The future of AI in building credit portfolios

Mark Jolley

November 6, 2025

For managers of fixed-income portfolios, whether investment grade (IG) with an emphasis on stability or high yield (HY) chasing higher spreads amid volatility, AI is becoming indispensable.

By integrating machine learning (ML), predictive analytics, and big data processing, AI enables more precise security selection, dynamic allocation and risk-adjusted optimization, outperforming traditional models in opaque, fragmented bond markets.

This shift from static to adaptive strategies is driving alpha generation, with the fixed-income AI market segment expected to grow rapidly as tools mature. Below, we outline the tailored case, drawing on recent advancements.

Key benefits of AI in IG and HY portfolio construction

AI transforms bond portfolio building by analyzing vast, structured and unstructured datasets - like issuer financials, market sentiment, and corporate announcement - to forecast spreads, defaults, and liquidity.

Here's a focused summary:

Benefit	Description	Impact/Examples
Enhanced credit and spread prediction	ML models evaluate issuer-specific risks (e.g., default probabilities, accounting quality and momentum) using alternative data, outperforming legacy scores for both IG stability and HY opportunism.	Improves spread forecasting; AllianceBernstein's ML ranks bonds by valuation and sentiment, boosting IG ETF returns via anomaly detection.
Dynamic allocation and optimization	AI simulates scenarios for duration, sector weights, and yield curve positioning, rebalancing in real-time amid rate volatility or AI-driven economic shifts.	Generates superior risk-adjusted returns; Vanguard's active strategies exploit curve opportunities in different economic growth scenarios for IG vs. HY credit tilts.
Liquidity and pricing intelligence	Predictive tools aggregate fragmented quotes to estimate fair values and trade levels in illiquid HY markets or IG off-the-run bonds.	Reduces execution slippage; SOLVE's 2025 AI pricing covers 100,000+ corporates, enabling confident trades and alpha in low-touch strategies.
Alpha generation via signal discovery	AI uncovers hidden patterns in big data (e.g., peer pricing, economic indicators) for tactical overweights in undervalued HY or IG sectors.	Enhances total returns; ML-driven portfolios deliver better diversification and dynamic rebalancing, per CFA Institute Research on fixed-income applications.
Risk management and scenario testing	Stress-tests portfolios against AI boom/bust outcomes, mitigating tail risks like widening HY spreads or IG downgrades.	Lowers volatility; AllianceBernstein’s integration of machine learning for real-time pricing in fixed-income strategies expands trading universes, feeding intraday signals for resilient HY-inclusive core-plus builds.
Advanced corporate default prediction	AI-powered engines scrutinize accounting data across 14 risk clusters to detect manipulation and forecast defaults/collapses 2-3 years in advance with forensic detail.	Transparently.ai's Risk Engine predicts corporate collapse with over 90% accuracy up to three years in advance, with near-zero false positives. It generates alpha by default avoidance and spread narrowing via superior security selection. Real-time API integration for portfolio screening, allowing credit analysts to stress-test bond holdings en masse.

These advantages allow investment-grade managers to tighten spreads, while high-yield counterparts capture upside in volatile environments, all while scaling portfolios efficiently.

Real-world case studies

SOLVE's predictive pricing for corporate bonds: Launched in June 2025 for IG and HY corporates, this AI tool processes millions of unstructured quotes to deliver trade-level prices with confidence scores. Portfolio managers use it for rapid security screening and execution, saving hours on fragmented data analysis and enabling systematic quoting - key for HY liquidity challenges. Early adopters report refined pricing boosting portfolio yields without added risk.
AllianceBernstein's AI-enhanced ETFs: In funds like the AB Corporate Bond ETF (EYEG) for IG and AB Core Plus Bond ETF (CPLS) with HY exposure, AI drives a three-step process: ML-based bond ranking (valuation, momentum, sentiment), portfolio optimization, and skilled implementation. This uncovers pricing inefficiencies, estimates defaults beyond traditional models, and generates alpha through data-driven selection - outperforming benchmarks in 2025's rate environment.
Vanguard's active fixed-income strategies: AI informs risk-taking by modeling AI-spending impacts on yields, creating opportunities like duration bets in flatter IG curves during growth phases or credit overweights in HY if AI falters. This scenario-based construction helps managers navigate higher rates, emphasizing reinvestment yields and exploiting dislocations for resilient portfolios.

Considerations and challenges

Implementation requires high-quality data feeds and hybrid human-AI oversight to address biases in machine learning signals, ensuring alignment with fiduciary standards. Funds typically begin with pricing tools for quick wins in IG/HY trading, then scale to full optimization. AI-savvy managers then seek tools such as the Transparently risk engine for security selection. The risk directly enhances credit analysis, the cornerstone of fixed income portfolio management, by quantifying "soft" accounting risks that traditional models often miss.

As 2025 unfolds, firms like SOLVE and AllianceBernstein demonstrate that responsible AI integration not only complies with evolving regs but amplifies competitive edges in bond markets.

AI equips fixed-income managers to build superior IG and HY portfolios with higher yields, lower drawdowns, and an adaptive edge amid increased volatility. AI is a must-adopt tool in an environment of accelerating innovation.

‍

Mark Jolley

Mark has been an investment strategist for almost 40 years and has advised some of the world’s biggest investment funds, public companies and notable investors. In the mid-1990s, as a global investment strategist with Deutsche Bank, Mark produced a daily note read by more than 16,000 investment professionals including voting members of the Federal Reserve and the European Central Bank. In the 2000s, Mark worked as Deutsche Bank’s Asian strategist and then as strategist for China Construction Bank. He is now an independent analyst.

Credit

AI in accounting

AI in asset management

Analysis