Thesis
Zahra Rezaei
Created on March 20, 2024
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Using SVM algorithm in order to enhance the Financial Alert Management to reduce false positive and increase true positive transactions
Department of Information Engineering, Computer Science and Statistics (DIAG).
Zahra Rezaei Estakhroueiyeh 1959632
Superviser: Professor Luca Iocchi
March 2024
Master of Artificial Intelligence and Robotics
Index
1.
Summary
2.
Introduction
3.
Quality check on Alert Evaluation
4.
Support Vector Machine (SVM)
5.
6.
7.
Libraries
Conclusions
Bibliography
1. Summary
Summary:
- The role of artificial intelligence (AI) in enhancing business efficiency and risk management
- How Machine Learning enables:
- Basic goals about Quality check on Alert Evaluation
- Introducing Support Vector Machine
- AI-powered solutions are enhancing efficiency, accuracy, and innovation.
- Effective alert management is paramount.
- harnessed SVM within the Palantir platform
- Area Under Curve (AUC) value of 98.2 percent
2. Introduction
Introduction:
3. Quality check on Alert Evaluation
Quality Check on Alert Evaluation
- Evaluating Alert Quality
3. Quality check on Alert Evaluation
Quality Check on Alert Evaluation
2. Enhancing Risk Analysis and Reporting -Definition and Implementation of Enhancement Logic - Creation of Code Repository - Creation of User-Friendly Report for Risk Analysis: -Testing and Delivery of Report:
3. Quality check on Alert Evaluation
Quality Check on Alert Evaluation
3. The steps involved the FS Risk Analysis and Quality Check process -FS Risk Analysis -FS Training: -FS Continuous Monitoring (TBE - To Be Evaluated)
4.Support Vector Machine
SVM propertoes:
- Belongs to the class of binary classifiers
- Well-suited for solving classification problems in high-dimensional spaces
- Handling complex decision boundaries
- The model can analyze various features and patterns associated with financial transactions
- Detect and classify patterns
The specific implementation within the Palantir platform :
- Dataset Selection
- Label Conversion
- Feature Selection
- Data Splitting
- Model Training
- Performance Evaluation
- Visualization
- SVM Model Training
- Pattern Learning
- Prediction Application
- Comparison with Actual Labels
- Decision Boundary
5. Librariesn
Libraries:
- from pyspark.ml.feature import StringIndexer, VectorAssembler
- from pyspark.ml.classification import LinearSVC
- from pyspark.ml import Pipeline
- from pyspark.ml.linalg import DenseVector
- from pyspark.sql import functions as F
- from pyspark.ml.evaluation import BinaryClassificationEvaluator
- from sklearn.calibration import Calibrated-ClassifierCV
- import numpy as np
8. Development
Results:
98.2%
AreaUnder Curve
- The results were significant, with the SVM model achieving an impressive AreaUnderCurve (AUC) value of 98.2 percent, during testing.
- Introducing SVM into financial alert management, marks a
6..Conclusions
Conclusions
- Internship Insights: educe false positives and increase true positive detections—critical for preventing fraud and satisfying customers
- Impressive Results: Area Under the Curve (AUC) value of 98.2 percent, showcasing its efficacy in alert management.
- Operational Transformation: transform financial institution operations, leading to improved security, operational efficiency, and customer trust
LMoraes, Daniel and Wainer, Jacques and Rocha, Anderson, 2016, Low False Positive Learning with Support Vector Machines, https: //www.researchgate.net/publication/298427125_Low_False_Positive_ Learning_with_Support_Vector_Machines/citation/download
Daniel Moraes, Jacques Wainer, Anderson Rocha, July 2016, Low false positive learning with support vector machines https://www.sciencedirect.com/ science/article/abs/pii/S1047320316300116
LGiancarlo Cesi (Head Of Global Regulatory Track), Email Address: Giancarlo.Cesi@unicredit.eu
- My supervisor/boss at UniCredit Bank:
- Paper
10. Bibliography
Bibliographic references
- Paper
LJanuary to July (2023). Internal Policies and Procedures. Milan, Italy: UniCredit Bank.
- UniCredit Bank (Milan)
Thank you!
Any questions?