AI can significantly assist in risk management at financial institutions and several specific tools have been developed for this purpose:

1. Fraud Detection Systems: Tools such as Featurespace and Darktrace use machine learning algorithms to analyze transaction data in real time and identify patterns or anomalies that might indicate fraudulent activity.
2. Credit Scoring Models: AI platforms like Zest AI develop advanced machine learning models that analyze a multitude of data points to assess credit risk more accurately and fairly than traditional models.
3. Market Risk Analysis Tools: Systems like IBM's Watson can process vast amounts of market data to identify potential risks and provide insights to inform investment strategies and risk management decisions.
4. Compliance Tools: AI tools like RegBot and ComplyAdvantage can automate the process of regulatory compliance, tracking changes in regulations and ensuring that financial activities are compliant.
5. Operational Risk Management: Platforms like Ayasdi use AI to analyze complex and large datasets to identify patterns that can lead to operational risks such as system failures, process inefficiencies, or human errors.
6. Risk Visualization Tools: Tools like SAS Visual Analytics provide AI-powered data visualization capabilities, allowing financial institutions to visualize risk scenarios and make more informed risk management decisions.
7. Cybersecurity Platforms: AI cybersecurity platforms like Cylance and CrowdStrike can identify and respond to cyber threats in real time, helping to manage cyber risk.
8. Liquidity Risk Management: AI systems can analyze market trends and predict cash flow patterns to help manage liquidity risk.
9. Insurance Underwriting Tools: AI platforms like Atidot and Planck resolve help insurance companies to analyze customer data and assess insurance risk more accurately.

By leveraging these and other AI tools, financial institutions can improve their risk management capabilities, making faster and more accurate risk assessments, identifying potential risks earlier, and responding more effectively when risks do materialize. However, the use of AI in risk management also brings challenges, such as the need for high-quality data, the management of AI systems, and the ethical and regulatory implications of AI decision-making.