Machine Learning for Fraud Detection
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1 Enhedens navn Machine Learning for Fraud Detection Dídac Rodríguez Arbonès
2 Fraud Fraud in Denmark is a substantial issue. The financial institutions are keen on prevention. It is a very difficult problem to solve. Substantial resources are dedicated to fight against it.
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4 Coverage 6 M users ~500 M transactions 4,5 M users ~145,5 M transactions
5 Overview of project Develop a prevention mechanism against online banking cyber-crime using machine learning (ML). Detection capable of identifying fraudulent authentication sessions in NemID. Possibility of working within other company IT assets. Project to be deployed to BankID in Norway and NemID in Denmark adding to the security measures currently in place. Self-updating and minimal operation intervention.
6 Phishing It consists on redirecting the user to a malicious webpage. The user is unaware who is the information recipient. The criminals can use the credentials to authenticate to the systems.
7 Machine learning Part of artificial intelligence. Ecompasses many algorithms to learn from data and make decisions on new events. Its power comes from the available information from the system. There is no one-size-fits-all, it is as good as the data it relies on. There are two major types: Supervised Unsupervised
8 Machine learning Various levels of performance and requirements. Ensemble systems possible. Feature space reduction. LDA SVM
9 Data source The data is preprocessed and anonymized after it is collected from the login applet of the BankID. The data is complex and heterogenous: Geographic information Browser information OS information Timing information There are no reliable labels to be used for analysis.
10 Data source An example of user data are keystrokes: tf tf td td td time There have been previous studies1 achieving good results in classification only with this type of data. It relies on heavily on previous user knowledge. Unfortunately, this type of data is not enough on its own for a reliable fraud detection. (1) Killourhy, Kevin S., and Roy A. Maxion. "Comparing anomaly-detection algorithms for keystroke dynamics." Dependable Systems & Networks, DSN'09. IEEE/IFIP International Conference on. IEEE, 2009
11 Data source Real-valued vectors Discrete data Unmatched sets Usage data Device information
12 System overview
13 Other information sources Credit cards Individual transactions Person-to-person Crowd-to-person Web service usage Customer profiling
14 Challenges & expectations Thieves are smart. Sometimes very smart. There exist numerous companies around the globe with the only goal of illicit financial gain. It is humanly impossible to detect and stop them without unlimited resources. Legal & marketing concerns make datasets scarce and incomplete. Machine learning is an excellent candidate to pursue these actions in a scalable manner with low expense and effort. It can find patterns not directly obvious to humans. It can self-adapt to new events.
15 Industrial PhD practicalities The programme is funded by the Ministry of Higher education and Science (DASTI1). There are several application reviews during the year. It typically takes 2-4 months to receive an answer. The company is required to have physical presence in Denmark. The student is formally employed at the company. There is a supervisor both at the university and at the company. Time allocation is roughly 50%, if not otherwise agreed. There is a budget allocated for the university expenses and a subvention for the company. Usually any patent or profitable outcome from the project is property of the company (IP agreement), but there has to be a scientific dissemination component. (1)
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