Artificial Intelligence “AI” in Structured Finance

AI (Artificial Intelligence) can play a significant role within structured finance, asset back commercial paper, securitization warehouses and covered bonds programs. Here are some examples on how AI can be applied within structured finance:

• Data mapping, validation and normalization: AI can be utilized to analyse vast amounts of data to confirm data is mapped correctly from source systems to the vendor’ software. Similar AI algorithms can be used to validate the data and transform for normalisation purposes.

• Risk Assessment and Underwriting: AI algorithms can analyze vast amounts of data and perform advanced risk assessments of loans required to be funded by third party originators (structured finance warehouse facility providers and asset-backed commercial paper sponsors). By confirming loans meet eligibility criteria, portfolio parameters business practices, this reduces the operational risk that the lenders are subject to.  AI models can incorporate a wide range of data sources, including origination sources, market trends, and macroeconomic indicators, to provide more accurate risk evaluations.

• PoolGeneration and Portfolio Management: AI-powered tools can assist in selection of structured finance pools. These tools can monitor the performance of various assets, track market conditions, and identify potential risks or opportunities. AI algorithms can analyze historical data to predict future trends and optimize portfolio allocations. This can help investors and financial institutions make informed decisions about asset selection, diversification, and risk management.

• Securitization and Asset Valuation: AI can play a role in the securitization process by assessing the value of underlying assets and structuring the financial instruments. Machine learning algorithms can analyze historical performance data of assets to estimate their expected cash flows, default probabilities, and correlations. This information can assist in pricing the structured products accurately and determining the appropriate level of risk associated with them.

• Fraud Detection: AI technologies can enhance fraud detection in structured finance transactions. Machine learning algorithms can analyze patterns, anomalies, and correlations within large datasets to identify potential fraudulent activities. By applying AI techniques, financial institutions can strengthen their risk management practices and mitigate the risk of fraudulent transactions within structured finance markets.

• Regulatory Compliance: AI can aid in ensuring compliance with complex regulatory requirements. By leveraging natural language processing and machine learning techniques, AI systems can assist in automating regulatory reporting, monitoring changes in regulations, and identifying potential compliance violations. This can help financial institutions navigate the regulatory landscape more efficiently and reduce compliance-related costs.