Internet fraud, also known as online fraud or cyber fraud, has a significant impact on financial institutions. The rise of the internet and digital technologies has provided new avenues for criminals to engage in fraudulent activities, posing various challenges for banks, credit card companies, and other financial institutions. Detection of these attacks comes via two paths: through inconsistent traditional methods or using ever-growing artificial intelligence (AI) mechanisms. Entrepreneur brought this interesting information to us in their article, “How AI and Machine Learning Are Improving Fraud Detection in Fintech.”
Traditional methods, such as the rule-based method, are still widely used by most fintech companies in contrast to AI. Some companies are adjusting, however, by leveraging machine learning and AI, greatly improving their chances of detecting fraud.
To mitigate the impact of internet fraud, financial institutions continually invest in technological advancements, cybersecurity measures, employee training and customer education.
Understanding the technology is also key. Most organizations have little knowledge regarding how AI systems make decisions, so they are ill-prepared to apply any results they obtain from these technologies. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
