A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study on electronic banking satisfaction in Nigeria.
Study compares classification techniques to predict customer churn in banking.
The paper uses RFM and clustering to segment bank customers.
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
The study improves CLV predictions in retail banking with machine learning.
Study predicts customer data sharing in Open Banking and explains key factors.
Study clusters bank customers using LSTM and DTW.
Paper proposes a new topology for AML analysis using Poincaré embeddings.
The changing nature of the relationship between a retail bank and its customers is examined, particularly with respect to new financial concepts, debt and regulation. The traditional image of a bank is portrayed as a physical building a classical Doric portico. This image conveys concepts of service, soundness, strengt…
Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we adopt the Variational Autoencoder (VAE), which has the ability to learn latent rep…
Study uses AI techniques to predict bank customer solvency.
Study evaluates sustainability of European banks using a new model.
Paper introduces a specialized text classification system for French Open Banking transactions.
Study examines European banks' digital transformation strategies.
Bayesian and simulation methods predict credit default probabilities.
Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.
ML system reduces overdraft fees for Mint users.
Commercial banks and other depository institutions in some countries are required to hold in reserve against deposits made by their customers at their Central Bank or Federal Reserve. Although some countries have been eliminated it, this requirement is useful as one of many Central Bank's regulation made to control rat…
New GNN method detects money laundering in diverse customer relationships.
We propose a useful approach for investigating the statistical properties of foreign currency exchange rates. Our approach is based on queueing theory, particularly, the so-called renewal-reward theorem. For the first passage processes of the Sony Bank US dollar/Japanese yen (USD/JPY) exchange rate, we evaluate the ave…
One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borr…
This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a new hope in researches of finding the proving method of comparisons of various credit scoring techniques. There is analyzed the influence of one cyclic macro--economic variable …
XGBoost predicts bank loan defaults with improved accuracy.
Model assesses credit risk using behavioral data from Experian and Bank of Italy.
Develops a framework to assess systemic risk in the economy using bank-firm network data.
Study shows non-systematic bias in customer satisfaction surveys limits data value.
Identifying customer segments in retail banking portfolios with different risk profiles can improve the accuracy of credit scoring. The Variational Autoencoder (VAE) has shown promising results in different research domains, and it has been documented the powerful information embedded in the latent space of the VAE. We…
The abstract covers various aspects of eBusiness and eGovernment, including digital currencies, m-government services, gender inclusivity, eLearning, export performance, SME digitalization, and banking customer behavior.
The study examines how social biases are reinforced in machine learning models used for credit scoring.
We evaluate the average waiting time between observing the price of financial markets and the next price change, especially in an on-line foreign exchange trading service for individual customers via the internet. Basic technical idea of our present work is dependent on the so-called renewal-reward theorem. Assuming th…
The global financial system has become highly connected and complex. Has been proven in practice that existing models, measures and reports of financial risk fail to capture some important systemic dimensions. Only lately, advisory boards have been established in high level and regulations are directly targeted to syst…
Digital Financial Services continue to expand and replace the delivery of traditional banking services to the customers through innovative technologies to meet the growing complex needs and globalization challenges. These diversified digital products help the organizations (service providers) to improve their firm perf…
PerfGD solves model-induced data shifts by finding optimal points.
Investors usually resort to financial advisors to improve their investment process until the point of complete delegation on investment decisions. Surely, financial advice is potentially a correcting factor in investment decisions but, in the past, the media and regulators blamed biased advisors for manipulating the ex…
Model predicts Chinese stock market liquidity and customer order behavior.
This paper presents the first topological analysis of the economic structure of an entire country based on payments data obtained from Swedbank. This data set is exclusive in its kind because around 80% of Estonia's bank transactions are done through Swedbank, hence, the economic structure of the country can be reconst…
In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number …
An asset network systemic risk (ANWSER) model is presented to investigate the impact of how shadow banks are intermingled in a financial system on the severity of financial contagion. Particularly, the focus of this study is the impact of the following three representative topologies of an interbank loan network betwee…
The study compares profitability of conventional and Islamic banks in Bangladesh.
Paper proposes a fair stock trading strategy using multi-agent reinforcement learning.
A new algorithm adapts to changing user behaviors in finance.
The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit…
Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confide…
This paper examines SVB's failure and its impact on bank stocks.
We report a study of a stylized banking cascade model investigating systemic risk caused by counter party failure using liabilities and assets to define banks' balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to d…
We consider the problem of governing systemic risk in an assets-liabilities dynamical model of banking system. In the model considered each bank is represented by its assets and its liabilities.The capital reserves of a bank are the difference between assets and liabilities of the bank. A bank is solvent when its capit…
This study uses high-frequency data to identify early warning signals for bank crises.
Study uses RL to optimize credit card limits, achieving better results than traditional methods.