Machine Learning (ML) and Artificial intelligence (AI) are algorithms that identify patterns in data sets and imitate decision-making, like humans. Both these principles replicate human learning to improve their accuracy and efficiency gradually. They utilize statistical data to analyze and uncover insights to make relevant predictions.

In recent years, ML and AI have gained a strong foothold in financial risk management, particularly in banking and similar institutions. They have been used to detect fraud, identify warning signs in customer lending, and improve compliance according to regulations.

The growth of AI-powered solutions has revolutionized financial risk controls and improved regulatory compliance management. Everything that includes risks, and their associated controls is on the table for AI-driven use cases.

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Applications of AI and ML Techniques

Credit Risk

Banks and other financial firms have relied on linear and profit regression methods to model their credit risk. However, with ML and AI, the firms have realized that their credit risk management can significantly improve and help them make better financial decisions. A combination of traditional statistics with bankruptcy prediction and network algorithms can make the model of credit risk analysis accurate.

An area where machine learning for credit risk has improved is the analysis of credit default swaps. The CDS market has many uncertain elements that determine the possibility of credit defaults and estimation of the costs if it materializes. Banks have increased reliance on ML and AI to make better SME and consumer lending decisions.

Market Risk

Market risk emerges from the exposure of the financial market, including trading and investment in assets such as stocks or bonds. ML and AI have made their mark in several key areas of market risk, including:

  • One of the hurdles banks faces is using incomplete, false, invalid, or unspecified model parameters that significantly increase the market risk. AI and ML can reveal the issues that these models have and recommend any needed changes. These can also identify any unsuitable assets within the trading model.
  • Clustering techniques used by AI and ML look for ways to reduce transaction costs associated with large trades that have the potential of moving the market rate (especially in the liquidity market).
  • Traders receive a real-time warning through the combination of neural networks and decision trees. These warnings of changes in underlying trading patterns enable traders to take precautionary measures and mitigate their losses.

Operational Risks

Operational risk is the concern emanating from the internal and external breakdown of operations. These include employee errors, neglected processes, fraud, or downtimes due to cyber and/or natural disasters. Since operational risk has become more complex and frequent, firms have explored ML and AI solutions to manage them. Such solutions enable firms to:

  • Identify operational risk and its exposure and measure their impact
  • Identify potential mitigation strategies
  • Find instruments that can help in shifting or trading risk

AI and ML automate operational tasks that can act as practical tools against fraud. They learn patterns to minimize human errors, process unstructured data, and evaluate emerging issues. AI tools can help traders by combining trade data with electronic voice commands to signal alerts for immediate response.

Regulatory Compliance

Any bank or financial firm that wants to operate successfully must comply with myriad risk and compliance regulations. Many larger financial organizations have incorporated RegTech, a subset of fintech that utilizes AI and ML algorithms to deliver efficient regulatory requirements. AI is an excellent tool of RegTech that enables continuous monitoring of activities, providing firms access to real-time insights.

Benefits of Using AI and ML in Financial Risk Management

For the domain of financial risk management, AI and ML offer numerous benefits, including:

Improvement in data processing: AI and ML have made it easier to process structured and unstructured data. AI can also combine data sets to create new variables to unravel critical relationships.

Efficiency: Firms can reduce costs and improve efficiency through automated operational functions.

Predictive Insights: AI and ML automation give real-time insights to banks and financial organizations about exposure to dangers faster than manual methods. They increase preventive risk management and support in developing faster responses in critical situations.

Better Decisions: With better insights and improved efficiency, banks and financial organizations have better decision-making capabilities with predictive insights and proactive risk visibility.

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Conclusion

Automating financial risk management through AI and ML has become today’s need as it helps mitigate dangers and optimize operations. Automation applications have brought a revolutionary change to the financial sector in issue management and better decision making.

Are you interested in seeing how AI automated risk and compliance management can help your firm? Get in touch with our experts for a demo of Predict360, the American Bankers Association (ABA) endorsed solution for risk and compliance.