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 Prescriptive Canvas improves business outcomes by directly prescribing actions based on predictions.
The paper uses machine learning to find causal rules from business process logs.
The paper proposes a ML workflow for B2B sales prediction.
Extends expected value framework for cost-sensitive causal decision-making.
Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decision to be made, and to give an informative explanation. We explore a new method of generating counterfactual explanations, which instead of ex…
This study integrates cost-sensitive and causal classification methods.
This paper explores how to interpret machine learning models in business process analytics.
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood …
The paper presents a practical method for evaluating investment projects using real options.
Two-sided marketplaces such as eBay, Etsy and Taobao have two distinct groups of customers: buyers who use the platform to seek the most relevant and interesting item to purchase and sellers who view the same platform as a tool to reach out to their audience and grow their business. Additionally, platforms have their o…
This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…
Optimizes user marketing campaigns to balance cost and effectiveness.
User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on pre- dicting attrition, which is one of typical user intended actions. Conventional attriti…
Paper proposes a GAN-based method for better next event prediction in business processes.
Paper assesses risks of stablecoins, from lending to business-to-business.
A machine learning model may exhibit discrimination when used to make decisions involving people. One potential cause for such outcomes is that the model uses a statistical proxy for a protected demographic attribute. In this paper we formulate a definition of proxy use for the setting of linear regression and present …
BanditLP optimizes personalized recommendations for large-scale systems.
Large corporate credit models may be adapted for small business risk assessment.
Study shows credit expansion in mortgage markets influenced U.S. business cycle.
Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on interna…
Framework adds human knowledge to AI decisions to improve outcomes.
Study evaluates sustainability of European banks using a new model.
Bayesian model uses mobile data to assess business resilience after hurricanes.
Modeling business expansion as a stochastic control problem, the study finds that firms are incentivized to expand but may wait.
The biggest problem with the methods of machine learning used today in business analytics is that they do not generalize well and often fail when applied to new data. One of the possible approaches to this problem is to enrich these methods (which are almost exclusively based on statistical algorithms) with some intrin…
Endogenous business cycles explain higher comovement across countries.
Machine learning predicts US and EuroZone business cycles with high accuracy.
The analysis of the theoretical material revealed the lack of consensus on defini-tion of the tax stimulation of innovation-active business entities within the re-gional taxation. The definition tax stimulation of innovation-active business en-tities is specified.
Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate class…
Examines international taxation's impact on Georgian businesses.
CONDA-PM framework helps analyze concept drift in business processes.
The study uses CoDa to analyze family business financial ratios, highlighting methodological issues.
Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the outcome or dependent feature, is highly important in statistical model building.…
Study examines how business units can benefit from group cohesion under regulatory constraints.
This paper studies business cycle patterns in UK sectoral output. It analyzes the distinction between white noise processes and their non-white noise counterparts in the frequency domain and further examines the associated features and patterns for the process where white noise conditions are violated. The characterist…
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. % It helps us infer the causality between two seemingly unrelated set of events. For example, if consumer spending in the United States declines, it has spillover effects on economies that de…
Development of efficient business process models and determination of their characteristic properties are subject of intense interdisciplinary research. Here, we consider a business process model as a directed graph. Its nodes correspond to the units identified by the modeler and the link direction indicates the causal…
Research shows franchised fast food companies' stock prices decline more during recessions.
New approach optimizes sales process for B2B businesses.
The accurate characterization of the business cycles in the nonlinear dynamic financial and economic systems in the time of globalization represents a formidable research problem. The central banks and other financial institutions make their decisions on the minimum capital requirements, countercyclical capital buffer …
Business cycles affect startup valuations, both directly and indirectly.
We propose a dynamical model for business cycle based on an optimal DI model. In the model there exists a conserved quantity, which corresponds to the total energy in a dynamical system. We found that the business cycle with the period 6 or 7 years is nicely reproduced, since the model predicts a periodic motion in the…
This paper introduces a reinforcement learning framework for A/B testing with dynamic causal effects.
Behavior modification improves prediction accuracy by nudging user behavior.
Mathematical model for focused investing reduces diversification risks.
The paper adds explanation to predictive process monitoring.
The paper explores how innovative financing solutions boost Moroccan businesses' performance.
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper pr…