Development of a Machine Learning-Based Financial Risk Control System

With the gradual end of the COVID-19 outbreak and the gradual recovery of the economy, more and more individuals and businesses are in need of loans. This demand brings business opportunities to various financial institutions, but also brings new risks. The traditional loan application review is mostly manual and relies on the business experience of the auditor, which has the disadvantages of not being able to process large quantities and being inefficient. Since the traditional audit processing method is no longer suitable some other method of reducing the rate of non-performing loans and detecting fraud in applications is urgently needed by financial institutions. In this project, a financial risk control model is built by using various machine learning algorithms. The model is used to replace the traditional manual approach to review loan applications. It improves the speed of review as well as the accuracy and approval rate of the review. Machine learning algorithms were also used in this project to create a loan user scorecard system that better reflects changes in user information compared to the credit card systems used by financial institutions today. In this project, the data imbalance problem and the performance improvement problem are also explored

Solvability of a Coupled System of Fractional Differential Equations with Periodic Boundary Conditions at Resonance

By using the coincidence degree theory, we study the existence of solutions for a coupled system of fractional differential equations with periodic boundary conditions. A new result on the existence of solutions of the indicated fractional boundary-value problem is obtained.Із використанням теорії збігу степенів досліджено існування розв'язків зв'язаних систем диференціальних рівнянь дробового порядку з періодичними граничними умовами. Встановлено новий результат щодо існування розв'язків граничної задачі дробового порядку

The longitudinal association between possible new sarcopenia and the depression trajectory of individuals and their intimate partners

BackgroundIt is currently unknown whether the dynamic nature of depression affects the development of sarcopenia. Herein, this study aims to assess the association between possible new sarcopenia and the depression trajectory of individuals and their intimate partners through a 4-year longitudinal cohort study.MethodsOur study included 784 pairs of individuals without possible sarcopenia and their spouses from the China Health and Retirement Longitudinal Study (CHARLS) 2011. All individuals and their spouses received three assessments of the Center for Epidemiologic Studies Depression 10-item (CESD-10) scale in 2011, 2013, and 2015. According to the diagnostic algorithm recommended by the Asian Working Group for Sarcopenia (AWGS) 2019, we evaluated the incidence of possible sarcopenia in individuals in 2015. Latent class analysis (LCA) was used to identify a longitudinal depression trajectory of individuals and their spouses during a 4-year follow-up. Subsequently, we assessed the relationship between possible sarcopenia and depression trajectory using three generalized additive models.ResultsIn 2015, 24.87% (195/784) of individuals were diagnosed with possible sarcopenia. LCA identified five depression trajectories: a persistently high risk of depression in individuals and their spouses (reference; class 1 = 34 [4.3%]); a persistently low risk of depression in individuals and their spouses (class 2 = 526 [67.1%]); a high risk of depression in individuals and a low risk of depression in spouses (class 3 = 46 [5.9%]); a low risk of depression in individuals and a high risk of depression in spouses (class 4 = 116 [14.8%]); and a reduced risk of depression in individuals and their spouses (class 5 = 62 [7.9%]). The highest incidence of possible sarcopenia was shown in class 1, followed by classes 3 and 5. Classes 2 (adjusted relative risk (RR) = 0.44, 95% confidence interval (CI): 0.20–0.97) and 4 (adjusted RR = 0.40, 95%CI: 0.17–0.96) had a significantly lower incidence of possible sarcopenia than class 1. Subgroup analysis demonstrated that the incidence of possible sarcopenia in class 4 was obviously higher in women (38.89%) than in men (18.4%).ConclusionsOur study indicates a persistently high risk of depression in individuals to develop possible sarcopenia. In addition, a persistently high risk of depression in intimate partners potentially increases the risk of possible new sarcopenia, especially in female individuals who are at low risk of depression