### Developers are expected to level up their baked-in security measures to prevent financial institution losses due to fraud. This is a tall order, but tools and software can assist developers in this task. Few developers realize the extent of the urgency of finding workable practices in Fintech, where fraud has always been a huge problem for banks and other financial institutions. Synthetic fraud, which involves building a false identity from one or two stolen data points, costs U.S. banks upwards of $20 billion annually. Protections built on static checklists and data are doomed to failure in today's fast-moving world, and designing more fluid user identification and authentication methods that can be checked in real-time is a better strategy. Developers need an idea of where to look for emerging or evolving patterns and should rely on AI and machine learning to detect patterns and anomalies indicating fraud. They also need to consider geographic biases, racial biases, gender biases, and other factors that may affect their systems' ability to identify fraudulent activity. By deploying AI to look for identity theft and synthetic identity account creation, and adding more layers of user authentication, developers can improve their organization's fraud detection capabilities.