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…
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48 results for “business analytics”
This paper introduces TDA and TSI for better business analytics.
problem Nonlinear, multi-scale business datasets under-represented by traditional tools.
method Topological Data Analysis (TDA) and Topological Stability Index (TSI).
result TSI reveals structural variability in business data.
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…
Deep learning faces adoption challenges in business analytics.
problem Adoption of deep learning in business analytics is hindered by various factors.
method Empirical study based on three industry use cases.
result Gradient boosting is recommended for structured datasets in business analytics.
Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional…
Study evaluates sustainability of European banks using a new model.
problem Lack of a framework to evaluate sustainability of banking business models.
method Delphi-Analytic Hierarchy Process method to develop and assess the model.
result Norwegian and German banks have higher sustainability of their business models.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
problem Lack of fine-grained company labels for analytics.
method Word2Vec and dimensionality reduction on company website data.
result Semantic company embeddings for various applications.
Paper proposes a new method for learning business process representations.
problem Challenges in capturing all useful information in business process data.
method Combines Gramian Angular Fields and Convolutional Neural Networks for representation learning.
result Demonstrates effectiveness of the approach through visualization and multiple process prediction tasks.
Study improves document processing in banking with multimodal analytics.
problem Raising operational efficiency in banking through document-intensive processes.
method Comparative analysis of text classifiers and multimodal model (LayoutXLM) on company register extracts.
result Incorporating layout information in a model substantially increases performance.
A microscopic model of aggregation and fragmentation is introduced to investigate the size distribution of businesses. In the model, businesses are constrained to comply with the market price, as expected by the customers, while customers can only buy at the prices offered by the businesses. We show numerically and ana…
Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Diceq-fin.GN
The study optimizes supply chain management through a dice-based model to predict cleaner production.
problem Uncertainty in supply chain management and economic predictions.
method A 4-component SC module (environmental, demand, economic, social uncertainties) ranked by weight, using Analytical Hierarchical Process and optimization of a weighted cost function.
result Identifies conditions validating the sustainability of a business venture and optimizes market uncertainty.
A Generalized Preferential Attachment Model for Business Firms Growth Rates: II. Mathematical Treatmentphysics.soc-ph
We present a preferential attachment growth model to obtain the distribution of number of units in the classes which may represent business firms or other socio-economic entities. We found that is described in its central part by a power law with an exponent which depends on the probabil…
Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for…
This review covers AI in finance, challenges, techniques, and opportunities.
problem Challenges and opportunities in AI applications in finance.
method Comprehensive categorization and overview of AI research in finance over decades.
result A dense roadmap of AI challenges, techniques, and opportunities in finance.
In this paper, we address the aggregation of dependent stop loss reinsurance risks where the dependence among the ceding insurer(s) risks is governed by the Sarmanov distribution and each individual risk belongs to the class of Erlang mixtures. We investigate the effects of the ceding insurer(s) risk dependencies on th…
We formulate banks' capital optimization problem as a classic mean variance optimization, by leveraging an accurate linear approximation to the Shapely or Constrained Aumann-Shapley (CAS) allocation of max or nested max cost functions. This reduced form formulation admits an analytical solution, to the optimal leverage…
The sectoral synchronization observed for the Japanese business cycle in the Indices of Industrial Production data is an example of synchronization. The stability of this synchronization under a shock, e.g., fluctuation of supply or demand, is a matter of interest in physics and economics. We consider an economic syste…
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
We study cascades on a two-layer multiplex network, with asymmetric feedback that depends on the coupling strength between the layers. Based on an analytical branching process approximation, we calculate the systemic risk measured by the final fraction of failed nodes on a reference layer. The results are compared with…
The paper analyzes systemic risk in an insurance model with multiple business lines and heterogeneous claims.
problem Analyzing systemic risk in a multi-dimensional insurance model with heterogeneous claims.
method A multi-dimensional Lévy process-based renewal risk model with pairwise asymptotic independence (PAI).
result Asymptotic formulas for tail probabilities and systemic risk measures are derived.
Most of the analytical techniques used in the business cycle synchronisation literature rely upon the estimation of an empirical correlation matrix of time series data of macroeconomic aggregates, real GDP usually being the key variable. But the small number of available observations and small number of economies mean …
Effects of Economic Interactions on Credit Riskphysics.soc-ph
We study a credit risk model which captures effects of economic interactions on a firm's default probability. Economic interactions are represented as a functionally defined graph, and the existence of both cooperative, and competitive, business relations is taken into account. We provide an analytic solution of the mo…
A (reactive) lattice-gas approach to economic cyclescond-mat.stat-mech
A microscopic approach to macroeconomic features is intended. A model for macroeconomic behavior under heterogeneous spatial economic conditions is reviewed. A birth-death lattice gas model taking into account the influence of an economic environment on the fitness and concentration evolution of economic entities is nu…
SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.
problem AI exclusion of SMEs due to data scale mismatch.
method Bayesian transfer learning with hierarchical pooling and conformal prediction.
result 96.7% AUC on 100 obs SMEs, 24.2 point improvement over logistic regression.
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…
Paper assesses risks of stablecoins, from lending to business-to-business.
problem Credit risks in decentralized stablecoin issuance.
method Examines mechanisms, risks, and mitigation strategies at each layer.
result Potential for scaling stablecoins while maintaining systemic health.
Paper establishes a formula linking model performance to insurance loss ratio.
problem Improving model performance does not always lead to proportional improvements in loss ratio.
method Derives a closed-form formula connecting Pearson correlation to expected loss ratio.
result Model improvements have diminishing marginal returns in reducing loss ratio.
This paper deals with the explicit design of strategy formulations to make the best strategic choices from a conventional matrix form of representing strategic choices. The explicit strategy formulation is an analytical model which is targeted to provide a mathematical strategy framework to find the best moment for str…
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely …
Favorit strategy helps farmers mitigate market price fluctuations.
problem Mitigating adverse impact of price fluctuation on farmers.
method Analyzes historical price data to select optimal market timing for crops.
result Developed a strategy to reduce volatility risk for Indian farmers.
Graph database outperforms in filtering ESG stocks efficiently.
problem Efficiently filtering ESG stocks from large lists of equities.
method Compared SQL, No-SQL, and graph databases; used Python for database interactions; collected data from stock price and financial news.
result Graph database is more efficient for ESG stock filtering.
Large corporate credit models may be adapted for small business risk assessment.
problem Limited data and lack of credit analysts for small businesses.
method Adapting large corporate credit risk models for small businesses.
result Adapted models can predict small business credit risk effectively.
Study shows credit expansion in mortgage markets influenced U.S. business cycle.
problem Lack of causal evidence in cross-country business cycle studies.
method Unique research design combining cross-metropolitan U.S. data.
result Credit expansion caused stronger booms and busts in house-related industries.
We analyse four consecutive cycles observed in the USA for employment and inflation. They are driven by three oil price shocks and an intended interest rate shock. Non-linear coupling between the rate equations for consumer products as prey and consumers as predators provides the required instability, but its natural d…
Bayesian model uses mobile data to assess business resilience after hurricanes.
problem Evaluating economic impact of extreme shocks on businesses.
method Bayesian structural time series model with mobile phone data.
result Estimates business resilience after hurricanes, revealing key characteristics.
Modeling business expansion as a stochastic control problem, the study finds that firms are incentivized to expand but may wait.
problem Optimizing business expansion under exposure constraints and opportunity costs.
method Formulated as a novel stochastic control problem combined with optimal stopping time, derived an explicit solution for exponential utility.
result Firms are incentivized to expand but may wait due to opportunity costs and other factors.
Futures trading is the core of futures business, and it is considered as one of the typical complex systems. To investigate the complexity of futures trading, we employ the analytical method of complex networks. First, we use real trading records from the Shanghai Futures Exchange to construct futures trading networks,…
Endogenous business cycles explain higher comovement across countries.
problem Standard models struggle to explain high comovement in business cycles across countries.
method Developed a demand-driven reduced-form model with strategic complementarities and international trade linkages.
result Combining endogenous business cycles with exogenous shocks matches empirical comovement levels.
Within the context of traditional life insurance, a model-independent relationship about how the market value of assets is attributed to the best estimate, the value of in-force business and tax is established. This relationship holds true for any portfolio under run-off assumptions and can be used for the validation o…
Machine learning predicts US and EuroZone business cycles with high accuracy.
problem Predicting the business cycle phases in US and EuroZone.
method Three machine learning approaches were compared: Multinomial Logistic Regression (MLR) achieved the best results.
result MLR achieved 65.25% accuracy for EuroZone and 75% for US in predicting business cycle phases.
Most of the banks' operational risk internal models are based on loss pooling in risk and business line categories. The parameters and outputs of operational risk models are sensitive to the pooling of the data and the choice of the risk classification. In a simple model, we establish the link between the number of ris…
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.
problem Impact of international taxation on Georgian business entities.
method Analyzes types, features, and approaches of international taxation.
result Provides recommendations to correct current taxation issues.
CONDA-PM framework helps analyze concept drift in business processes.
problem Analyzing changes in business processes over time.
method Systematic Literature Review and framework development.
result Highlights areas needing research to complement existing efforts.
The study uses CoDa to analyze family business financial ratios, highlighting methodological issues.
problem Asymmetry, non-normality, and non-linearity in financial ratios of family businesses.
method Compositional data analysis (CoDa) and classical analysis strategies.
result Results are sensitive to the methodology used, emphasizing the need for appropriate methodologies.
Study examines how business units can benefit from group cohesion under regulatory constraints.
problem Regulatory constraints limit business units' ability to form a single cohesive group.
method Defined and analyzed cohesive risk measures to minimize capital costs.
result Cohesive risk measures allow groups to achieve minimal capital costs without altering individual liabilities.
A new optimizer, MVO, improves nonlinear regression performance.
problem Finding optimal coefficients in nonlinear regression models.
method Multi-Verse Optimizer (MVO) compared to Particle Swarm Optimizer (PSO).
result MVO statistically outperforms PSO in 10 nonlinear regression problems.