Browsing by Author "Sayar, Alperen"

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Customer Segmentation and Churn Prediction via Customer Metrics
(IEEE, 2022) Bozkan, Tunahan; Cakar, Tuna; Sayar, Alperen; Ertugrul, Seyit
In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data-driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) was carried out. It was estimated by the XGBoost model with an F1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customers who will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.
Citation - Scopus: 1
Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making
(Ieee, 2024) Filiz, Gozde; Son, Semen; Sayar, Alperen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna
Functional near-infrared spectroscopy (fNIRS) has seen increasingly widespread use in examining brain activity and cognitive processes. However, the existing literature provides insufficient information on distinguishing between different decision-making mechanisms. This study explores the application of fNIRS in differentiating between two distinct decision-making processes: third-party punishment decisions and credit decisions. The research includes analyzing fNIRS data collected during these processes and classifying the associated neural patterns using machine learning. The findings reveal that fNIRS, in conjunction with ML, holds substantial potential to enhance the depth of understanding of decision-making processes in neuroscience research.
Citation - Scopus: 2
Emg-Based Bci for Picar Mobilization
(IEEE, 2022) Yilmaz, Yasin; Günden, Burak Bahri; Ertekin, Efe; Sayar, Alperen; Çakar, Tuna; Arslan, Şefik Şuayb
In this study, the main scope was to develop a brain-computer interface (BCI) with the use of PiCar and EEG/ERP devices. Thus, it is aimed to facilitate the lives of people with certain diseases and disabilities. The ultimate goal of this project has been to direct and control a BCI-based PiCar concerning the signals captured via the EEG/ERP device. With the EEG headset, the EMG signals of the gestures (facial expressions) of the participant were captured. With the collected data, filtering and other preprocessing methods were applied to have noise-free signals. In the preprocessing, the detrending method was used to clean the data set which showed a constantly increasing trend, to a certain range, and zero trends. The denoising (Wavelet Denoising) and outlier detection/elimination methods (OneClassSVM) were used for noise elimination. The SMOTE oversampling method was used for data augmentation. Welch's method was used to get band powers from the signals. With the use of augmented data, several machine learning algorithms were applied such as Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, Random forest Classifier, Gradient Boosting Classifier, Multinomial Naive Bayes, Decision tree, K-Nearest Neighbor, and voting classifier. The developed models were used to predict the direction that is passed as an input to PiCar's API. After that, PiCar was controlled concerning the predicted direction with HTTP GET requests. In this project, the OpenBCI headset and the Brainflow library for EEG/EMG signal obtaining and processing were used. Also, the Tkinter library was used for the Graphical user interface and Django for establishing a server on PiCar's brain which is RaspberryPi. © 2022 IEEE.
Citation - Scopus: 1
Enhancing Quality Control in Plastic Injection Production: Deep Learning-Based Detection and Classification of Defects
(IEEE, 2023) Mutlu, İsmail; Çakar, Tuna; Aslan, Yeşim; Yıldız, Ahmet; Sayar, Alperen; Şimsek, Kamil; Tunalı, Mustafa Mert
This study investigates the applicability of diverse deep learning techniques in detecting and classifying defects within plastic injection manufacturing processes. The findings derived from the models yield several feasible solutions that hold potential practical implications. Notably, the implementation of the Xception model as a classification framework presents a potential domain for enhancing quality control procedures. The developed models, trained on the prepared data sets, provide compelling evidence for the potential utilization of artificial intelligence technologies in the manufacturing industry. Consequently, this study represents a noteworthy contribution to the limited yet auspicious academic research in the field.
Fraud detection and prediction with machine learning applications
(MEF Üniversitesi, 2023) Sayar, Alperen; Çakar, Tuna
Bu çalışmanın temel amacı, faktoring sektöründe faaliyet gösteren bir şirketin müşterilerinin işlemleri üzerindeki dolandırıcılık faaliyetlerini tespit etmek ve buna bağlı olarak müşterilerin geçmiş işlem ve bağlantı verilerine dayalı keşifsel veri analizi ile ölçülebilir parametreler yakalamaktır. ve ardından hedef için tahmine dayalı modeller gerçekleştirmek. Sınıflandırma modeli algoritmaları olan XGBoost ve CATBoost modellerinde %79 civarında isabet oranı elde edilmiştir. Bu sayede dolandırıcılık yapma potansiyeli yüksek müşteri tespit edildikten sonra daha etkin, verimli ve doğru bir yaklaşımla hareket edilerek işlem bazında dolandırıcılık faaliyetlerinin doğrudan tespit edilmesi amaçlanmaktadır.
Citation - Scopus: 2
Grafraud: Fraud Detection Using Graph Databases and Neural Networks
(IEEE, 2023) Raina, Ajeet Singh; Çakar, Tuna; Ertuğrul, Seyit; Arslan, Şuayip; Sayar, Alperen
The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle Databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle Database to construct and deploy real-time systems that can efficiently detect fraudulent activities.
Citation - Scopus: 5
High-Performance Real-Time Data Processing: Managing Data Using Debezium, Postgres, Kafka, and Redis
(IEEE, 2023) Çakar, Tuna; Ertuğrul, Seyit; Arslan, Şuayip; Sayar, Alperen; Akçay, Ahmet
This research focuses on monitoring and transferring logs of operations performed on a relational database, specifically PostgreSQL, in real-time using an event-driven approach. The logs generated from database operations are transferred using Apache Kafka, an open-source message queuing system, and Debezium running on Kafka, to Redis, a non-relational (No-SQL) key-value database. Time-consuming query operations and read operations are performed on Redis, which operates on memory (in-memory), instead of on the primary database, PostgreSQL. This approach has significantly improved query execution performance, data processing time, and backend service performance. The study showcases the practical application of an event-driven approach using Debezium, Kafka, Redis, and relational databases for real-time data processing and querying.
Citation - Scopus: 1
Modeling Consumer Creditworthiness Via Psychometric Scale and Machine Learning
(IEEE, 2022) Çakar, Tuna; Ertugrul, Seyit; Sayar, Alperen; Sahin, Türkay; Bozkan, Tunahan
Although the predictive power of economic metrics to detect the creditworthiness of the customers is high, there is a rising interest in the integration of cognitive, psychological, behavioral, alternative, and demographic data into credit risk systems and processing the data through modern methods. The primary motivation for the rising interest is increased customer classification accuracy. In this research, customer creditworthiness was modeled through data consisting of personality, money attitudes, impulsivity, self-esteem, self-control, and material values and processed through artificial intelligence. The obtained findings have been evaluated as a reference point for the following research. © 2022 IEEE.
Performing Disc Personal Inventory Analysis in Job Postings Using Artificial Intelligence Methods
(Data science and applications, 2023) Sayar, Alperen; Yıldız, Ahmet; Çakar, Tuna; Şengüloğlu, Dilara; Ertuğrul, Seyit
One of the application fields of DISC selfevaluation analysis was introduced to predict people's performance and orientation in their working life. Each letter in the word DISC represents an essential personal characteristic, dividing the profiles of people in business life into four essential parts. In the current study, DISC analysis is conducted on job postings to match the person with the job posting. The current study was based on the analysis of 3 different datasets with job postings in English, Turkish and Romanian prepared by using web scraping methods and then labeled in accordance with DISC criteria. Several different machine learning algorithms have been performed on the DISC analysis outputs, and they reached the best results with accuracy values of around over 96% on the English dataset, around over 95% on the Turkish dataset, and around over 96% on the Romanian dataset, for both D, I, S, C models.
Predicting Credit Repayment Capacity With Machine Learning Models
(Ieee, 2024) Filiz, Gozde; Bodur, Tolga; Yaslidag, Nihal; Sayar, Alperen; Çakar, Tuna
This study examines the transformation in the financial services sector, particularly in banking, driven by the rapid development of technology and the widespread use of big data, and its impact on credit prediction processes. The developed credit prediction model aims to more accurately predict customers' credit repayment capacities. In pursuit of this goal, demographic and financial data along with credit histories of customers have been utilized to employ data preprocessing techniques and test various classification algorithms. Findings indicate that models developed with XGBoost and CATBoost algorithms exhibit the highest performance, while the effective use of feature engineering techniques is revealed to enhance the model's accuracy and reliability. The research highlights the potential for financial institutions to gain a competitive advantage in risk management and customer relationship management by leveraging machine learning models.
Prediction of Loan Decisions With Optical Neuroimaging (fnirs) and Machine Learning
(IEEE, 2023) Girişken, Yener; Son Turan, Semen; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen
The successful applications of neuroscientific methods and artificial learning approaches have increased in applied fields such as economics, marketing, and finance in the last decade. In this study, a prediction model was developed using the output of optical neuroimaging (fNIRS) measurements from the prefrontal brain regions while 40 participants made decisions for 35 credit offers. The aim was to predict participants' responses to credit offers using artificial learning methods based on four metrics obtained over time from the optical neuroimaging system. The findings of the study indicate that the first 6 seconds (prior to the response entry) are particularly critical. While the performance rate in the developed prediction models is found to be higher, especially in tree-based algorithms, this paper includes a performance comparison of 5 models specifically.
Citation - Scopus: 2
Segmentation for Factoring Customers: Using Unsupervised Machine Learning Algorithms
(IEEE, 2023) Yalçuva, Berat; Akçay, Ahmet; Ertuğrul, Seyit; Çakar, Tuna; Sayar, Alperen; Ayyıldız, Nur Seher
Nowadays the fact that technology facilitates data collection is an important opportunity, as well as making the management of all this data difficult and makes no sense unless it is well processed. This stored data is extremely important, and companies use data provided by their customers. Catching the needs of the customer profiles of the changing world is now a necessity and takes the first place for companies. With the increase in the amount of stored data over time, it has become difficult to establish a relationship between the data and to separate them from each other. At this point, machine learning methods have become more involved in our lives. In this study, what segmentation is and its change over the years are mentioned. It has been mentioned which machine learning techniques will be useful in data selection. Then, possible machine learning methods are shown in real life segmentation problem by using the domestic factoring company’s customer check data. Since this study aims to group unlabeled data, unsupervised learning techniques are emphasized. Among these methods, Hierarchical Clustering, DBSCAN, Gaussian Mixture Modeling methods, Fuzzy c- Means were used as well as the most popular K-Means algorithm. When the clustering results were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering could not produce expected results. It was observed that the best results were obtained with the K-Means and Fuzzy c - Means algorithms.
Citation - Scopus: 2
Ssqem: Semi-Supervised Quantum Error Mitigation
(IEEE, 2022) Sayar, Alperen; Arslan Suayb S.; Çakar Tuna
One of the fundamental obstacles for quantum computation (especially in noisy intermediate-scale quantum (NISQ) era) to be a near-term reality is the manufacturing gate/measurement technologies that make the system state quite fragile due to decoherence. As the world we live in is quite far away from the ideal, complex particle-level material imperfections due to interactions with the environment are an inevitable part of the computation process. Hence keeping the accurate state of the particles involved in the computation becomes almost impossible. In this study, we posit that any physical quantum computer sys-tem manifests more multiple error source processes as the number of qubits as well as depth of the circuit increase. Accordingly, we propose a semi-supervised quantum error mitigation technique consisting of two separate stages each based on an unsupervised and a supervised machine learning model, respectively. The proposed scheme initially learns the error types/processes and then compensates the error due to data processing and the projective measurement all in the computational basis. © 2022 IEEE.
Transaction Volume Estimation in Financial Markets With Lstm
(IEEE, 2023) Bozkan, Tunahan; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Akçay, Ahmet
In this study, it was aimed to determine the transaction volume that will be encountered in the future (hourly) in the factoring sector, and then to take financial and operational action early. For the study, the LSTM model, which is a kind of recurrent neural network (RNN) that can capture long and short-term dependencies, was applied by using data-driven approaches to estimate the check amounts of hourly transactions. As a result of the results, it was aimed to increase the operational efficiency in a broad scope by allowing the factoring company to determine the loan amounts to be obtained from banks in the most optimal way, and then to take early action within the scope of both the workforce and business management of the financial resource allocation management process and operational activities. MAPE score was used as a measure of error in the time series analysis model. MAPE scores were found as %5.05 for 30 days, %4.18 for 10 days, %3.47 for 5 days, %3.09 for 3 days and %1.83 for 1 day. According to the MAPE scores calculated for different days, the enterprise will be able to decide on the loan to be drawn from banks both in terms of time and amount, and the necessary action will be taken.
Citation - WoS: 1
Citation - Scopus: 1
Understanding the Psychological and Financial Correlates for Consumer Credit Use;
(Sosyoekonomi Society, 2024) Ertuğrul, Seyit; Sayar, Alperen; Şahin, Türkay; Çakar,Tuna
This study investigated the behavioural and cognitive predictors of consumer credit usage to develop a behavioural credit risk assessment procedure for a factoring company. Participants completed surveys measuring personality traits, self-esteem, material and monetary values, compulsive and impulsive buying tendencies, self-control, and impulsiveness. Financial surveys also assessed financial literacy and knowledge of financial concepts. The results indicated that extraversion, conscientiousness, emotional stability, and experiential self-control were significant predictors of consumer credit usage. These findings suggest that a finance company can use these personality traits and financial characteristics to develop a more accurate and effective credit risk assessment procedure, such as psychometric tests. © 2024, Sosyoekonomi Society. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
Unlocking the Neural Mechanisms of Consumer Loan Evaluations: an Fnirs and Mlbased Consumer Neuroscience Study
(2024) Girişken, Yener; Son, Semen; Demircioğlu, Esin Tuna; Filiz, Gözde; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen
This study conducted a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and artificial intelligence (AI). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. The study integrates fNIRS data with advanced AI algorithms, specifically Extreme Gradient Boosting, CatBoost, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. This interdisciplinary approach bridges neuroscience and finance, offering unprecedented insights into the neural mechanisms guiding financial choices. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.
Yapay Öğrenme Tabanlı Mikrofaktoring Skorlama Modeli ve Kredi Risk Yönetim Sistemi Geliştirilmesi
(Institute of Electrical and Electronics Engineers Inc., 2025) Sayar, Alperen; Ates, Yigit; Ertugrul, Seyit; Turan, Elif Naz; Drias, Yassine; Çakar, Tuna
Credit scoring systems are critical tools used by factoring institutions to assess the credit risks of SME businesses seeking microloans. This study presents a comprehensive predictive modeling framework that achieves 82.67% ROC-AUC with 65.34% Gini score on test data, demonstrating robust discriminative capability despite significant class imbalance. Our ensemble approach outperforms individual boosting models by leveraging their complementary strengths in payment behavior analysis and fraud detection. The raw data was cleaned, transformed, and optimized using the Polars library, with specialized features for detecting fraud patterns and time-based risk indicators. When implementing a score threshold of 950, our model significantly improves the detection of non-performing loans (NPL) compared to traditional rule-based approaches by reducing the net deficit from 6.59% to 2.62%. When applied to previously rejected applications, the model projects a potential 762.57% increase in transaction count and 747.05% growth in transaction volume. © 2025 Elsevier B.V., All rights reserved.