Nowadays, technology advancements such as Artificial Intelligence and Machine Learning algorithms are being used in banking to identify fraudulent behavior more precisely in real-time and with a lower percentage of incorrect declines.

Many businesses are affected by fraud detection, including banking and financial services, insurance, healthcare, government agencies, and so on. In plain terms, fraud detection is a mechanism for detecting and blocking suspicious activity to prevent businesses from being jeopardized.

What is fraud detection?

Fraud can be an extremely adaptable and technologically sophisticated activity. As a result, the more technology on the market, the more advanced the methods for detecting and fighting fraud should become.

Examples of fraudulent techniques in Fintech

• Fake papers and credit cards are used to commit loan fraud
• Insurance fraud is a serious problem
• Theft of a bank account
• Fraudulent use of credit and debit cards
• Mobile fraud is on the rise
• Identity theft is a serious problem
• Money laundering
• False account statements are used to commit tax fraud

When a scam is committed, the victim is not the only one who suffers, but also the financial institution that is involved, which suffers negative publicity.

As a result, Fintech organizations are actively looking for fraud detection solutions based on modern technologies such as Artificial Intelligence (AI) and Machine Learning.

Why is fraud detection in Fintech important?

As the amount of electronic transactions continues to rise, identifying and preventing fraud using traditional approaches and data analysis becomes increasingly difficult. End-users are still unable to protect themselves against fraud, as it becomes more complex and technologically advanced while building products.

The amount of data in every industry is increasing at an exponential rate, which makes spotting fraud for fintech companies more difficult. Machine Learning systems are required to deal with massive volumes of data. Deep learning fraud detection allows for the identification of hidden fraud situations and well-masked correlations in data using a variety of Machine Learning-based methodologies.

The role of Artificial Intelligence and Machine Learning in detecting financial crimes and scams

Both e-sellers and customers have an interest in keeping fraud away, and AI and ML are the most effective options for both detection and prevention that we currently have.

Fraud prevention teams have fought for years to detect and prevent fraud, especially as strategies become more sophisticated with each passing year.

Machine-learning technology gives fraud control teams unique insights and support by detecting fraud as it occurs and responding rapidly to stop it before any damage is done. Machine Learning technology has a reduced learning cycle because it does not require rebuilding the model in batches and instead modifies it dynamically with each new data.

• While human tests can take days, if not weeks, an ML-driven system with smart data processing capabilities can process data in real-time and provide a result in milliseconds. What’s the result? Decisions and reporting will be made very quickly.

• ML-driven software can identify fraud situations and trends by evaluating prior patterns of fraudulent activity, thanks to its forecasting capabilities. The predicting capability of Machine Learning-based fraud detection systems can give you a competitive advantage.

• Automation is that consistent trend that has remained on top for years and will continue to do so in the future. Fraud detection Machine Learning algorithms provide complete automation of different banking and financial procedures, eliminating human errors and any potential internal fraudulent behavior caused by human participation.

To tackle hackers, more leading banks and financial institutions are already using Machine Learning-based fraud detection technologies.

Mastercard can be a great example. It tracks and processes numerous details linked to the transaction, such as the location of the transaction and the device on which it occurred, as well as purchase data, using an ML-based fraud detection system. Throughout the process, the system evaluates account activity and offers a real-time decision on whether a transaction is fraudulent.

Detecting and preventing fraud must be a dynamic and ongoing process because scammers are continuously looking for new ways and techniques to steal money and personal information.

Conclusion

It’s important to remember that fraud prevention is a dynamic process. Monitoring, detection, decisions, case management, and learning are all part of the cycle.

Your fraud detection system must continuously learn from fraud occurrences and apply the information gained in monitoring and detection procedures.

When developing fraud detection Machine Learning algorithms, you must create a model that can distinguish between genuine and fraudulent actions while also adapting to the new and previously unknown fraud strategies. That is, your Machine Learning algorithms must learn the proper information.

As you can see, a good fraud detection and prevention system should be able to detect fraud and highlight transactions that need to be investigated.

The Machine Learning fraud detection system should be able to learn the proper things from the complex data patterns you have, therefore data analytics should be the foundation of your solution.

To develop better models, well-architected Machine Learning models should allow for the usage of rich data after fraud events. It should provide financial trends and forecasts, as well as assist your company’s analytics in identifying potential flaws in new products and areas of business and gaining insights for improved operational safety.

Please do not hesitate to CONTACT US if you are concerned about the future of your company and require a trustworthy AI fraud detection solution.