Businesses can benefit from artificial intelligence (AI) deployment in so many ways, from creating highly personalized user experiences to getting valuable insights from big data to boosting their cybersecurity efforts. While today’s AI solutions can’t replace existing cybersecurity systems, they can supplement them, giving more freedom to your human analysts and cybersecurity experts. There are successful cases of using AI for fraud detection in sectors including healthcare, banking, finance, and real estate.
Implementing AI can be challenging and resource-intensive, however, mostly due to the high costs of the technology itself and lack of expertise. To effectively leverage this innovative technology without overspending, businesses need to come up with customized and efficient AI adoption strategies. In this article, I explore several ways your business can effectively leverage AI to detect fraud while minimizing costs.
Does Your Business Actually Need AI To Combat Fraud?
When my company takes on a new project where AI is a potential part of the technology stack, we first evaluate non-AI alternatives, as their deployment can usually save money for the client.
With fraud detection, traditional non-AI systems often rely on techniques like manual reviews, statistical analysis, and rule-based systems. When your business deals with simple or common types of fraud, relying on traditional detection techniques can be more resource-efficient.
These same techniques also can and should be part of AI-based fraud detection solutions. For example, human experts can manually review AI forecasts to exclude bias and unfairness and enable context-aware decisions in complex cases.
Major fraud detection tasks that are best delegated to AI-powered systems include:
- Anomaly detection. AI systems can be trained to establish baseline profiles of user or system behavior and detect deviations in real time. For example, AI can timely identify suspicious transactions or data flows.
- Behavioral analysis. AI-powered solutions can analyze and evaluate behavior patterns, whether of individuals or groups of people, and flag suspicious deviations that may signal potential fraud.
- Complex pattern recognition. AI systems can analyze large volumes of data to identify previously unknown and complex fraud patterns, leading to better detection of zero-day vulnerabilities and synthetic scams.
- Predictive modeling. AI-based solutions can analyze historical data and trends to predict possible malicious activities, enabling proactive fraud prevention.
Aside from specific non-trivial fraud detection tasks, it would be wise to delegate to AI data-intensive cases. This is especially true for industries like finance and e-commerce, where data surges can be so high they can overwhelm non-AI systems.
Now that you understand how AI can help you combat complex types of fraud, it’s time to explore implementation strategies for budget-friendly deployment of this technology.
How Can You Save Time And Money When Adopting AI For Fraud Detection?
Let’s discuss how you can maximize the efficiency of your AI initiatives while minimizing implementation costs. The key lies in adopting the right approach for a specific project. Based on AI projects my company has worked on, there are three common scenarios:
1. Use a ready AI solution
Ready-made AI solutions offer a quick and convenient way to implement fraud detection capabilities. While they may lack customization, they can be cost-effective for small projects and businesses dealing with common types of malicious activity, like transaction or payment fraud.
However, before opting for a ready solution, it’s essential to assess its scalability, compatibility with your existing systems, and alignment with compliance requirements.
2. Customize a pre-trained model
Pre-trained AI models provide a solid foundation for customized fraud detection systems, offering more control and flexibility while balancing cost considerations. Customizing a pre-trained AI model is the most common approach taken by my AI development teams.
In contrast to ready AI solutions, pre-trained models can be customized to your specific needs. At the same time, fine-tuning an existing model usually takes less time and fewer resources compared to building an AI solution from scratch.
3. Build a custom model from scratch
For businesses with unique fraud detection requirements and non-trivial use cases, building a custom AI model may be the best option. While this approach can be more resource-intensive, it allows for unparalleled customization and adaptability, saving your budget in the long run.
Based on projects my company has been involved in, I suggest investing the majority of your resources in preparing quality data. The quality and relevance of the data you use to train and test an AI model directly impact its performance and accuracy.
With detailed planning and the guidance of experts, you can ensure the successful integration of AI-based fraud detection capabilities into your product. To learn the best AI implementation strategies and leverage this technology’s potential to the full, read our detailed overview AI and ML for Fraud Detection: Top Use Cases, Approaches, and Technologies.
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