Step $1$: Data Privacy

Financial documents often contain sensitive information, such as customer data, financial transactions, and personal identification numbers. Ensuring that this data is protected and not misused is critical. The project must implement robust data privacy measures, such as encryption, access controls, and data masking.

Step $2$: Data Accuracy

The accuracy of the extracted data is crucial for making informed financial decisions. The project must implement rigorous data validation and verification measures to ensure that the extracted data is accurate and reliable. This includes cross$-$checking data with multiple sources, using advanced AI algorithms, and implementing manual review processes.

Step $3$: Regulatory Compliance

The financial industry is heavily regulated, and any project must comply with all relevant laws and regulations. The project must implement robust compliance measures, such as data retention policies, audit trails, and access controls. This includes complying with regulations such as the General Data Protection Regulation $($GDPR$),$ the Financial Conduct Authority $($FCA$)$ regulations, and the Bank Secrecy Act $($BSA$).$

Step $4$: System Failure

The AI system must be robust and capable of handling large volumes of data. The project must implement measures to ensure that the system can handle unexpected spikes in data volume, network failures, and hardware failures. This includes implementing redundancy measures, such as backup systems and disaster recovery plans.

Step $5$: Human Error

Human error can lead to data breaches, inaccurate data, and regulatory non$-$compliance. The project must implement measures to minimize human error, such as training and education programs, access controls, and audit trails.

Step $6$: Ethical Considerations

The use of AI in the financial industry raises ethical considerations, such as bias, discrimination, and transparency. The project must implement measures to ensure that the AI system is fair, transparent, and unbiased. This includes implementing bias detection and mitigation measures, providing clear explanations of AI decisions, and ensuring that the AI system is aligned with ethical principles

What are the contingency and trigger for aboeve risks