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Machine learning (ML) is one of the most discussed technological tools, and if in the past only a few companies could use it due to high cost and lack of resources, today many industries use ML. The financial sector is not an exception and embraces all possible advances in digital transformation. The main trouble of the financial domain is fraud detection. ML is the number one technology that helps Fintech companies detect fraud. Let’s find out how.
First, we would like to share some data on the losses of the banks because of fraud.
The Financial Regulation News data say the banking industry lost $2.2 billion in fraud losses in 2016, 58% related to debit card fraud. ATM Marketplace indicates card fraud losses escalated in 2017 and card fraud may increase by 42% by 2020. Statista forecast, however, is more positive – by 2018 payment card fraud losses in the United States are to decrease to $1.8 billion.
As Fintech offers digital cashflow, users, afraid of data loss or fraud. As big amounts of money are processing online, hackers are more interested to steal the data and even money. Fraud detection is a system for identification and blocking suspicious activities to prevent such activities endanger the business. The volume of digital transactions and payment processing are growing. There is a majority of people who even don’t use cards of a physical bank, prefer digital banks. This brings even more challenges to Fintech. Data protection and safety in the transaction are the number one requirement of the clients, and you need to consider it as a financial forecast for digital solutions. Even one negative case can destroy business and its reputation.
The fraud detection has several steps, which involves monitoring, detection, decisions, case management, and learning. ML can automatically and independently identify unusual patterns in datasets which can be characteristics of fraud. And, there is no need to mentor by the human analyst. It is really hard to conduct proper fraud detection by humans only, as it will need too many highly-qualified resources and there is always a human factor.
Machine learning algorithms that detect fraudulent behaviors and adapt to unseen fraud actions. While building machine learning tools to identify fraud in Fintech companies, you should integrate supervised and unsupervised AI models. Supervised models are the most used for the majority of ML cases, and trained on a set of properly “tagged” transactions - either fraud or non-fraud. Unsupervised models – identify anomalous behavior in cases where tagged transaction data is relatively thin or non-existent.
The best machine learning fraud detection system is the one that combines these two models like supervised and unsupervised AI techniques, behavioral analytics, and adaptive analytics to enable real-time decision making.
To conduct an effective fraud detection in the Fintech area, machine learning tools are the most effective. ML algorithms can process a huge amount of data and detect as many cases as possible, as well as predict possible safety risks and unseen scenarios.