري ﺑﺮاي اﺗﺨﺎذ ﺗﺼﻤﯿﻢ ﻣﻨﺎﺳﺐﺗﺮ در اﻋﻄﺎي ﺗﺴﻬﯿﻼت اﺳﺖ. ﺑﺪﯾﻦ ﻣﻨﻈﻮر ﺑﺮاي اﻧﺘﺨﺎب ﻣﺘﻐﯿﺮﻫﺎي ﻣﻮﺛﺮ، از اﻟﮕﻮرﯾﺘﻢﻫﺎي ﺗﺠﺰﯾﻪ و ﺗﺤﻠﯿﻞ ﻣ ﻔﻪ ﻫﺎي ﻫﻢ ﺑﺴﺘﮕﯽ و ﻻﺳﻮ و ﺑﺮاي ﮐﻼس ﺑﻨﺪي ﻧﻤﻮﻧﻪﻫﺎ، از ﺷﺒﮑﻪﻫﺎي ﻋﺼﺒﯽ و ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن اﺳﺘﻔﺎد ﺷﺪه اﺳﺖ. در اﯾﻦ ﭘﮋوﻫﺶ، ﻧﻤﻮﻧﻪاي از 660 ﻣﺸﺘﺮي ﺣﻘﻮﻗﯽ ﺑﺎﻧﮏ ﺳﭙﻪ ﺑﺮاي ﺳﺎل ﻫﺎي 1385-1396 اﻧﺘﺨﺎب ﺑﺮ ﻣﺘﻐﯿﺮﻫﺎي ﺧﺼﻮﺻﯿﺘﯽ ﻣﺴﺘﺨﺮج از ﻗﺮاردادﻫﺎي ﺗﺴﻬﯿﻼﺗﯽ اﯾﻦ ﻣﺸﺘﺮﯾﺎن در ﮐﻨﺎر ﻣﺘﻐﯿﺮﻫﺎي ﻣﺎﻟﯽ، ﻏﯿﺮﻣﺎﻟﯽ، ﺣﺴﺎﺑﺮﺳﯽ و اﻗﺘﺼﺎدي ﺗﻤﺮﮐﺰ ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد اﻟﮕﻮرﯾﺘﻢ ﻻﺳﻮ ﺑﺎ ﺗﻤﺮﮐﺰ ﺑﺮ ﻣﺘﻐﯿﺮﻫﺎي ﻣﺎﻟﯽ، اﻗﺘﺼﺎدي
ﺣﺴﺎﺑﺮﺳﯽ، ﻋﻤﻠﮑﺮد ﺑﻬﺘﺮي ﻧﺴﺒﺖ ﺑﻪ اﻟﮕﻮرﯾﺘﻢ ﺗﺠﺰﯾﻪ و ﺗﺤﻠﯿﻞ ﻣﻮﻟﻔﻪﻫﺎي ﻫﻢ ﺳﺎﯾﮕﯽ داﺷﺘﻪ و ﺑﺮاﺳﺎس اﯾﻦ اﻟﮕﻮرﯾﺘﻢ، 10 ﻣﺘﻐﯿﺮ ﮐﻠﯿﺪي ﺗﺎﺛﯿﺮﮔﺬار ﺑﺮ ﻣﻄﺎﻟﺒﺎت ﻏﯿﺮﺟﺎري ﺷﻨﺎﺳﺎﯾﯽ ﺷﺪﻧﺪ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻋﻤﻠﮑﺮد ﺑﻬﺘﺮ ﺎﺷﯿﻦ ﻫﺎي ﺑﺮدار ﭘﺸﺘﯿﺒﺎن ﺑﺎ ﻫﺴﺘﻪ ﺷﻌﺎﻋﯽ، اﺳﺘﻔﺎده از آن در ﻣﺪل ﺳﺎزي ﻣﻄﺎﻟﺒﺎت ﻏﯿﺮﺟﺎري ﭘﯿﺸﻨﻬﺎد ﻣﯽ ﺷﻮد.
چكيده لاتين :
The main purpose of this paper is to identify the factors influencing the creation and increase of non-current debts to make a more appropriate decision in granting facilities. For this purpose, to select effective variables, from the analysis algorithms of correlation and Lasso components; and to classify the samples, neural networks and support machine were used. In this study, a sample of 660 legal customers of Sepah Bank for the years 2006-2017 was selected and focused on the characteristic variables extracted from the facility contracts of these customers along with financial, non-financial, auditing and economic variables. The results showed that the Lasso algorithm focused on financial, economic and auditing variables, performed better than the neighboring component analysis algorithm, and based on this algorithm, 10 key variables affecting non-current debts were identified. Due to the better performance of support vector machines with radial cores, its use in modeling non-current debts is recommended.
عنوان نشريه :
مدلسازي اقتصادي
