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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Predicting Loan Defaults in Maritime Finance Through Topological Data Analysis |
| Authors Name: | Jinka Shiva , Gunti Viswanath , Boya Shiva Prasad , Bonam Sanju |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_211035
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| Published Paper Id: | IJRTI2604059 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i4.211035 |
| Abstract: | —The maritime finance industry struggles to forecast loan defaults because its highly unpredictable markets and changing freight rates and interconnected risk systems. The traditional credit risk assessment methods for shipping companies use financial ratios and historical performance data to evaluate companies as independent entities, which restricts their ability to understand the intricate relationship network that exists among companies operating in identical market conditions and trade routes. The research presents a combined method that merges Topological Data Analysis with machine learning techniques to enhance the precision of loan default predictions in maritimefinance. The framework develops a correlation-based network in which shipping companies function as nodes and their financial and operational connections determine the weight of their inter company links. The network generates network-based topological metrics that include degree centrality, betweenness centrality, clustering coefficient, and PageRank which help to identify structural patterns and systemic interdependencies that standard financial data cannot reveal. The Pearson correlation coefficient identifies the most important features for predicting loan default through its analysis of the linear relationship strength between each input feature and the Default target variable. The feature set contains financial, operational, and topological data which enables four machine learning classifiers to be trained on the dataset that includes Logistic Regression, Decision Tree, Random Forest, and XGBoost. The experimental results show that XGBoost delivers the best classification results which include an accuracy of 0.9000 and an F1-Score of 0.9143, because it outperforms all other models in the baseline configuration. The topological features which we added to the model showed better prediction results with all four classifiers, because the network data which includes relational and structural information provided additional predictive value beyond standard financial metrics. The new hybrid system which we developed enables financial institutions to assess credit risk in the maritime shipping industry through a complete, precise, and operational assessment tool. |
| Keywords: | Maritime finance, loan default prediction, topological data analysis, machine learning, credit risk assessment,correlation network, XGBoost, shipping industry. |
| Cite Article: | "Predicting Loan Defaults in Maritime Finance Through Topological Data Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a432-a441, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604059.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2604059
Registration ID:211035
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i4.211035
Page No: a432-a441 Country: Kurnool, Andhra Pradesh, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604059 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604059 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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