Financial fraud and data hacking are the most popular activities among cybercriminals. Moreover, they adore the banking industry, since the largest amount of money and valuable information is concentrated inside. How can financial business owners protect themselves from fraudulent attempts and keep their reputation clean? Machine learning in banking is one of the most advanced security measures at the moment, so let’s find out how fraud detection analysis works and how your financial business can benefit from integrating such solutions into your processes.
What Is Fraud Detection in Banking?
Fraud detection in banking means the totality of all technological and protective measures taken to prevent fraudulent attempts, hacker attacks, and any possible fraud on the part of the client.
At this point, the banking fraud prevention process is increasingly relying on real-time data manipulation, which prevents fraudulent attempts even at the intent stage. This has become possible thanks to the integration of artificial intelligence, machine learning, and blockchain into the banking sector.
What Are the Main Challenges Banks Facing Today
The infographic below illustrates the challenges banks facing around the world today. As you can see, the problem of data hacking remains the main one for all banks, regardless of their location. In addition, data breaches and hacking are the biggest sources of losses for the banking sector, as they mean losses both for the organization itself and for its clients.
The development of mobile banking, tight integration of all types of business with social networks gives attackers even more opportunities to collect data for the next hacking, password guessing, and identity theft.
More data needs better security systems. That is why artificial intelligence and machine learning in banking are the technologies on which the leading financial companies around the world are placing the highest bets. Let’s find out how it works and what fraudulent scenarios can be recognized and prevented using anti-fraud systems based on AI and ML.
How Does Machine Learning Detect Fraud?
So how do artificial intelligence and machine learning in banking prevent fraud? The picture below shows this as simply as possible, but let us explain it in the same simple terms.
The machine learning system designed to prevent fraud is self-learning and works using data provided by a banking organization. In order for the system to be able to draw the line between legitimate and illegal operations, it is necessary to provide it with information about what is the norm. Thanks to its ability to identify anomalies, the machine learning algorithm can draw the right conclusions about whether a certain action is legitimate.
For example, if the system knows that a certain client is conducting banking transactions using an IP address based in the USA, then an attempt to make a transaction from an IP address based in Taiwan would be suspicious. At this moment, the system blocks this attempt, and also sends the user a notification that someone tried to perform an operation from a suspicious IP.
AI/ML Fraud Detection vs Old Approaches
According to the global Banking Fraud Survey, “financial institutions face a significant challenge to outpace fraudsters’ changing techniques. Banks are increasingly looking to enhance systems through enhanced transaction monitoring enabled by machine learning/artificial intelligence and biometric access management.”
But why are these technologies so effective for fraud detection in fintech? They are effective because they meet the following three characteristics.
- Fast- Anti-fraudulent machine learning systems work in real-time to prevent hacking attempts and avoid consequences.
- Efficient- The integration of machine learning systems and AI in banking business processes is becoming more and more accessible, even for novice startups in the field of financial technology. And effective protection allows you to avoid risks associated with the safety of data and other assets, as well as preserve your reputation.
- Secure- The probability of an algorithm’s error is extremely small. What is more, a machine learning system never makes the same mistake again, since it is always in the process of self-training.
What Types of Fraud Scenarios Can Be Covered With ML Solutions?
Since machine learning systems in banking work with the help of the data that the company itself provides for training, then they can cover any scenarios that they will be trained in. Here are the scenarios that can be prevented with the help of Fraud Detection with AI and machine learning solutions for banking:
- Authorized push payment fraud (APP)
- SMS spoofing
- Deep Fakes and voice biometrics
- Social and voice banking
- Breaching 2FA
- Institutional disruption
- Return of the ATM
How to Use Artificial Intelligence in Banking to Prevent Fraud
So, here’s how you can use machine learning and artificial intelligence in banking to minimize fraudulent attempts and stop them at the intent stage.
- Analyze customers’ data- Data analysis is the most powerful ability of artificial intelligence and machine learning. By analyzing customer data, it is possible to determine their involvement in money laundering, terrorist financing, as well as eliminate doubts about the authenticity of the customer’s identity by analyzing biometric indicators.
- Track fraudulent- Credit card transactions. Credit card fraud is the most common form of online financial fraud. In order to identify such cases, it is necessary to analyze a huge amount of data carefully noting any details and anomalies. But fortunately, anti-fraudulent AI and ML systems are quite capable of doing this.
- Prevent fraudulent insurance claims- Insurance fraud is also a common type of financial fraud. In order to prevent such attempts, it is also necessary to apply data analysis. In addition, modern IoT devices help determine intent by analyzing the expression on the client’s face when signing an insurance policy or filing a claim for damages.
- Monitor behavior patterns- Your customers’ behavior may change but still remain legitimate. In order for the machine learning system not to give out a false-positive result, it is necessary to monitor the changing behavior and continue to train the system to distinguish between lawful and illegal actions.
- Evaluate investing risks- Emotional investing is a bad strategy. The machine learning algorithm allows you to assess investment risks objectively, relying only on historical and projected data, and excluding the possibility of error.
Artificial Intelligence in Banking Case Studies
Here are some of the artificial intelligence in banking case studies to find out how leading banks are already utilizing this technology for their benefit.
- JP Morgan Chase uses the ML system to detect malware, Trojans, and fishing emails sent to employees. Surely, it is used along with strong employee training, however, it is efficient to detect such attempts with ML as well.
- Danske Bank uses a deep learning algorithm to automate routine tasks, reduce the false-positive results, and leverage the fraud detection capabilities.
- HSBC pays great attention to the customer’s behavior. Their ML-powered system analyses the historical patterns related to money laundering and matches them with the behavioral patterns of the customers to be sure that they are not involved in this crime.
So, bank fraud detection with machine learning and artificial intelligence is the safest and promising way of dealing with external fraudsters and dishonest customers. What is more, such systems are becoming more and more affordable every year and can be developed to solve a highly-specific problem of your business. However, make sure that you are going to get in touch with a reliable AI and ML development partner who will protect your data and create a really outstanding solution that will meet your business needs!