Exploratory Data Analysis for Banking and Finance: Unveiling Insights and Patterns
- URL: http://arxiv.org/abs/2407.11976v1
- Date: Sat, 25 May 2024 16:15:21 GMT
- Title: Exploratory Data Analysis for Banking and Finance: Unveiling Insights and Patterns
- Authors: Ankur Agarwal, Shashi Prabha, Raghav Yadav,
- Abstract summary: The study examines transaction patterns, credit limits, and usage across merchant categories.
It also considers demographic factors like age, gender, and income on usage patterns.
The report addresses customer churning, analyzing churn rates and factors such as demographics, transaction history, and satisfaction levels.
- Score: 0.2594420805049218
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper explores the application of Exploratory Data Analytics (EDA) in the banking and finance domain, focusing on credit card usage and customer churning. It presents a step-by-step analysis using EDA techniques such as descriptive statistics, data visualization, and correlation analysis. The study examines transaction patterns, credit limits, and usage across merchant categories, providing insights into consumer behavior. It also considers demographic factors like age, gender, and income on usage patterns. Additionally, the report addresses customer churning, analyzing churn rates and factors such as demographics, transaction history, and satisfaction levels. These insights help banking professionals make data-driven decisions, improve marketing strategies, and enhance customer retention, ultimately contributing to profitability.
Related papers
- InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation [79.09622602860703]
We introduce InsightBench, a benchmark dataset with three key features.
It consists of 100 datasets representing diverse business use cases such as finance and incident management.
Unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics.
arXiv Detail & Related papers (2024-07-08T22:06:09Z) - 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) - Proactive Detractor Detection Framework Based on Message-Wise Sentiment
Analysis Over Customer Support Interactions [60.87845704495664]
We propose a framework relying solely on chat-based customer support interactions for predicting the recommendation decision of individual users.
For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America.
Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
arXiv Detail & Related papers (2022-11-08T00:43:36Z) - 5-Star Hotel Customer Satisfaction Analysis Using Hybrid Methodology [0.0]
Our research suggests a new way to find factors for customer satisfaction through review data.
Unlike many studies on customer satisfaction that have been conducted in the past, our research has a novelty of the thesis.
arXiv Detail & Related papers (2022-09-26T04:53:10Z) - Topic Modelling on Consumer Financial Protection Bureau Data: An
Approach Using BERT Based Embeddings [0.0]
We evaluate BERTopic, a novel method that generates topics using sentence embeddings on Consumer Financial Protection Bureau (CFPB) data.
Our work shows that BERTopic is flexible and yet provides meaningful and diverse topics compared to LDA and LSA.
domain-specific pre-trained embeddings (FinBERT) yield even better topics.
arXiv Detail & Related papers (2022-05-15T11:14:47Z) - Characterization of Frequent Online Shoppers using Statistical Learning
with Sparsity [54.26540039514418]
This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
arXiv Detail & Related papers (2021-11-11T05:36:39Z) - Augmenting Decision Making via Interactive What-If Analysis [4.920817773181235]
Business users currently need to perform lengthy exploratory analyses.
The increasing complexity of datasets combined with the cognitive limitations of humans makes it challenging to carry over multiple hypotheses.
Here we argue for four functionalities that we believe are necessary to enable business users to interactively learn and reason about the relationships (functions) between sets of data attributes.
arXiv Detail & Related papers (2021-09-13T17:54:30Z) - Enhancing User' s Income Estimation with Super-App Alternative Data [59.60094442546867]
It compares the performance of these alternative data sources with the performance of industry-accepted bureau income estimators.
Ultimately, this paper shows the incentive for financial institutions to seek to incorporate alternative data into constructing their risk profiles.
arXiv Detail & Related papers (2021-04-12T21:34:44Z) - Supporting Financial Inclusion with Graph Machine Learning and Super-App
Alternative Data [63.942632088208505]
Super-Apps have changed the way we think about the interactions between users and commerce.
This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
arXiv Detail & Related papers (2021-02-19T15:13:06Z) - Categorizing Online Shopping Behavior from Cosmetics to Electronics: An
Analytical Framework [3.6726589459214445]
The proposed framework is extendable to other large e-commerce data sets to obtain automated purchase predictions and descriptive consumer insights.
The proposed system achieves 97-99% classification accuracy and recall for user-journey level purchase predictions.
arXiv Detail & Related papers (2020-10-06T06:16:44Z) - Face to Purchase: Predicting Consumer Choices with Structured Facial and
Behavioral Traits Embedding [53.02059906193556]
We propose to predict consumers' purchases based on their facial features and purchasing histories.
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers.
Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
arXiv Detail & Related papers (2020-07-14T06:06:41Z)
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.