Use cases transforming finance risk management

Risk prediction and assessment

AI assistants enable fast, multi-asset risk analysis. It flags trends and anomalies early across equities, bonds, and currencies. 

One relevant example is this: BNP Paribas partnered with QuantumStreet AI to use IBM’s watsonx platform. The result? Real-time trend signals combined with AI-generated indicators across global markets.

Fraud detection and prevention

AI is redefining fraud prevention. According to the Feedzai 2025 report, 90% of financial institutions now use AI to monitor fraud. Real-time detection has improved performance by up to 50%, with some systems blocking over $350 million in fraud attempts.

Stress testing and scenario planning

Machine learning lets finance teams run thousands of scenarios at scale, which can lead to better speed, accuracy, and regulatory alignment when leveraging this type of technology.

TurinTech’s evoML platform helped a U.K. bank streamline scenario planning. It reduced overfitting, improved macroeconomic analysis, and added clarity to model outputs.

New frontiers: liquidity and ESG risk

AI is starting to track liquidity exposure across accounts and detect climate-related or reputational risk in real time. By scanning policy updates, news, and social media, it gives finance teams early warning signs — critical for managing modern portfolios.

AI implementation roadmap for finance teams

Phase 1: Laying the foundation (Months 1–3)

Audit and strategy
Start by reviewing current systems and identifying operational pain points. Define use cases that save time or reduce error, and research tools that can help you build a governance framework for your business.

Prepare your data

Clean, well-structured data is non-negotiable. This includes scalable storage, clear labeling, and secure access. 

Phase 2: Pilot projects and quick wins (Months 4–8)

Start with low-risk, high-reward tasks
Begin with invoice processing, forecasting, or expense classification. These areas deliver fast ROI and are easy to manage. 

Track KPIs
Measure success by accuracy and time saved. Assess metrics like mean absolute error and processing time.

Equip your team

Invest in data literacy. Training employees to use new automation technology drives adoption. Use tools tailored to finance workflows, and study guidance about change management and change resilience to better support your team in adopting new processes.

Phase 3: Scaling the system (Months 9–12)

Expand proven pilots
Once pilots deliver value, scale across departments. Integrate AI with ERP and reporting platforms. Broaden visibility using cross-asset frameworks.

Match tools to company size
Small firms can benefit from simple tools like invoice OCR. Larger firms may need custom-built models.

Refine based on feedback
Use pilot data to improve workflows. Document processes and refine AI governance as adoption grows.

AI best practices checklist

1. Governance and compliance

• Form an internal AI ethics group.
• Align with frameworks like the EU AI Act.
• Keep audit logs of AI decisions.
• Apply a risk-based framework.

2. Technical oversight to consider

• Monitor data quality and input bias.
• Validate models every three months.
• Involve employees and stakeholders in high-impact decisions.
• Use explainable AI models.

3. Risk management

• Conduct regular AI risk audits.
• Watch for hidden bias in the training data.
• Build backup systems in case AI fails.
• Run crisis simulations and make sure you test its resilience regularly.

4. Change management

• Train teams at all levels. 
• Set clear expectations; AI assists, not replaces.
• Use feedback loops to guide development.
• Document changes and decisions. 

Audit readiness in the AI era

AI is now a key part of audit scopes. A 2024 KPMG report found that 64% of companies expect auditors to evaluate AI controls. Over half also expect reviews of AI maturity.

Real-time tools can support audit readiness by flagging irregularities and automating reconciliation. They align compliance with performance goals.

Meanwhile, the U.S. Treasury is deploying its own AI to prevent fraud, and the Financial Stability Board is calling for tighter AI risk monitoring across jurisdictions.

Scaling AI: ethics, transparency, and culture

Rolling out AI isn’t just technical, it’s cultural. Leadership must support adoption. Ethics boards must review the impact. Teams must understand how models work and when to override them.

Bias isn’t a theory. Poor training data can lead to discriminatory outcomes. Finance teams must test models across different groups and regions.

Transparency in risk management is critical. Regulators and other key stakeholders may ask for logs, documentation, or proof of explainability. Be ready to show how your AI makes decisions.

Conclusion: Act now, lead with purpose

AI is no longer a nice-to-have — it’s a finance essential. It brings faster access to information, sharper fraud detection, and better audit outcomes. But getting it right requires smart planning, cross-team alignment, and strong oversight.

Start now. Build on a solid foundation. Choose ethical tools. Train your people. And stay ahead of regulatory change because tomorrow’s risks won’t wait.