A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912133979045888 |
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| author | Groll, Andreas Khanna, Akshat Zeldin, Leonid |
| author_facet | Groll, Andreas Khanna, Akshat Zeldin, Leonid |
| contents | Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to ensure data reliability is the automatic detection of anomalies. While this approach is highly useful, it is also challenging due to the scarcity of labeled data that distinguish between normal and anomalous contracts or inter\-actions. This manuscript discusses several classical and modern unsupervised anomaly detection methods and compares their performance across two different datasets. In order to facilitate the adoption of these methods by companies, this work also explores ways to automate the process, making it accessible even to non-data scientists. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17495 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts Groll, Andreas Khanna, Akshat Zeldin, Leonid Applications Machine Learning Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to ensure data reliability is the automatic detection of anomalies. While this approach is highly useful, it is also challenging due to the scarcity of labeled data that distinguish between normal and anomalous contracts or inter\-actions. This manuscript discusses several classical and modern unsupervised anomaly detection methods and compares their performance across two different datasets. In order to facilitate the adoption of these methods by companies, this work also explores ways to automate the process, making it accessible even to non-data scientists. |
| title | A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2411.17495 |