How AI is Solving Data Challenges in Private Credit

The private credit market has experienced substantial growth over the past fifteen years, expanding nearly tenfold to reach $1.5 trillion in 2024, per a Morgan Stanley report. The growth has been driven by institutional investors seeking higher yields and diversification. However, private credit managers face significant challenges alongside this growth, particularly in managing and analyzing large amounts of unstructured data. Unlike public markets, where financial information is structured and readily available, private credit transactions rely on disparate, unstructured, and sometimes incomplete data sources.

Artificial Intelligence (AI) has emerged as a powerful tool to address these data challenges and enhance efficiency in private credit operations. While AI integration presents both opportunities and challenges, its ability to optimize processes is already reshaping the industry.

The Role of AI in Private Credit Data Management

AI-powered tools like Co-Pilot, Chat GPT and other machine learning models have accelerated the conversation around AI adoption in alternative investments. The key to leveraging AI in private credit lies in training models on industry terminology and structuring data in a way AI can interpret.

By utilizing machine learning algorithms, AI can process unstructured data at scale, enhancing accuracy and efficiency in credit decisioning. Research indicates that AI-driven credit risk models can reduce classification errors by up to 50% compared to manual methods, thanks to their capacity for real-time data processing. However, current AI aggregation tools still only achieve about 70% accuracy in data classification, requiring a hybrid approach where human oversight is necessary for validation.

Opportunities

AI is advancing rapidly, unlocking operational efficiencies across various areas in private credit operations, such as data segmentation and standardization, credit risk assessment, compliance, and portfolio monitoring. At Petra Funds Group, we see AI gradually being leveraged to optimize operations in several areas.

• Data Standardization and Aggregation – Private credit managers must process large amounts of unstructured data to capture deal terms and conditions and monitor private company financials to create a holistic view of deal risk management. AI-powered document intelligence tools are being utilized to extract and categorize contractual data, making information more accessible and actionable.
• Enhanced Due Diligence – Firms are using AI to make due diligence more efficient by scanning vast amounts of data to detect inconsistencies and flag potential risks. For example, Mindfields Global developed an AI-powered credit risk agent that integrates generative AI with automation tools to streamline due diligence. The system parses legal documents to verify company registrations and conducts credit assessments to generate credit risk reports in real time. This technology has reduced the due diligence timeline for credit risk evaluation from days to minutes while improving accuracy and fraud detection.
• Compliance and Regulatory Reporting – Regulatory compliance remains a critical challenge for private credit managers operating across multiple jurisdictions. AI-powered automation and machine learning tools are streamlining compliance workflows by monitoring evolving regulations and adapting reporting structures accordingly. Global Finance Group, a multinational banking institution, deployed an AI-driven compliance system using Natural Language Processing (NLP) to interpret regulatory updates and automate reporting adjustments. This initiative reduced regulatory adaptation time by 80% and minimized non-compliance risk by 90%
• Portfolio Monitoring and Risk Management – AI analytics platforms enable private credit managers to track borrower performance more efficiently. By integrating financial data with macroeconomic trends, firms can proactively manage risk and make informed, data-driven investment decisions. For instance, EquityTrade Partners, a London-based investment firm, implemented an AI-based trade surveillance system to analyze transaction data and detect potential compliance breaches. The system enhanced regulatory adherence by reducing non-compliant trades by 70% through pattern recognition and anomaly detection algorithms.

Implementing AI in Private Credit Operations

For private credit funds looking to integrate AI, a structured approach is essential to maximize its effectiveness. Key areas for AI integration include:

• Build a Robust Data Infrastructure – Developing a robust data infrastructure is essential to AI integration. Firms must standardize data formats, establish cloud-based storage solutions, and implement data governance frameworks to create structured datasets for AI training.
• Train AI Models with Internal Data – Training AI models on proprietary datasets enhances their accuracy and relevance to private credit workflows. Firms should start with internal deal data before incorporating third-party sources to refine predictive capabilities.
• AI-Driven Document Processing – Automating document extraction and classification can significantly improve efficiency. NLP tools can process borrower agreements, term sheets, and financial statements, reducing manual input and accelerating data accessibility.
• AI-Assisted Compliance Monitoring – Using AI for regulatory tracking can streamline compliance efforts. Machine learning models can continuously analyze regulatory updates, flag potential risks, and generate automated reports to aid in compliance with regulations.

The Future of AI in Private Credit

As AI continues to evolve, its role in private credit will only grow in significance. Advancements will enable AI to automate more complex financial processes to scale operations while maintaining rigorous risk controls. However, AI will remain a complement to, rather than a replacement for, human expertise. Financial executives will continue to play a critical role in interpreting AI-generated data and making strategic investment decisions.

At Petra Funds Group, we recognize AI’s transformative potential in streamlining private credit operations. As industry participants embrace AI-driven solutions, they will unlock efficiencies, allowing investment professionals to focus on high-value, strategic initiatives.

The use of AI in private credit is reshaping the industry. Firms that proactively adopt AI will gain a competitive edge in an increasingly complex investment landscape.