It feels like the 1980s: analysts are skeptical—Lotus 1-2-3 is their trustworthy spreadsheet, but Excel will soon transform how they get work done. Fast-forward to today, and the structured finance industry is facing a similar shift with the recent advancements in AI.

New AI models are unlocking patterns hidden in vast datasets, automating processes, and revealing insights that often remain in our blind spots. But that’s not the only problem you can solve with AI.

Let’s take a look at 5 key areas of structured finance where AI can bring tangible benefits.

1. Repetitive and Manual Processes: Many workflows, such as cash flow modeling or asset-level analysis, are manual, repetitive, and prone to errors. A lot of the work involves plugging in assumptions, running calculations and tweaking things over and over for different scenarios.
   
   Imagine taking this whole process and automating it. Take stress testing as an example: an AI model can simulate thousands of scenarios in minutes, a great deal faster than traditional models. Advanced pattern recognition could also help catch potential issues early.
   
   
   
2. Incomplete and low-quality data: The sector depends on large volumes of data that are often incomplete or riddled with issues, which undermines the reliability of forecasts and increases risk exposure. Important insights can be buried under inconsistent or incomplete data.
   
   Analysts are using AI to clean and prepare unstructured data for analysis. Automated cleaning—deduplication, flagging/removing missing fields, formatting, etc.—makes life a lot easier. Especially in a sector where data may come from multiple sources, AI can simplify the data integration process.
   
   
   
3. Measuring Unknowns in Portfolio Risk: Portfolio risk analysis can depend on identifying complex patterns in diverse datasets. Traditional models rely heavily on historical data and linear assumptions, failing to capture the more nuanced and dynamic risks of today’s markets.
   
   Stress tests also require modeling multiple inputs and more granular analysis at the asset level, and today’s tools are hard to scale for complex scenarios.
   
   AI models are able to identify correlations between asset performance and macroeconomic indicators, and can integrate non-traditional data sources, offering more reliable predictions. 
   
   Generative AI can further provide clear visualizations of stress test results and generate scenario-specific reports for different audiences.
   
   
   
4. Compliance & Reporting: Legal teams are buried under heaps of documentation with exhaustive details about the deal: 200+ pages of prospectus to read in order to identify key terms and potential issues. And that’s before monitoring for regulatory updates to ensure continuous compliance.
   
   Teams can analyze prospectuses through a smarter interface. Imagine having an assistant that can read the prospectus in minutes, and quickly answer every question while referencing the original document. One example of this is Prospekto, a tool helping professionals quickly make sense of Asset-Backed Securities (ABS) deals. 
   
   Taking it a step further, users could quickly generate compliance reports using data from multiple sources. Secondly, AI can scan and interpret regulatory changes in real-time, helping compliance team stay up to date.
   
   
   
5. Emerging Risks: Trends such as ESG-focused investments and electric vehicle (EV) financing are introducing new datasets and risk factors that traditional models aren’t equipped to handle. 
   
   AI, on the other hand, can handle unconventional data sources, such as ESG ratings, climate impact assessments or EV charging infrastructure data. Thus, it enables a more realistic and accurate prediction of asset performance and borrower risk. It is great for analyzing new relationships—e.g. residual value trends—and uncovering patterns that would escape traditional models.

Barriers to Adoption

The structured finance sector operates at the intersection of complexity and opportunity. While the benefits of AI are compelling, the path to adoption and success is challenging:

• Interpretability and Trust: AI models are often seen as “black boxes,” making it difficult for investors to trust their outputs. Decision-making is high stakes in structured finance, meaning stakeholders need to be able to trust and justify the rationale behind predictions.
  
  This lack of transparency becomes an even greater challenge when it comes to sensitive user data, which is another reason many financial institutions hesitate to adopt AI. The sector needs transparent models to build confidence in AI outputs.
  
  
• Better Access to Clean Data: AI models rely on high-quality data to generate accurate insights, but structured finance datasets are often fragmented, inconsistent, or incomplete. Without clean, well-structured data, even the most advanced AI models can produce unreliable results.
  
  
• Cost Perception: Some institutions see AI as a luxury, a tool for firms with big budgets instead of a necessity. Smaller players sometimes feel they lack the scale to justify the investment. This perception is slowly changing with the rise of affordable and accessible tools. In this sense, the barrier isn’t the actual cost as much as the perceived ROI. 
  
  
• Regulatory Compliance: The structured finance sector is highly regulated, and AI tools have to meet strict compliance standards. On one hand, organisations have to prove that AI recommendations align with regulations. On the other, emerging regulations about AI itself add another layer of complexity. No one wants a technology that could cause compliance headaches in the future.
  
  
• Complexity of Implementing:  Integrating AI into existing workflows might require significant time, resources, and technical expertise, particularly for firms unfamiliar with the technology.
  
  
• In-house Expertise: AI adoption requires skilled professionals who can train and fine-tune models, interpret AI outputs and bridge the gap between technology and business needs. Lack of expertise might lead to poor use of the technology or hesitation.
  

Truth is, structured finance has always been a high-stakes industry, and change won’t come easily—just like for our analysts back in the 90s. Today, data issues, infrastructure, cost concerns and regulatory hurdles are all real challenges. 

For most organizations, adopting AI will not happen overnight. But Excel wasn’t an immediate replacement; it worked alongside existing tools and gradually proved its value in certain areas before it became a necessity.

The potential is impressive, and we may be looking at serious changes in both the tech stack and the company culture of institutions in this space—sooner than we might think.

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