000			02182nam a22002417a 4500
003			ft8914
005			20251218153413.0
008			251218b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA76.9 A43 C75 2025
082			_a.
100	1		_aCrisostomo, Cyril Gwyneth F.; Daranciang, Angelica T.; Sapno, Randolf Sergio L.
245			_aEnhancement of catboost algorithm applied on insurance fraud detection
264		1	_a. _b. _cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: Detecting insurance fraud remains one the main areas of concern, causing great financial loss to the insurers and an increase in premium rates for the genuine policyholders. In most situations, traditional fraud detection systems have a hard time achieving a proper balance between their accuracy, generalization, and interpretability. The focus of the research is to enhance the CatBoost algorithm for fraud detection while addressing hyperparameter sensitivity, overfitting, and diminished explainability. To achieve that, the proposed method is the integration of Sherpa to enhance the hyperparameter selection, apply Stratified Bootstrapping to avoid overfitting, and implement SHAP (Shapley Additive Explanation) for enhanced transparency of the issues found on the model and it undertakes the evaluation of these proposed refinements on a publicly accessible fraud detection dataset. Results have shown a significant improvement in fraud classification precision, increasing from 83% to 96%, whereas the overfitting gap only decreases from 13.13% to 3.5%. Besides, the model also enhances the recall of fraud detection, thus becoming a more reliable and explainable option. The new framework provides insights into key fraud drivers via SHAP, allowing insurance companies to make better data-informed decisions, eventually resulting in reduced fraudulent claims and financial exposures.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37379 _d37379