AI SearchAn explainable and risk-sensitive machine learning model for loan approval in digital banking (Record no. 4324)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260817162221.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260224s20259999th mm 000 0 eng d |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12479342 |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT PJPR PMDS no.25-05 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nguyen Thanh Quang |
| 245 10 - TITLE STATEMENT | |
| Title | An explainable and risk-sensitive machine learning model for loan approval in digital banking |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 64 leaves : |
| Other physical details | ill.+ |
| Accompanying material | 1 online resource |
| 490 1# - SERIES STATEMENT | |
| Series statement | Project ; |
| Volume/sequential designation | no. PJPR PMDS-25-05 |
| 500 ## - GENERAL NOTE | |
| General note | A project report submitted in partial fulfillment of the requirements for the Degree of Master of Science (Professional) in Data Science and Artificial Intelligence Applications |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Master of Science (Professional) - Asian Institute of Technology, 2025 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | In the era of rapid digital banking transformation, integrating machine learning into credit decision systems has become increasingly important to improve efficiency, accuracy, transparency and risk management. This study designs, develops and evaluates an explainable machine learning model to support loan approval decisions in a digital banking context. The research pursues two main objectives. First, it aims to construct a predictive model that combines financial and contextual loan attributes in order to enhance loan default prediction accuracy. Second, it applies explainable artificial intelligence techniques, specifically SHAP Shapley Additive Explanations, to improve transparency, interpretability and user trust in model outputs.The study employs a publicly available Small Business Administration loan dataset containing 899,164 records and 27 variables. Comprehensive data preprocessing and exploratory data analysis were conducted to remove irrelevant and post default information, handle missing values and identify key predictive variables. A Random Forest classifier was selected for its robustness with structured data. On an independent test set, the model achieved strong predictive performance with accuracy of about 0.92, ROC AUC of about 0.955 and balanced precision and recall for both Paid in Full and Default classes. SHAP analysis identified Term and ApprovalFY as the most influential predictors, followed by loan size variables, urban or rural location, franchise status and selected sector and state indicators. The findings demonstrate that combining predictive modeling with SHAP based explainability can produce models that are both accurate and transparent for banking decision makers. The study advances the integration of explainable machine learning in credit risk modeling and provides a practical framework for explainable loan approval that can be adapted to digital banking operations in a responsible that is consistent with regulatory expectations. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Bank loans |
| General subdivision | Data processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Risk management |
| General subdivision | Data processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Internet banking |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chantri Polprasert, |
| Relator term | Chairperson |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chaklam Silpasuwanchai, |
| Relator term | Examination Committee |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vatcharaporn Esichaikul, |
| Relator term | Examination Committee |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Project ; |
| Volume/sequential designation | no. PJPR PMDS-25-05 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B23689">http://203.159.5.9/ait-thesis/detail.php?q=B23689</a> |
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| Operator's initials, OID (RLIN) | 0 |
| Cataloger's initials, CIN (RLIN) | 260311 |
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| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 67-Electronic Resource |
| 909 ## - LOCAL ITEMS USED | |
| Barcode | Barcode : - |
| CREATED | CREATED : 2026-02-24 |
| RECORD Id | RECORD # : i13576318 |
| LPATRON | LPATRON : 0 |
| LCHKIN | LCHKIN : - |
| RENEWALS | # RENEWALS : 0 |
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| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Copy number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 17/08/2026 | AIT PJPR PMDS no.25-05 | 17/08/2026 | 1 | 17/08/2026 | 67-Electronic Resource |