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Leverage AI for Anomaly Detection in Banking

Fraud is one of the greatest threats to the Banking, Financial Services and Insurance (BFSI) industry and its clients, resulting in billions of dollars in damages each year, leading to exponential growth in the Fraud Detection and Prevention Market by the year 2025. As a result, banks, insurance and financial companies turn towards Artificial Intelligence (AI) and Machine Learning (ML) to counter this challenge.

AI experts believe that organizations should not wait for the ‘best’ AI to serve their needs. Instead, they should start using it now because contemporary AI technology is like a secure investment that grows over time to deliver multifold benefits in the future. As a result, many organizations have begun to tap into the inventive power of AI and are seeing great results.

Let us explore key areas where AI and ML play critical roles in detecting fraud and other anomalies in the banking sector.

Fraud detection for apps

Apps are the driving forces of today’s business. Unfortunately, it also happens to be a vulnerable target. Modern AI solutions can assist in detecting fraudulent activities in apps early in the process. It uses complex anomaly detection algorithms that can swiftly look for links between credit card and loan applications and monitor newly opened accounts to prevent financial harm before it happens.

Using unsupervised machine learning (UML) technologies – which learns patterns from untagged data – banking organizations can detect ever-evolving attack patterns, collate the most accurate results, and facilitate immediate protection.

Money laundering detection

Money laundering usually lurks under the radar, and thus it goes undetected. But today’s AI can be trained to monitor spending and deposit patterns. Its algorithms are devised to track various data points beginning from the root of a transaction to its destination and identify deviations from normal patterns. Then AI can pick up anomalies and block payments before they can be completed.

Fraudulent transactions

Cybercriminals use innovative methods to steal bank account/credit card details and then use that information to swipe off money from the victim’s accounts fraudulently.

Banks are increasingly using machine learning models to detect suspicious and fraudulent transactions in near real-time, blocking them from happening and even alerting authorities automatically. Recent research shows that ML systems can detect 30% more frauds with 90% accuracy. As a result, AI and ML security implementation has now become a vital aspect of many banking infrastructures.

Insurance claims frauds

False insurance claims from scammers can lead to huge losses for insurance companies. Many insurance companies are using AI to identify fraudulent claims before it’s too late.

Advanced predictive analytics is widely deployed in this area. An adequately structured predictive analytics solution can tell if a claim requires investigation or not within 10 seconds. The AI and predictive analytics solutions are immensely helping insurance companies where fraudulent claims detection is said to have increased over the past few years.

Phishing threat detection

Phishing is a looming threat that many large organizations fall prey to. In fact, some of the most devastating cybercrimes, occurred in the last decade were through phishing. Malicious links are sent over emails. When unsuspecting people click open, it spreads through their network systems. The perpetrators can steal large volumes of data, banking details, and credit card information.

Banks and email companies have started to implement Artificial Intelligence and Machine Learning to identify and prevent phishing scams. Machine Learning typically uses two deep learning classifiers to deal with this menace.

The first classifier processes email headers (using a deep neural network) to detect signs of malicious software such as a Ratware – that automatically generates and sends mass messages. The second classifier is designed to analyze incoming messages and identify phishing phrases in them.

Using these two classifiers, the Machine Learning solutions can easily decide whether the messages arriving in the mailbox contain phishing context or not. If it does, it immediately sends them to the spam section and alerts the user.


Today’s advanced machine learning models are designed to help organizations to reap benefits right from the start. They are designed to shorten the learning curve, demanding less interference from the organizations and quick onboarding. Along with an effective in-house security protocol, AI and ML can play a critical role in preventing and reducing overall fraudulent activities in the banking sector.

Netlabs Global ( is a leader in providing Robotics Process Automation, Artificial Intelligence and Machine Learning solutions and services to help organizations defend themselves against the growing cyber threats. Talk to us today to learn more about how our solutions and services can help your business.

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  1. Anomaly detection-based fraud detection and prevention solutions are more common than predictive and prescriptive analytics. This type of application requires a much more common machine learning model that is trained on a continuous stream of incoming data. The model is trained to have a baseline sense of normalcy for the contents of banking transactions, loan applications, or information for opening a new account.

    The software can then notify a human monitor of any deviations from the normal pattern so that they may review it. The monitor can accept or reject this alert, which signals to the machine learning model that its determination of fraud from a transaction, application, or customer information is correct or not.

    This would further train the machine learning model to “understand” that the deviation it found was either fraud or a new acceptable deviation.

    This kind of baseline could also be established for interactions with various other banking operations or entities. In addition to account owners, fraud can come from merchants and issuers, and their transaction information can be used to train a machine learning model to recognize transactions processing properly. This would usually involve pricing, but could also involve the omission of unpaid merchandise.