The banking industry is extremely vulnerable to hacks and scams; fraud detection and mitigation should be the topmost priority of the banks. The financial services industry has been getting transformed through the adoption of AI. Deep learning which is an important part of AI is a crucial fixture in the banking industry giving way to infinite possibilities and transforming the way people/organizations bank.
Artificial Intelligence has several use cases in the banking and finance industry, ranging from sales forecasting to risk management. However, fraud detection in banking is surely one of its most impactful use cases. Artificial Intelligence improves fraud detection by combining supervised learning algorithms with unsupervised learning to the effect of gaining a better understanding of customers' behaviors. A better understanding of customers' behavior allows organizations to better identify and prevent unauthorized activity.
In this article, we will discuss how Artificial Intelligence is beneficial for fraud detection and how it works.
Benefits of using Artificial Intelligence for fraud detection in banking
Artificial Intelligence and machine learning are helpful in finding quick and efficient solutions for detecting frauds and malpractices in banking. They enable machines to process large datasets accurately, which is something that humans can falter at. There are several benefits of using artificial Intelligence for fraud detection. Some of them are:
- Fast Detection – Fast computation is a widely known benefit of Artificial Intelligence and machine learning. It develops an understanding of a user’s app usage patterns like transaction methods, payments, etc., which can easily detect an anomaly in real-time. With better efficiency than manual methods, it avoids the occurrence of false positives and gives specialists an opportunity to focus on more complex issues.
- Increased Accuracy – Artificial Intelligence enables your machines to compute massive amounts of data at greater speed with increased accuracy. It gives you a huge set of actionable insights, which empowers your team of analysts to work faster. Artificial Intelligence helps you to differentiate between genuine and fraud customers based on the data they are fed, as well as ensure the authenticity of payment methods.
AI & ML solutions not only work with more precision, but also at a faster speed. So, the risk of blocking genuine customers is less as real-time fraud detection is done with a high rate of accuracy, which in turn retains your clientele and ensures exceptional user experiences.
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Common fraudulent activities in the banking and how can AI help combat them?
Email phishing is done by sending fake sites and messages to users via email, in order to retrieve some confidential information. This information is then used to attack the system and steal valuable info like data and money. Such kind of emails can be misjudged by the human eye, which put users in a vulnerable position and their data or money at risk.
Solution: ML algorithms can differentiate between legitimate and spam email addresses via their content, subject lines, and email details without requiring the user to open the email. Also, by classification models, fraudulent activity can be avoided.
The robbery of a user’s identity linked with their bank accounts is known as identity theft where criminals hack into the accounts, gain access to crucial credentials, and change them so that the user can no longer access these accounts.
Solution: Artificial Intelligence solutions can be used to implement effective security with features like multi-factor authentication and human-like Intelligence. So, if a user’s password is being changed or any updates are being made to their identity, they will be notified immediately. Such actions in real-time can help banking institutions to ensure that their users don’t fall prey to fraudulent activities like identity theft.
Credit Card Theft
Credit card theft can be done via the fraudulent activities mentioned above. By email phishing or identity theft, criminals can access your credit card details and use them for purchases without physically possessing your card. This can also lead to payment fraud.
Solution: AI solutions can learn about a user’s spending patterns and gain actionable insights to make effective predictions about what kind of expenditures are likely to be done in the future. So, if something contrary to the regular happens, the user can be notified immediately, and their card can be blocked. Such a system can detect the fraudulent activity quickly and in real-time to prevent fraud.
Forgery of Documents
Formation of fake IDs, use of fake applications, forged IDs, and illegal purchases of consumer IDs are common these days. These activities can cause a lot of damage to users, especially when it comes to financial transactions. With access to a user’s IDs, fake applications for loans and credit cards can be made in their name, costing them more losses.
Solution: With well-fed machine learning algorithms, an AI powered solution’s neural networks can be trained well enough to detect a forged ID, differentiate between a fake and original identity, and ask for user access before the use of an ID for an application. With the increase in the number of datasets being fed to the machine, the accuracy rate of detecting such fraudulent activities increases, thereby helping banking institutions to implement fool-proof solutions for their users.
Mimicry of Buyer Behaviour
Sometimes, criminals can try to mimic the behaviour of a user to use their credentials for purchases without actually getting caught. This kind of fraudulent activity, although new, is becoming very common to take advantages of the loopholes in the security systems of banking institutions.
Solution: With the help of in-depth understanding of a user’s expenditure patterns and detection of location spoofing details, AI solutions can detect when buyer behaviours are mimicked and take appropriate actions accordingly.
Attacks via Application Protection Solutions
With the use of anti-piracy and anti-detection software, experienced hackers can leverage virtual IPs and different machines to avoid their detection in a system or a regular browser, allowing them to commit the crime without getting caught.
Solution: Machine learning algorithm can decipher the data and learn from it, which can help banking institutions to prevent such fraudulent attacks in the future.
How does AI in fraud detection work?
Gathering and segmentation of data is the first step of the process of fraud detection in banking using AI. Let’s understand various elements and steps that go into the process of leveraging fraud detection via machine learning with an example of a credit card fraud detection process:
- Step 1: Input Data– Input of data is an essential requirement for training the machine learning algorithm and developing it. By feeding segregated data to the AI solution, in different forms, such as good data, fraudulent data, etc., banking institutions can enable their AI solution to learn & perform better.
- Step 2: Extract Features– This step includes extraction of features that play a crucial role in determining the signals for identifying frauds. These features include past transactions, identity details, locations, preferred choices during payment, and network details.
- Step 3: Algorithm Training – To help the machine comprehend the difference and learn to differentiate between legitimate and fraudulent transactions, algorithms are trained with the help of learning data sets so that the machine can predict fraudulent activities accurately.
- Step 4: Model Development– Model development can be done after you’re done training your machine. The model you build can be improved by adding new data or features to the system. Different techniques like the following are used to build models:
- Use of cause-effect relationship to form structured datasets
- Adds value by assessing variables’ predictive power (individual or combinations)
- Comparison of authentic transactions with fraudulent transactions for algorithm development
- A machine learning algorithm for automation of creating rules pertaining to classification tasks
- They can be used for regression or classification predictive modelling challenges
- Examples of fraud faced by clients are used for training these algorithms
- It ignores irrelevant features
- It is open to inspection for understanding the chain of actions
- Use of multiple decision tree combinations for improvement of classification or regression performances
- Enhances the model’s overall performance and accuracy
- Allows you to provide explainable scores to users
- Complement above-mentioned techniques
- Improve with exposure to data
- A constituent of cognitive computing technology (working of the human brain and observation of patterns are mimicked by the machine)
- Adaptive, fast and real-time decision making
- Adapt to change and identify patterns for fraudulent transactions
So, when you leverage a model for credit card fraud detection, it must understand the difference between genuine and fraudulent transactions by analyzing following factors:
- Addition of multiple payment methods from a single account
- Purchase of multiple premium goods in large quantities
- Addition of new, absurd, and fake locations or addresses
- Use of suspicious email IDs
- Mismatching and discrepancies in the name of account, name of the card, etc
Hence, when it comes to Fraud Detection in banking we have seen that AI has helped in minimizing fraudulent banking thefts to reduce operational losses, deepening customer relationship, strengthening a reliable banking reputation towards market & the customers and saving financial losses before they occur. By implementing apt AI driven fraud detection solutions, banking institutions can enhance their fraud detection capabilities extensively.
Discover how Qentelli is bringing AI to the banking industry through early detection and actionable results – Drop us an email -> Info@Qentelli.com