The future of fraud detection lies in the integration of advanced machine learning algorithms, proactive data analytics, and comprehensive internal and external collaboration, setting a new standard for financial security and institutional trustworthiness.

Explainable artificial intelligence is a new concept that aims at bringing transparency and traceability to decision-making powered by machine learning. 

In the future, the complete and fast detection and prevention of fraud would not be possible without the use of artificial intelligence and machine learning. We may expect stricter regulations to play an increasingly prominent role in knowing how customers interact online and what private information may be obtained to be used for making individuality verification and authentication of customers and stakeholders. 

The main inspiration was our human brain and relevant cognitive functions. The main artificial intelligence and machine learning approaches are based on logic and computer programs. Artificial intelligence and machine learning from data took longer to mature and had their breakthrough with the significant increase of available storage and computational resources as well as the vastly larger quantities of data.

The use of artificial intelligence and machine learning shall be the attempt to develop robust real-time data-driven fraud prevention strategies. 

Machine learning which is a subset of artificial intelligence includes algorithms that allow computers and systems to learn from available data without being explicitly programmed. the institutions would require to develop such techniques and systems in their internal processes to manage fraud and incidents.

Data is the new oil. Learning algorithms are refineries that extract information from raw data, information can be used to create knowledge, knowledge leads to understanding, and understanding leads to wisdom. It is a long journey from raw data to building wise computers, and we are only at the very beginning. However, it is fascinating now to see and be able to benefit from the progress that has already been made in the area.

As we know that fraud detection is a process designed to identify anomalies in the data and as such prevent unauthorized financial activities, i.e. unauthorized payment transactions. The anomalies are traditionally detected by applying rule-based models such as a certain number of transactions within a specific time, and of a certain amount, in a particular category. 

Within the last few years, ML models have gained more and more interest due to the ability of ML to learn from historical fraud patterns such as cumulated amounts in the last 24 hours combined with the accumulated number of transactions, and recognize them in the future.

In general, every payment you make with your debit card is vetted by the fraud detection process to ensure the legitimacy of the payment. It goes through a ruleset and possibly also an ML model. What are the main challenges with fraud detection? How does machine learning help tackle them?

The Future of Fraud Detection

With the rapid rise of e-commerce markets, the sophistication and dynamics behind fraudulent behavior have also increased. The enhancement and maintenance of rule-based models is becoming not only more complex but also more time-consuming. It is exactly in this context that the application of machine learning may help.

It may help to reduce manual work by learning new patterns itself and adjusting the decision-making process according to human feedback on fraudulent/non-fraudulent behavior.

Another benefit is what is known as collective intelligence. 

With the increasing maturity of privacy-preserving technologies, i.e. the ability to share insights without sharing the data per se, involved parties such as banks, payment service providers, etc. will become more open to contributing to the collective intelligence network. This concept will enable the training of ML models with more data and therefore better prevent fraudulent behavior.

What are the main techniques used in the area?

Rule-based fraud detection is still the most common practice. However, with the rise of AI during the last decade, ML-based solutions are also slowly gaining in popularity. The bundle of techniques used ranges from decision tree-type algorithms to deep learning. 

In addition, we see more and more solutions leveraging unsupervised learning to detect new patterns of fraud. Feeding classifiers with new labeled data the output of unsupervised learning further improves the ability to act quickly towards detecting unknown fraud patterns.

Machine learning is often called a black box because it’s difficult to understand how a decision was made.

Yes, machine learning is not always self-explanatory or straightforward. Therefore, I believe, knowledge is crucial to increase both acceptance and the level of confidence in using these systems. It is important to educate users about the main principles behind machine learning, its advantages, and its limitations.

Further, making the decision process of a machine learning model understandable is almost as important as the accuracy of these models. 

Final Thoughts

The emergence of explainable artificial intelligence underscores the importance of transparency in machine-learning-driven decisions. As the digital realm evolves, particularly in e-commerce, the ability to promptly detect and prevent fraud becomes paramount. The inspiration for many AI techniques, rooted in emulating human cognitive functions, has now paved the way for cutting-edge fraud detection methods.

Although traditional rule-based systems remain prevalent, the adaptability and dynamic learning offered by machine learning models, especially with the increasing volumes of data available, present an invaluable tool in the combat against fraud. Yet, the opacity of these machine learning models remains a significant challenge. As we harness the power of AI and machine learning, ensuring that these technologies are not only accurate but also comprehensible will be pivotal.