Banking and financial institutions lose billions of dollars because of fraud. Machine learning can help detect and prevent fraud.
Machine learning algorithms can reveal fraud patterns much faster and more accurately than humans or traditional rule-based systems. Read this article to understand how exactly banks can benefit from ML-powered solutions in fraud detection.
Each year, banking and financial institutions from all over the world lose many billions of dollars because of fraud. Machine learning seems to be the most efficient technology for detecting and preventing fraud in this rapidly evolving sphere. From this article, you’ll understand how exactly banking and financial institutions can benefit from integrating ML algorithms. Plus, you’ll learn about the shortcomings of traditional fraud detection techniques.
Informative Statistics About Fraud in Banking and Finance
In 2020, PwC experts carried out the Global Economic Crime and Fraud Survey. Here is what they found out:
- The respondents reported $42 billion losses over the past 24 months.
- Only 56percent of financial institutions investigated their worst fraud incident.
- Only around 30% of financial institutions reported the fraudulent behavior to their board.
Why Do Traditional Fraud Detection Techniques Fail Today?
Before integrating machine learning algorithms in their workflows, banks used to rely on rule-based systems with manual evaluation for fraud detection. Now, such systems might fail to prevent fraud for the following reasons:
- Fraud patterns are evolving too fast and rule-based systems fail to adapt to them.
- Conventional systems often deliver false-positive results that block genuine customers.
- Frauds remain undetected because ruled-based systems can’t handle increasingly large amounts of data.
ML-based solutions can easily overcome all these obstacles and deliver fraud protection of much higher quality.
How Can Banks Benefit from Machine Learning in Fraud Detection?
ML algorithms automatically find improvements based on their previous experiences. They analyze huge sets of data to identify patterns. Even though the algorithms were not explicitly programmed to predict some specific situations and respond to them, they can learn how to do it.
Compared to conventional rule-based systems or human professionals, ML algorithms can cope much better with the following two tasks:
- Detect fraudulent activity much faster and with greater accuracy. This happens because they can make use of larger sets of data.
- Analyze even the most seemingly-unrelated information to find a pattern. Rule-based systems or human experts might ignore or unknowingly overlook some pieces of information.
ML algorithms reveal patterns that differ from the behavior of legitimate clients and label them as potential fraud. Apart from detecting fraud, ML algorithms can also conduct market research, come up with product recommendations and carry out predictive analytics.
What Exactly Can ML Algorithms Do to Detect Fraud?
Fraud prevention and risk management programs powered by machine learning normally stick to the following scheme:
- Gather and categorize as much previously recorded data as possible. This includes information about both legitimate and fraudulent transactions that are labeled respectively (so that algorithms understand how to classify them).
- The program learns to predict whether a certain customer or transaction is legitimate or not. The more real-life examples the program analyzes, the better.
- Once the program obtains the minimum required knowledge, it can’t be characterized as generic ML-based software anymore. It becomes specific to the business that trained it. Now, It’s ready to detect fraud for this particular financial institution but not for anyone else. The program will never stop learning. It will keep analyzing new data every day to become more powerful and accurate.
Even though ML-based software can evolve by itself, it still needs human supervision. Human professionals need to update, debug, and supervise the program to make the most of it.
Benefits of Machine Learning in Fraud Detection
Banking institutions rely on machine learning software for fraud detection for the following reasons:
- Speed: Machine learning algorithms work at an incredibly high speed. They don’t need rest and can process huge sets of data in real-time.
- Efficiency: Machine learning algorithms can analyze hundreds of thousands of payments per second. Even the most skilled and experienced team of human experts won’t be able to deliver the same efficiency. Algorithms never get tired. They don’t become less focused when handling repetitive tasks. They can easily detect even the subtlest changes in patterns across large amounts of data. Improved efficiency reduces the time that algorithms need to cope with tasks. Plus, it helps financial institutions to cut down fraud detection expenses.
- Scalability: The number of transactions that financial institutions need to handle increases every year. Human experts and rule-based systems fail to cope with such a workload. If banks go on relying on traditional approaches, it could lead to reduced accuracy in fraud detection. Banks would lose a lot of money and waste too much time on revealing fraud patterns. Machine learning algorithms are ideal for scaling. They will remain just as accurate and efficient regardless of the number and frequency of the transactions.
- Accuracy: Unlike humans, machine learning algorithms don’t make mistakes. Unlike rule-based systems, they rarely deliver false-positive results. They effortlessly identify even the least intuitive patterns. No fraud attempt will be left unnoticed!
As for the drawbacks, ML-powered solutions seem to have only two of them. First, they might be costly. Before integrating them into the bank’s workflows, managers need to convince all the decision-makers of the importance of ML in fraud detection. Second, financial institutions need to train their staff to handle ML programs, which also requires time and money. Yet the advantages that this technology brings far outweigh any of its potential shortcomings.
Hopefully, you found this article informative and now you better understand the benefits of ML-powered fraud detection in the financial sphere. Machine learning algorithms can reveal and prevent fraud much faster and with greater accuracy than human professionals or traditional rule-based systems. Such programs learn by analyzing large datasets of real-life examples and customize themselves to the needs of the company that uses them. If your organization doesn’t rely on machine learning in banking yet, it might be the right time to start!
Rippleshot uses machine learning and automation to detect high risk merchants and fraudulent transactions to help financial institutions protect themselves and proactively stop card fraud. Contact us today to learn more and schedule a product tour.