Anomaly Detection Technique Based on Machine Learning for Mortgage Contract Classification Problem

Student: Mirzaaghayev Ramil

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2020

The availability of data and the democratization of open-source machine learning tools can help businesses to optimize their processes. One of the many concerns of any business is to keep data quality at a high level by preventing anomalies from entering the system. Compared to traditional approaches where these anomalies are identified by manual checks or by using automated static rule-based solutions, machine learning methods are proven to be very cost-effective and robust. This work includes studying these methods thoroughly and developing a machine learning solution that can detect anomalies, particularly in mortgage contracts data provided by one of the local banks in Germany.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses