DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
Paper proposes a new topology for AML analysis using Poincaré embeddings.
problem Complex money laundering schemes and regulatory constraints hinder AML analysis and information sharing.
method Proposes a new topology for AML analysis using Poincaré embeddings.
result Demonstrates improved AML analysis and information sharing through Poincaré embeddings.
Reduce survey questions to scale market research without annoying customers.
problem Performing market research by surveying customers with many questions is inefficient and annoying.
method Used Bayesian networks to model and reduce the number of questions asked to customers.
result Demonstrated the effectiveness of the approach using an example of broadband customer segmentation.
We consider a planning problem where the dynamics and rewards of the environment depend on a hidden static parameter referred to as the context. The objective is to learn a strategy that maximizes the accumulated reward across all contexts. The new model, called Contextual Markov Decision Process (CMDP), can model a cu…
Telecommunications operators (telcos) traditional sources of income, voice and SMS, are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experie…
The study explores machine learning for predicting customer propensity-to-pay uncertainty.
problem Improving customer experience, reducing financial hardship, and managing cash flow risks.
method Investigated machine learning models for predicting propensity-to-pay, focusing on uncertainty estimation.
result Novel Bayesian Neural Network model for binary classification of propensity-to-pay.
Dynamic pricing model for airline ancillaries improves revenue and conversion.
problem Inefficient conventional pricing strategies for airline ancillaries.
method Developed a dynamic pricing model using AI, combining forecasting and optimization.
result Deep learning models outperform traditional machine learning techniques in online settings.
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
A new pricing strategy learns customer valuations without noise distribution knowledge.
problem Setting optimal prices for products based on customer valuations with unknown noise.
method Developed a novel perturbed linear bandit framework to learn both contextual functions and market noise.
result Proved sub-linear regret bound and demonstrated superior performance on simulations and real data.
FinBloom enhances LLMs for real-time financial queries.
problem Limited access to real-time financial data by LLMs.
method Developed a custom 7B parameter LLM, Financial Context Dataset, and a Financial Agent.
result Significantly improved LLMs' capability to handle dynamic financial tasks.
Outfittery uses machine learning to help stylists choose appropriate fashion items.
problem Selecting appropriate fashion items and ensuring relevance to customers.
method Combining machine learning with human expertise to recommend items by style fit and relevance.
result The method successfully recommends fashion items by style fit and relevance.
Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of t…
MTDS improves sequence generation adaptability via latent code control.
problem Lack of adaptability in sequence generation models like RNNs.
method Hierarchical multi-task dynamical systems (MTDS) with latent code control.
result MTDS enables style transfer, interpolation, and morphing in generated sequences.
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its …
This work optimizes marketing by targeting persuadable customers with causal effects.
problem Optimizing marketing ROI by targeting only those who would be influenced.
method Causal contextual multi-armed bandits, incorporating causal inference and uplift modeling.
result Preliminary experiments show the approach improves marketing ROI.
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
problem High dimensionality and sparsity in user-item data, especially in terms of revenue contributions.
method The approach encodes revenue contributions in the user-item matrix and computes customer similarity using suitable distance measures.
result The method segments users based on revenue-based similarity and supports recommendations aligned with profitability objectives.
Study online pricing with contextual elasticity and heteroscedastic valuation.
problem Online contextual dynamic pricing with customer decision based on features and price.
method Introduced a novel approach to modeling customer demand with feature-based price elasticity and heteroscedastic noise. Proposed an efficient algorithm called Pricing with Perturbation (PwP).
result Proved an O ( d T log T ) O(\sqrt{dT\log T}) O ( d T log T ) regret bound for the algorithm, matching a lower bound of Ω ( d T ) Ω(\sqrt{dT}) Ω ( d T ) . The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…
Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.
problem Low accuracy in demand forecasts for Knitwear product category.
method Dynamic selection of the best algorithm from an algorithm rack based on performance and context.
result Increased forecast accuracy from 60% to 80% for Knitwear.
A new method for uplift modeling using learning-to-rank techniques.
problem Improving customer targeting in marketing and retention.
method Unified formalization of uplift measures, learning-to-rank with PCG metric, LambdaMART optimization.
result Improved results compared to standard learning-to-rank metrics and state-of-the-art uplift modeling.
LLMs learn probability density functions in-context, showing distinct learning trajectories.
problem Density estimation of time series data in LLMs.
method Intensive Principal Component Analysis (InPCA) to visualize and analyze LLMs' learning dynamics.
result LLMs follow similar learning trajectories in a low-dimensional InPCA space, distinct from traditional methods.
Financial institutions use LSTM models to predict customer goals.
problem Predicting customer goals and actions in financial services.
method Used LSTM models with state-space graph embeddings on historical customer traces.
result Demonstrated the effectiveness of LSTM models in predicting customer goals and actions.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
problem Understanding the relationship between a firm's returns and its customers' past returns.
method Examined customer momentum using a long-short equally-weighted decile portfolio and Fama-French factor models.
result Customer momentum generates significant monthly returns and is statistically significant.
Method generates realistic customer transaction sequences for retail analysis.
problem Modeling customer behavior and purchasing patterns in retail databases.
method Customer embedding using RNN and GAN for generating plausible baskets.
result Generated baskets resemble real transactions and replicate sequential patterns.
PROPS personalizes complex models for sequential data.
problem Personalizing complex models for sequential data.
method Pin black-box predictions to a hidden Markov model and learn probabilistic perturbations.
result PROPS achieves good customization with minimal data.
Automates molecule design with simpler SMILES generation and reinforcement learning.
problem Designing molecules with specific chemical properties.
method Combines context-free grammar for SMILES strings and reinforcement learning with a Transformer model.
result Significantly reduces model steps per atom and beats previous baselines.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
problem Limited CLV estimation using single-entity data.
method Introduces PCLV framework using Open Banking data for comprehensive customer value estimation.
result Open Banking data can estimate PCLV per competitor, showing a 21.06% increase over Actual CLV.
Predicts customer call intent for auto dealerships using CNN.
problem Understanding customer intent from phone calls for better service.
method Developed a CNN-based supervised learning model for multi-class classification.
result CNN model performs well on customer call intent classification.
This research tackles unsupervised topic extraction in noisy social media data.
problem Capturing customer insights from social media data is challenging due to noise and heterogeneity.
method The research presents three nonparametric approaches based on the Variational Autoencoder framework: Embedded Dirichlet Process, Embedded Hierarchical Dirichlet Process, and time-aware Dynamic Embedded Dirichlet Process.
result The models achieve equal to better performance than state-of-the-art methods in topic extraction from noisy social media data.
Algorithm learns feature representations from randomized experiments to improve counterfactual inferences.
problem Measuring the impact of interventions with limited feedback.
method Feature learning algorithm from randomized experiments to identify effective and ineffective interventions.
result The algorithm leverages feature representations to derive the value of interventions for each instance, improving decision-making.
VAE learns latent representations for bank customers' creditworthiness.
problem Improving marketing, CRM, and credit risk assessment in retail banks.
method Adopted Variational Autoencoder (VAE) to learn latent representations.
result VAE latent representations capture customers' creditworthiness and generalize to new data.
The paper uses RFM and clustering to segment bank customers.
problem Challenges in customer retention and profitable segmentation in banking.
method RFM technique and clustering algorithms applied to real data.
result Successful customer segmentation improves conversion rates.
Paper tackles AI risks by customizing metrics and models.
problem AI risks are multidimensional and immaturely managed.
method Decomposes AI risks into data protection, fairness, etc., and develops metrics and models.
result Customized metrics and models reduce AI risk uncertainty.
The study improves CLV predictions in retail banking with machine learning.
problem Estimating customer lifetime value in retail banking is challenging.
method Developed a novel framework for CLV predictions over arbitrary time horizons.
result 43% improvement in out-of-time CLV prediction error.
Adapts large transformer model for search query intent understanding.
problem Understanding and predicting user intents from search queries.
method Adapts BERT-like architecture for search queries, accounts for query noisiness and sparseness.
result Builds a shareable deep learning model for query intent identification.
Study clusters bank customers using LSTM and DTW.
problem Efficiently segmenting bank customers for targeted offers.
method Encoder-decoder LSTM network and Dynamic Time Warping (DTW).
result Hybrid method yields more accurate clusters.
Statistical test verifies long-term rating system calibration with overlapping time windows.
problem Verifying supervisory requirements for overlapping time windows in rating systems.
method Analyzes long-run default rate distribution and correlation effects; presents conservative calibration test methods.
result Developed a test for individual and portfolio levels that can handle unknown variance.
ADMM solves constrained CASH problems by breaking them into smaller, manageable pieces.
problem Handling black-box constraints in CASH problems.
method Leverages ADMM optimization framework to decompose CASH problems.
result ADMM facilitates incorporation of black-box constraints.
Study compares classification techniques to predict customer churn in banking.
problem Predicting customer churn in banking industry.
method Comparison of six supervised classification techniques (ANN and random forest) on 10000 European bank customers.
result ANN structure with five nodes in a single hidden layer is the best performing classifier.
KodeXv0.1 improves financial question answering over GPT-4.
problem Lack of specialized financial language models.
method Custom training on financial documents, RAG-aware 4bit LoRA tuning.
result KodeX-8Bv0.1 outperforms GPT-4 by up to 9.24% in financial tasks.
It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial for such predictions. However, their demographic data, preferences, and other information that might be useful for building loyalty programs …
Silent abandonment reduces contact center efficiency by 5%-15%.
problem Measuring customer abandonment and patience in text-based contact centers is challenging due to uncertainty.
method Developed methodologies to identify silent-abandonment customers and estimate customer patience using text analysis and queueing models.
result Silent abandonment accounts for 30%-67% of customer abandonments and reduces system efficiency by 5%-15%.
DALC customizes LSTM models for detectors in large-scale traffic networks.
problem Fine-grained traffic prediction for large-scale transportation networks.
method Formulated as a finite Markov decision process, introduced ALC algorithm for automatic customization, and developed DALC for distributed customization.
result DALC provides higher prediction accuracy than Apache Spark MLlib approaches.
New algorithm balances limited resources for unknown customer types over time.
problem Allocating limited resources to diverse customers with uncertain demands.
method Synthesizes inventory balancing and online learning.
result Performance guarantee is tight, showing both competitive ratio and regret losses are relevant.
Insurance firms use RL to optimize customer offers for desired target portfolios.
problem Optimizing insurance offers to achieve a desired customer portfolio.
method Developed a novel reinforcement learning algorithm.
result The RL algorithm outperforms traditional methods in a synthetic market.
Deep learning predicts customer churn in retail.
problem Accurately predicting which customers are likely to stop purchasing.
method Survival model parameters learned by recurrent neural networks.
result Individual level survival models for purchasing behavior.
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.