Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost

• URL: http://arxiv.org/abs/2412.16333v1
• Date: Fri, 20 Dec 2024 20:45:42 GMT
• Title: Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost
• Authors: Sahar Yarmohammadtoosky Dinesh Chowdary Attota,
• Abstract summary: This study employs advanced machine learning techniques to analyze consumer behavior and predict responses to direct mail campaigns. XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation.
• Score: 0.0
• License:
• Abstract: As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the probability that borrowers would default, which has a direct bearing on lending choices and risk management tactics. Despite the progress made in this domain, there is still a substantial knowledge gap concerning consumer actions that take place prior to the filing of credit card applications. The objective of this study is to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. This research employs advanced machine learning techniques, specifically logistic regression and XGBoost, to analyze consumer behavior and predict responses to direct mail campaigns. By integrating different data preprocessing strategies, including imputation and binning, we enhance the robustness and accuracy of our predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is particularly effective in handling complex data structures and provides a strong predictive capability in assessing credit risk.

Related papers

• Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns [0.0]
  The purpose of this research is to examine the efficacy of logit and probit models in predicting term deposit subscriptions. The target variable was balanced, considering the inherent imbalance in the dataset. The logit model performed better than the probit model in handling this classification problem.
  arXiv  Detail & Related papers  (2024-10-28T21:04:58Z)
• Achieving Fairness in Predictive Process Analytics via Adversarial Learning [50.31323204077591]
  This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value.
  arXiv  Detail & Related papers  (2024-10-03T15:56:03Z)
• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• Early Churn Prediction from Large Scale User-Product Interaction Time Series [0.0]
  This paper conducts an exhaustive study on predicting user churn using historical data. We aim to create a model forecasting customer churn likelihood, facilitating businesses in comprehending attrition trends and formulating effective retention plans.
  arXiv  Detail & Related papers  (2023-09-25T08:44:32Z)
• Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions [9.84528076130809]
  Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. This study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn.
  arXiv  Detail & Related papers  (2023-04-23T00:45:35Z)
• Causal Analysis of Customer Churn Using Deep Learning [9.84528076130809]
  Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. This paper proposes a framework using a deep feedforward neural network for classification. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn.
  arXiv  Detail & Related papers  (2023-04-20T18:56:13Z)
• Estimating defection in subscription-type markets: empirical analysis from the scholarly publishing industry [0.0]
  We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context.
  arXiv  Detail & Related papers  (2022-11-18T01:29:51Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes [61.20223338508952]
  Credit Risk Modelling plays a paramount role. Recent machine and deep learning techniques have been applied to the task. We suggest to use LIME technique to tackle the explainability problem in this field.
  arXiv  Detail & Related papers  (2020-12-30T10:27:59Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.