Data mining groups bank clients for anti-money laundering.
problem Detecting money laundering through client profiling.
method Agent-based architecture, clustering, classification rules.
result Generated rules for signaling suspicious transactions.
On-device research index
arXiv research
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.
169,341 papers · 148 categories
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48 results for “bank clients”
CPOPT-Net predicts sparse client actions in banking using tensor decomposition and neural networks.
problem Predicting sparse client activities in the banking environment with evolving regulations.
method Combines CP tensor decomposition and neural networks for time series predictions.
result CPOPT-Net achieves accurate predictions of clients' financial activities.
Graph neural network predicts new bank client interactions using transaction data.
problem Predicting new interactions in the network of bank clients.
method Proposes a graph neural network model that uses both network topology and time-series data.
result The model outperforms existing approaches in link prediction and credit scoring.
Client appraisal improves efficiency in microfinance banks in Adamawa State.
problem Increasing loan defaults and losses in microfinance institutions.
method Survey method with primary and secondary data collection, multi-stage sampling, questionnaires, descriptive and inferential statistics.
result Client appraisal positively affects efficiency and productivity.
EWS-GCN improves credit scoring by analyzing money transfer connections.
problem Improving credit scoring in transactional banking data.
method Edge Weight-Shared Graph Convolutional Network (EWS-GCN) combining graph and recurrent neural networks.
result EWS-GCN outperforms state-of-the-art models in credit scoring.
Bank transactions help predict macroeconomic indexes faster and more accurately.
problem Lag in macroeconomic index availability and autoregressive models' limitations in complex scenarios.
method Use financial transactions data to estimate macroeconomic indexes using neural networks and smart sampling.
result Neural network approach outperforms baseline methods on hand-crafted features based on transactions.
Client2vec automates banking data analysis tasks.
problem Efficient data analysis and automation for banking applications.
method Marginalized stacked denoising autoencoders on transaction data.
result Vector embeddings represent client behaviors for various tasks.
Prime Match protects client stock trades from market price manipulation.
problem Protecting client stock trades from market price manipulation.
method Prime Match uses a two-round secure linear comparison protocol to match orders without revealing information.
result Prime Match reduces market impact and maintains client privacy.
Behavioural effects on XVAq-fin.PR
Bank behavior affects XVA pricing by influencing counterparty independence assumptions.
problem XVA pricing assumes counterparty independence, but bank behavior complicates this assumption.
method Developed a theoretical framework considering bank behavior and anonymous counterparties.
result Bank behavior requires inclusion of multiple CVA costs and affects KVA and FVA.
The paper examines how banks charge a KVA to clients under stricter capital requirements.
problem Impact of stricter capital requirements on OTC transaction valuations.
method Optimization using indifference pricing approach, considering both bank and shareholder perspectives.
result The study finds that charging a KVA can affect the profit and loss distribution of transactions.
Paper proposes a fair stock trading strategy using multi-agent reinforcement learning.
problem Unfair stock trading strategies lead to long-term losses for companies.
method Multi-agent reinforcement learning system to balance revenue and fairness.
result The system optimizes individual revenue while maintaining fairness.
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 …
A competing market model with a polyvariant profit function that assumes "zeitnot" stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An associated Markov process method for finding an optimal choice strategy for monovariant…
New algorithm APHEN improves tensor decomposition for mobile banking user-device authentication.
problem Enhancing user-device authentication in mobile banking for financial services.
method Tensor decomposition using Paratuck2 and APHEN algorithm for faster and more accurate computation.
result Improved user-device authentication for financial services through faster and more accurate tensor decomposition.
Personalized adaptation improves building-specific energy forecasting.
problem Building-specific temporal behavior is lost in fully shared adapters, and local adaptation discards cross-building transfer.
method Proposes a personalized federated sparse adaptation framework with a heterogeneous temporal mixture-of-experts (MoE) adapter.
result Personalization consistently outperforms Global FL-MoE and Local MoE across 50 buildings and three TSFM backbones.
A stock loan is a contract whereby a stockholder uses shares as collateral to borrow money from a bank or financial institution. In Xia and Zhou (2007), this contract is modeled as a perpetual American option with a time varying strike and analyzed in detail within a risk--neutral framework. In this paper, we extend th…
French bank uses corporate transaction data to predict credit default risk better than traditional methods.
problem Predicting credit default risk of enterprises using financial ratios and transaction data.
method Advanced machine learning methods applied to transaction data.
result Transaction data outperforms traditional financial ratios in predicting credit default risk.
This paper reviews statistical and machine learning methods for anti-money laundering.
problem Lack of scientific literature on statistical and machine learning methods for anti-money laundering.
method Client risk profiling and suspicious behavior flagging.
result Client risk profiling involves diagnostics, while suspicious behavior flagging involves non-disclosed features and hand-crafted risk indices.
Develops a machine-learning framework for optimal share repurchase hedging.
problem Challenges in hedging share repurchase programs due to market regulations and trading activity.
method Machine-learning framework that optimizes execution and hedging of share repurchase programs.
result Substantial performance improvements and an optimized hedging approach.
MQLV uses Q-learning to optimize money management in retail banking.
problem Optimizing money management in retail banking for personalized credit limits and loan applications.
method Modified Q-learning applied to Vasicek model for mean reverting processes.
result Unlock new decision-making processes in retail banking with MQLV.
New DL model handles missing data in biomedical datasets.
problem Handling missing data in modern biomedical datasets.
method Proposed a new DL architecture, dlglm, for generalized linear models.
result Outperforms existing methods in MNAR missingness scenarios.
PrivacyFL simulates privacy-preserving federated learning.
problem Ensuring privacy in federated learning environments.
method Extensible, configurable simulator for federated learning.
result PrivacyFL checks feasibility and improves model accuracy.
A market fix serves as a benchmark for foreign exchange (FX) execution, and is employed by many institutional investors to establish an exact reference at which execution takes place. The currently most popular FX fix is the World Market Reuters (WM/R) 4pm fix. Execution at the WM/R 4pm fix is a service offered by FX b…
Client adaptation improves federated learning performance with non-IID data.
problem Improving model performance in federated learning with non-identically and non-independently distributed data.
method Simulates heterogeneous clients to learn client-specific conditioning using a conditional gated activation unit.
result Client adaptation enhances model performance across balanced and imbalanced data sets from audio and image domains.
FedSTaS stratifies and samples clients for efficient FL.
problem Inefficient client sampling in federated learning.
method Stratifies clients based on compressed gradients, uses Neyman allocation for sampling, and samples local data uniformly.
result FedSTaS achieves higher accuracy than FedSTS in fixed training rounds.
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
This paper analyzes and improves convergence in federated learning with biased client selection.
problem Analyzing convergence in federated learning with biased client selection.
method First convergence analysis of federated optimization for biased client selection strategies, proposing Power-of-Choice framework.
result Power-of-Choice strategies converge up to 3 times faster and give 10% higher test accuracy than random selection.
Proposes client-level differential privacy for federated learning.
problem Differential privacy attacks on federated learning.
method Client-side differential privacy preserving federated optimization.
result Empirical studies show maintaining client-level differential privacy at minimal cost in model performance.
This paper optimizes brokerage contracts for multiple clients trading a single asset.
problem Optimizing brokerage contracts for multiple clients trading a single asset.
method Endogenously determines clients' reservation values and strategically chooses clients. Characterizes optimal portfolios computationally.
result Characterizes optimal portfolios of clients and their profits, showing dependence on price impact coefficients.
Active Federated Learning selects clients to maximize efficiency.
problem Minimizing bandwidth usage and maximizing model accuracy in federated learning.
method Clients are selected with a probability conditioned on the current model and client data to maximize efficiency.
result Reduces the number of required training iterations by 20-70% while maintaining the same model accuracy.
Despite the availability of very detailed data on financial market, agent-based modeling is hindered by the lack of information about real trader behavior. This makes it impossible to validate agent-based models, which are thus reverse-engineering attempts. This work is a contribution to the building of a set of styliz…
Personalized federated learning for diverse client objectives.
problem Training a single global model across diverse local datasets is not optimal.
method Efficiently calculates optimal weighted model combinations for each client based on their specific objectives.
result Our method outperforms existing alternatives and enables new personalized features.
ConvMFiT classifies client utterances in counseling dialogues.
problem Classifying client utterances for better counseling outcomes.
method Fine-tuning a pre-trained conversation model on counselor-client dialogues.
result ConvMFiT outperforms state-of-the-art models in classification.
A new scheme for private computation splits client data into shares for server operations.
problem Private computation between client and server without revealing data.
method Stochastic scheme splitting client data into privatized shares.
result Server performs operations on privatized shares without learning raw data.
This work detects anomalous clients in federated learning to prevent their adverse impacts.
problem Detecting and preventing anomalous client behaviors in federated learning systems.
method Generates low-dimensional surrogates of model weight vectors and uses them for anomaly detection.
result The proposed detection-based approach significantly outperforms conventional defense-based methods.
A federated minimax framework for heterogeneous clients.
problem Training with edge devices having different datasets and capabilities.
method Proposes a federated minimax optimization framework with normalized updates.
result Improves convergence and communication complexity for nonconvex functions.
FedAMD framework improves federated learning with partial client participation.
problem Data heterogeneity and inactive client updates in partial client participation.
method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.
result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.
Robo-advisor improves investment advice through client interaction.
problem Offering personalized financial advice to clients.
method Adaptive mean-variance portfolio optimization with client interaction.
result Optimal investment strategy includes both myopic and intertemporal hedging terms.
Machine learning improves risk prediction for online lending.
problem Traditional credit scoring models fail to utilize big data effectively.
method Collected diverse data, built and tested ensemble machine learning models (random forest and XGBoost).
result XGBoost model outperforms traditional models in loan default probability prediction.
Federated framework learns causal states to predict counterfactuals without centralizing data.
problem Decentralized counterfactual reasoning in coupled industrial systems with private data.
method Federated causal representation learning in state-space systems.
result Proves convergence to centralized oracle and provides privacy guarantees.
Unified Bayesian framework for clustered federated learning improves model performance.
problem Handling non-IID client data in federated learning.
method A unified Bayesian framework for clustered federated learning that associates clients to clusters and proposes practical algorithms for data associations.
result The proposed framework increases model performance by circumventing the need for unique client-cluster associations.
FLowDUP trains personalized models with unlabeled clients in low dimensions.
problem Training personalized models on clients with only unlabeled data.
method FLowDUP uses a forward pass with unlabeled data and a transductive PAC-Bayesian bound to generate personalized models in a low-dimensional subspace.
result FLowDUP enables efficient communication and computation with personalized models generated from unlabeled data.
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availabilitystat.ML
Federated learning algorithm improves with intermittent client availability.
problem Performance degradation in Federated Averaging due to client availability changes.
method Federated Latest Averaging (FedLaAvg) uses latest gradients from all clients, even when unavailable.
result FedLaAvg achieves sublinear speedup compared to classical Federated Averaging.
Modeling bank portfolio risk under climate transition impacts.
problem Evaluating risk measures for a bank's collateralized loans in a climate transition economy.
method Developed an end-to-end modeling framework using stochastic processes and dynamic macroeconomic variables.
result Derived expressions for risk measures as functions of climate transition parameters.
DFedAvgM is a decentralized FedAvg with momentum for privacy and communication efficiency.
problem Efficiently train models with privacy and communication efficiency in federated learning.
method Decentralized Federated Averaging with Momentum (DFedAvgM) on clients connected by an undirected graph, using stochastic gradient descent with momentum and quantization.
result DFedAvgM converges under trivial assumptions and can be improved with the PŁ property, numerically verified.
Group personalization improves FL performance in heterogeneous client data.
problem Mitigating client drift in federated learning with heterogeneous data.
method Fine-tuning a global FL model over homogeneous groups of clients, then personalizing each group's model.
result The proposed method achieves superior personalization performance compared to other FL approaches.
Hybrid-FL improves ML model accuracy in non-IID data environments.
problem Performance degradation in FL due to non-IID data.
method Hybrid-FL combines client and server learning, selecting optimal clients and data for aggregation.
result 13.5% higher classification accuracy than previous methods.
This study analyzes how carbon pricing affects credit risk measures in a portfolio.
problem Impact of carbon pricing on credit risk measures in a portfolio.
method Adapted stochastic multisectoral model to account for GHG emissions costs and carbon prices.
result Carbon pricing distorts firm value distributions, increases banking fees, and reduces profitability.