Technology’s role in private credit is expanding, with digital tools, data management, and artificial intelligence (AI) reshaping underwriting, portfolio monitoring, and reporting processes.
Hosted by DLA Piper and the Private Market Forum, the 2025 Private Credit Technology Summit convened general partners (GPs), limited partners (LPs), and technology leaders to explore the future operating model of private credit.
At the June 17, 2025 event, industry players discussed the rapid evolution of private credit, the drivers behind its explosive growth, and the operational challenges that come with scaling the asset class. Participants examined the shift from manual, spreadsheet-driven workflows to integrated, automated platforms, exploring the potential for data normalization, interoperability, and real-time analytics to help meet the demands of institutional and retail investors.
The discussions further highlighted how partnerships between investment professionals and technology providers can help build scalable, efficient, and resilient private credit platforms for the next phase of industry growth.
Below, we summarize key perspectives and takeaways from each panel discussion.
Expected continuation of growth in private credit
- According to panelists, institutionalization of private credit allocations among LPs (eg, sovereign wealth funds, insurance companies, Canadian pensions) has created “sticky” and sustained demand.
- The industry is experiencing a fundamental shift from a bank-based financial system to one anchored by permanent or long-term pools of capital, with private credit as a central pillar.
- The market has evolved from a focus on small corporate credit to a broader mix that includes asset-backed finance (ABF), large corporate credit, high-yield-grade and investment-grade debt, and the “buy box” for private credit is expanding rapidly.
Needs for technology to improve competitiveness
- The basis of competition is shifting: Origination and underwriting remain important, but workouts, operational scale, and technology-driven efficiency are becoming key differentiators.
- Advanced technology adoption for underwriting, loan monitoring, and portfolio construction is limited; related innovation is largely at the margins or in pilot phases.
- If AI can automate routine tasks and beta tracking activities, it could free up time and resources for human analysts to concentrate on higher-value work, such as in-depth analysis, creative problem-solving, and the pursuit of alpha generation.
Needs for technology to improve operation
- Ingestion and normalization of unstructured data (eg, credit agreements, agent bank notices, portfolio company financials) are major operational challenges.
- Interoperability and integration between systems may be key, as firms often have multiple, siloed systems that do not communicate.
- There is a shift from quarterly, lagged reporting to real-time, on-demand analytics and benchmarking, driven by the needs of sophisticated LPs and wealth platforms as well as increasing interest and participation from the retail investors.
- LPs increasingly evaluate managers based on their data infrastructure, reporting capabilities, and ability to support strategic asset allocation.
- Centralizing deal pipeline data and automating workflows could improve transparency, efficiency, and reduce errors.
Obstacles in adopting technology
- Many firms lack dedicated portfolio management functions, and buyer personas for technology solutions are often unclear.
- Technology is often viewed as a cost center or an ROI-driven purchase, not as a source of competitive advantage.
- Cultural resistance is significant, especially among senior staff who may feel threatened by new technology or are wedded to legacy processes.
- The market is seeing a proliferation of point solutions, but there is a trend toward orchestration and “platformization” to connect disparate tools.
- Data comes in a variety of formats, and every portfolio company may report differently, making it difficult to automate processes.
Suggested solutions
- Bridging front-, middle-, and back-office functions through technology and shared data platforms could help scale operations.
- Collaboration across business units (eg, operations, finance, front office, technology) may be necessary to break down silos and create a unified data strategy.
- Ongoing feedback, collaboration, and industry events can allow professionals to share best practices, drive innovation, and address common challenges.
- Education, change management, and cultural adaptation could help firms realize the full potential of technology in private credit.
Use of AI in private credit
- AI is automating operational, process-driven tasks such as deal screening, investment committee memo creation, financial analysis, and portfolio monitoring.
- Such technology is being used to generate first drafts of memos, spread financials, and conduct desktop research.
- AI tools are also being piloted for stress testing, risk analytics, and natural language querying of portfolio data.
- AI is well suited for repetitive, rules-based tasks; human review largely remains essential for subjective analysis, decision-making, and final signoff.
- AI could “supercharge” analysts and associates, potentially enabling faster ramp-up and more efficient deal processing rather than replacing them.
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