Structured products are tailored financial instruments that combine traditional securities like bonds or stocks with one or more derivative components, offering investors customized risk-return profiles that standard investments can’t match.
Currently, the industry is at a technological inflection point. With the U.S. market now valued at $300 billion and experiencing a 23% increase in new issuances, artificial intelligence is fundamentally altering how these complex financial instruments are created and managed. This shift presents both challenges and opportunities for issuers—such as investment banks, insurers, broker-dealers, asset management firms, specialized structured product issuers, retail banks, and government-sponsored enterprises—who are ready to modernize their operations and adopt new technologies.
In our latest white paper, we explore how machine learning and AI automation are transforming key areas like product design, risk management, and compliance, illustrated through a real-world case study. Looking ahead, generative AI and advanced language models will further democratize structured products, accelerating product creation and improving secondary market liquidity. Issuers who embrace automation and AI early will lead the next wave of innovation.
Download the full white paper now to explore the future of structured products and learn how your organization can stay competitive in this evolving market.
Types of structured products
Structured products come in a variety of forms to meet diverse investor needs:
- Capital-protected products: Ideal for risk-averse investors, these offer partial or full principal protection while allowing for upside exposure to an underlying asset.
- Yield-enhancing products: Designed for higher returns, these take on additional risk to boost potential income.
- Participation products: These allow investors to benefit from the performance of an underlying asset or index, often with the use of leverage to amplify returns.
- Leverage products: For those seeking magnified exposure, these products amplify gains but also increase the potential for losses.
- Hybrid multi-asset products: Baskets to combine exposures to multiple asset classes—equities, interest rates, commodities, FX—within a single note.
- Credit-linked products: Payouts are tied to the credit risk of a specific entity or basket of entities—corporate bonds, CDS spreads—for potentially higher yield.
- Thematic products: Provide customized exposures around themes like ESG indices, inflation or volatility-linked or unique investment goals.
How market trends and regulatory shifts are reshaping the structured products market
Market trends and regulatory changes are compelling structured products issuers and distributors to make substantial investments in technological infrastructure.
Key trends influencing the market include the dominance of autocallable products, high turnover rates due to frequent redemptions, and a shift towards products offering deeper buffer protections, reflecting evolving risk preferences. Moreover, there is a noticeable diversification in the underlying assets used in these products, moving beyond traditional indices like the S&P 500 to include stocks from sectors such as telecommunications, pharma/biotech, and technology. This diversification is a response to broader investor demands and market dynamics.
In parallel, regulatory changes introduced in 2024 have necessitated a rapid evolution in operations. The amendment to FINRA’s Rule 6730 now requires trade reporting within one minute, highlighting the need for advanced real-time data capture and processing capabilities. Additionally, the SEC’s expanded disclosure requirements and the shift to T+1 settlement protocols call for significant trade-processing infrastructure upgrades. These regulatory adjustments not only demand enhanced compliance but also spur innovation as the industry strives to meet new standards while seizing new market opportunities.
What challenges do issuers face and how can AI help?
The changing nature of the structured product market and shifting regulations has brought both opportunities and also posed challenges along various axes. This is where AI-driven digitalization can play a transformative role—addressing challenges related to manual effort, complexity, and opacity. AI and related technologies offer powerful ways to reimagine how structured products are created, sold, and managed, reducing time-to-market, higher throughput and volume, cost savings, risk reduction, and improved client satisfaction.
Product design and customization
Challenge: Creating structured products requires careful selection of underlyings, option strategies, and payoff mechanisms to meet specific investor goals. This process is complicated by regulatory requirements such as the U.S.’s Regulation Best Interest and the EU’s MiFID II, which mandate precise tailoring to client profiles.
Solution: Here’s how AI platforms accelerate the design and development of structured products.
- Algorithmic structuring for optimized product design: AI models analyze client-specific data—investment goals, risk tolerance, and portfolio holdings—to generate optimal structured product configurations. Advanced algorithms evaluate millions of payoff combinations to balance return potential and risk, ensuring tailored solutions that meet regulatory standards.
- Scalable personalization and rapid structuring: AI reduces the time needed for product customization from hours or days to seconds. Relationship managers can instantly generate tailored proposals, making structured products accessible to a broader range of investors, including smaller ticket sizes.
- Predictive AI for enhanced innovation: Machine learning analyzes past deals and market trends to identify high-performing payoff structures. Generative AI further expands product innovation by creating new payoff formulas, enabling issuers to diversify their offerings and stay ahead of market demands.
Risk management and hedging strategies
Challenge: Every structured product issuance exposes the issuer to multiple market risks, including equity, interest rate, foreign exchange, and volatility risks. Effective risk management requires issuers to hedge exposures through derivatives like options or futures. Market fluctuations require dynamic hedging adjustments to maintain risk neutrality or stay within risk limits. Hedging itself incurs costs—bid-ask spreads, market impact, funding costs—that must be minimized. Moreover, model risk is a persistent challenge—if pricing and hedging models misestimate key variables like volatility or asset correlations, issuers may suffer unexpected losses.
Solution: Here’s how AI-driven solutions are transforming hedging and risk management by optimizing hedging strategies, enhancing risk analytics, and improving pricing accuracy.
- Reinforcement learning for dynamic hedging: AI-powered reinforcement learning continuously adjusts hedge positions based on market data, reducing profit-and-loss variance and hedging costs. Deep hedging models outperform traditional delta-hedging, particularly for complex, path-dependent risks.
- Advanced risk analytics and anomaly detection: Machine learning enhances risk monitoring by identifying concentration risks, exposure anomalies, and liquidity stress points. AI models flag gamma clustering, correlated risks across products, and potential capital reserve strains from multiple simultaneous payouts.
- Market sentiment-driven hedging: AI analyzes news, social media, and alternative data sources to anticipate volatility spikes in underlying assets. Risk managers can proactively adjust hedge positions, minimizing unexpected exposure.
- Capital optimization: AI identifies re-hedging and offsetting opportunities to reduce regulatory capital requirements, free up balance sheet capacity, and improve counterparty risk management.
- Improved pricing models: Machine learning enhances volatility forecasting and stress testing, ensuring more accurate pricing and risk mitigation, particularly in turbulent market conditions.
Compliance, monitoring, and regulatory reporting
Challenge: Ensuring compliance in the structured products market is critical, given the potential penalties for regulatory infractions. Issuers must effectively monitor sales practices and maintain accurate, timely reporting to regulatory authorities. In the US, issuers are required to ensure each product meets regulatory guidelines. These could include: FINRA Rule 2310/2210/2232 (communications, fees, risk, costs, disclosure, performance, customer confirmation, etc.) Rule 2330 (Deferred Variable Annuities), Rule 2111 (Suitability) Guidelines vide Notice 10-09/12-03 SEC Rule 424(b), Reg S-K, 10(b) and 15(c).
Solution: Here’s how AI-based solutions can greatly aid in compliance and monitoring.
- Automated compliance checks: Models can parse draft term sheets and marketing materials to verify that required phrases and risk warnings are present. They can also compare the payoff characteristics against the rules. For example, if a product is too complex or high-risk for a certain client category, an AI-based compliance system could flag it before approval. By embedding these automated checks into the issuance process, issuers can prevent problems rather than reacting to them post facto.
- Surveillance and mis-selling detection: AI-driven monitoring of sales data and client communications can detect patterns of potential mis-selling or inappropriate recommendations. Such surveillance techniques are already widely used in trading, where models detect market abuse patterns. Applying them to structured product sales and distribution significantly enhances compliance oversight.
- Regulatory reporting and data aggregation: Structured products require a lot of data aggregation for regulatory reports, for example, reporting requirements for shelf registration takedowns, SEC S-3, etc. AI-based solutions can automate data collection across systems, collating it into required formats such as FINRA EBS v3—a must for structured products, where data on each note’s features, underlying assets, and investors must be pulled together, from structured and unstructured data sources.
Why a data-first strategy is crucial for issuers leveraging AI
AI is fuelled by data. However, without a well-defined data model, banks risk creating a maze of disjointed information silos that doesn’t allow AI to produce optimum results. One desk might record product terms in spreadsheets, another in an internal database, and a third in a custom application—all using slightly different naming conventions or calculation assumptions, often leading to operational bottlenecks where teams spend precious time reconciling discrepancies or chasing missing details.
A “data-first” strategy in structured product issuance means making the product’s data the single source of truth at inception that ties together design, issuance, distribution, servicing, and risk management processes.
Every key term—underlying assets, payoff formula, barriers, maturities, etc.—is captured in a structured format from the outset. Instead of drafting free-form term sheets and then extracting details, the issuer first defines the note in a structured data format such as JSON or YAML.
Download the whitepaper to explore a simplified JSON representing a principal-protected note (P-Note) capturing all its attributes in a standard, machine-readable way.
All documentation, pricing, and downstream processes then flow from this data. Industry initiatives like the FINOS Common Domain Model (CDM) exemplify this push—providing machine-readable and machine-executable product definitions that align data across systems and processes. This approach ensures consistency across the product lifecycle and enables automation since systems can directly consume standardized data without manual re-entry or interpretation.
Client success story: Structured products automation solution
Discover how Grid Dynamics engineered a transformative automation platform for a major global investment bank, enhancing their structured products operations. This platform streamlined the issuance process and integrated advanced data architecture to ensure coherence across different lifecycle stages, resulting in:
- Reduced time-to-market: The launch time for new structured products decreased from weeks to just 4 days, enabling rapid responses to market opportunities.
- Operational efficiency: Manual efforts were cut by 40%, significantly reducing errors and operational risks.
- Increased capacity and revenue: The platform allowed the bank to handle a 15% increase in product issuances within a year, accessing new market segments and improving competitive pricing.
Ready to transform your structured products strategy? Download our comprehensive white paper now and learn how AI-driven solutions can revolutionize your product design, risk management, and compliance.
