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.
The paper uses RFM and clustering to segment bank customers.
Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and commun…
The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing…
We consider the problem of segmenting a large population of customers into non-overlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, etc. over subsets of items. We focus on the setting where the universe of items is large (ranging from thousands to millio…
Paper presents a novel time series clustering algorithm for financial inclusion.
Image segmentation is widely used in a variety of computer vision tasks, such as object localization and recognition, boundary detection, and medical imaging. This thesis proposes deep learning architectures to improve automatic object localization and boundary delineation for salient object segmentation in natural ima…
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
A new clustering framework optimizes customer search data for personalized travel recommendations.
Study shows non-systematic bias in customer satisfaction surveys limits data value.
System uses TDA for user segmentation and demand forecasting.
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…
For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge. Intel's Sales and Marketing Group (SMG) faces similar challenges while growing in new markets and domains and evolving its existing business. In today's complex technolog…
In medicine, visualizing chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosomes often overlap and it is necessary to identify and distinguish between the overlapping chromosomes. A segmentation solution that is fast and automated will enable scaling of co…
We consider the problem of learning the preferences of a heterogeneous population by observing choices from an assortment of products, ads, or other offerings. Our observation model takes a form common in assortment planning applications: each arriving customer is offered an assortment consisting of a subset of all pos…
Study clusters bank customers using LSTM and DTW.
A new model analyzes document structure and customer shopping patterns.
Simplifies complex pricing models for better interpretability and revenue.
A framework for faster, better infographic design by non-experts and experts alike.
Study examines pricing strategies in competitive supply chains with discrete prices.
In order for an e-commerce platform to maximize its revenue, it must recommend customers items they are most likely to purchase. However, the company often has business constraints on these items, such as the number of each item in stock. In this work, our goal is to recommend items to users as they arrive on a webpage…
In marketing we are often confronted with a continuous stream of responses to marketing messages. Such streaming data provide invaluable information regarding message effectiveness and segmentation. However, streaming data are hard to analyze using conventional methods: their high volume and the fact that they are cont…
3D ConvNets improved with Project & Excite for medical imaging segmentation.
Paper proposes a method to estimate consumer valuations from bundle sales data.
New algorithm for active learning in multiple matrix completion problems.
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm. We extend its fine-tuning procedure to address one of its major limitations - applicability to inputs longer than a few hundred words, such as…
LLmFPCA-detect detects anomalies in sparse longitudinal text data using LLMs and mFPCA.
Semantic image segmentation is one the most demanding task, especially for analysis of traffic conditions for self-driving cars. Here the results of application of several deep learning architectures (PSPNet and ICNet) for semantic image segmentation of traffic stereo-pair images are presented. The images from Cityscap…
New RDPC dissimilarity measure improves time series clustering.
The DeepSurv model predicts purchase timing better than other survival models.
The pairwise influence matrix of Dobrushin has long been used as an analytical tool to bound the rate of convergence of Gibbs sampling. In this work, we use Dobrushin influence as the basis of a practical tool to certify and efficiently improve the quality of a discrete Gibbs sampler. Our Dobrushin-optimized Gibbs samp…
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…
The study optimizes free trial lengths to boost subscriptions and consumer loyalty.
DCE learns customer embeddings from digital activity and financial context.
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives. This short-paper proposes PODNet, Plannable Option Discovery Network, addressing how to segment an unstructured set of demonstrated traject…
Financial institutions use LSTM models to predict customer goals.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
Automates detection of electric devices in 3D x-ray images of luggage.
Study examines European banks' digital transformation strategies.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
In order to better engage with customers, retailers rely on extensive customer and product databases which allows them to better understand customer behaviour and purchasing patterns. This has long been a challenging task as customer modelling is a multi-faceted, noisy and time-dependent problem. The most common way to…
The study improves CLV predictions in retail banking with machine learning.
In the recent years money laundering schemes have grown in complexity and speed of realization, affecting financial institutions and millions of customers globally. Strengthened privacy policies, along with in-country regulations, make it hard for banks to inner- and cross-share, and report suspicious activities for th…
Study compares classification techniques to predict customer churn in banking.
Auto dealerships receive thousands of calls daily from customers who are interested in sales, service, vendors and jobseekers. With so many calls, it is very important for auto dealers to understand the intent of these calls to provide positive customer experiences that ensure customer satisfaction, deep customer engag…
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%.
Insurance firms use RL to optimize customer offers for desired target portfolios.