| Title |
Classification of incoming payments / |
| Translation of Title |
Įeinančių mokėjimų klasifikavimas. |
| Authors |
Kolesnikova, Julija |
| Full Text |
|
| Pages |
58 |
| Keywords [eng] |
incoming payments, machine learning, classification, text analysis, Natural Language Processing |
| Abstract [eng] |
Classification of the incoming payments to the private customers of the bank is essential for customer behaviour and income analysis. However, more often banks classify the outgoing transactions and only specific categories of customer incomes. This thesis proposes possible categories of incoming transactions and investigates how do different algorithms perform at classification. There were manually obtained 21 new class of customer incomes. The Support Vector Machine, Naïve Bayes and Gradient Boosting classification algorithms show promising results and can be implemented in the bank instead of rule-based approach. The main focus in the future work is to reduce a noise in the textual data by automatic correction of mistakes in the transaction description. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2021 |
