Analytics at the Core: The Future of Non-Bank Lending and Private Equity

Analytics have transformed non-bank lending and private credit from a niche to a mainstream financing source. This shift, driven by banks pulling back, investors seeking higher returns, and new financial products, has made sophisticated data analysis a strategic must-have.

Private equity (PE) firms that depend on this debt for financing and returns must grapple with the key questions: how analytics will advance credit and liquidity risk measurement, what shifts are expected in data and benchmarks, and how best to realign investment strategies.

What's Driving the Analytics Revolution?

Examining the factors driving this change is crucial to gaining a deeper insight into the shift in non-bank lending.

Traditional credit scores are still used, but lenders are now augmenting them with more data, including cash flow, telecom usage, utility payments, and e-commerce activity. By integrating these various data points, companies can approve more borrowers while obtaining a more accurate risk assessment. This presents a significant opportunity for investors; platforms that effectively manage data quality, mitigate bias, and ensure proper governance will be positioned to expand lending without a substantial rise in defaults.

The Analytics Stack: From Data to Decision 

The analytics revolution is built on a rapidly evolving technology stack that leverages alternative data and advanced modelling.

• Ingestion and Data Intelligence: Beyond traditional credit scores, lenders are now integrating alternative data from cash flow, telecom usage, and e-commerce activity. This is made possible by automated pipelines that ingest real-time data to create rich borrower-level cash flow histories. Platforms like OakNorth's Credit Intelligence Suite go further by blending forecasting, peer benchmarking, and covenant monitoring for sophisticated, forward-looking insights. For PE, this shift from static scores to dynamic credit intelligence offers a proprietary data moat and superior risk assessment.

  
  

• Modelling and Explainable ML: Underwriting at scale now leverages machine learning (ML) models, which are replacing traditional scorecard methods. While powerful, these models introduce the risk of "black boxes." 

  
  

  To address this, the industry is shifting towards explainable ML. Zest AI, a fintech company located in California, develops AI-powered underwriting software for banks, credit unions, and lenders, allowing them to approve more borrowers while upholding compliance and fairness.

  
  

• Upstart, an AI lending marketplace based in the U.S., collaborates with banks and credit unions to streamline consumer loan approvals by utilising alternative factors such as education, employment, and income. Both firms prioritise transparency and auditability, ensuring credit decisions can be clearly explained to regulators and investors. For private equity diligence, this entails assessing accuracy metrics and model governance, version control, and readiness for regulatory compliance.

  
  

• Monitoring and Stress Analytics: The industry is transitioning from quarterly covenant checks to continuous monitoring. Real-time data feeds from bank balances and payroll allow lenders to detect stress earlier, reduce surprise defaults, and facilitate proactive workouts. This infrastructure is a core enabler for scaling private credit operations, directly enhancing recovery economics and portfolio reporting quality for PE owners.

Strategic Implications for Private Equity 

Advanced analytics are reshaping the PE value chain, from due diligence to exit.

• Diligence: PE firms that embed alternative-data credit analytics can more precisely price add-ons, pinpoint refinancing risks, and fine-tune covenant packages. The capability to conduct counterfactual stress scenarios using loan-level data fundamentally changes valuation and financing strategies.

  
  

• Portfolio Monitoring: PE sponsors increasingly request weekly or monthly metrics such as utilisation, roll rates, and cure rates to reduce feedback loops for operational interventions. This enhanced telemetry significantly boosts exit readiness and facilitates more accurate strategic adjustments.

  
  

• Origination: Some PE firms are exploring captive lending arms or collaborations with fintech companies. Analytics are the crucial enabler for this approach, although they introduce balance-sheet complexity and necessitate thorough integration of credit operations.

Case Study: The Carlyle Group 

In June 2025, Carlyle's private credit arm structured a US$2 billion bespoke financing package for Diversified Energy, a U.S. oil and gas company. Using securitisation, Carlyle converted cash flows from producing wells into multiple investment tranches tailored to the borrower's capital needs. 

This internal structuring allowed Carlyle to deliver speed, certainty, and a flexible financing solution without relying on a bank syndicate. For private equity sponsors, the deal clearly illustrates how integrated credit and analytics can enable financial groups to originate, structure, and manage complex transactions in-house.

New Infrastructure and Key Risks 

The private credit sector is developing the essential infrastructure to standardise analytics. Kroll, a global provider of risk and financial advisory solutions, and StepStone Group, a private markets investment firm, have jointly launched new benchmarks that deliver loan-level transparency and performance attribution to private credit. These indices are vital for improving pricing discipline, mark-to-market practices, and capital allocation decisions.

However, this progression introduces new risks. Model risk and data privacy are critical concerns, necessitating strong validation and governance. The IMF and Financial Stability Board have also highlighted concentration risk, as expanding private credit could heighten systemic vulnerabilities if underwriting standards decline.

Practical Takeaways 

To remain competitive, PE firms must take immediate steps:

• Invest in a data-ops layer that processes loan- and borrower-level feeds and facilitates scenario analyses. 

  
  

• Mandate third-party model validation for any ML-driven underwriting utilised in due diligence. 

  
  

• Demand benchmarked reporting from lenders to minimise information asymmetry.

  
  

• Create integrated monitoring dashboards that link credit metrics to portfolio KPIs and exit timing.

Analytics represent the new competitive arena in non-bank lending. For PE firms, the decision is clear: embrace modern credit analytics with robust governance, or risk being at a continual information disadvantage and encountering increased valuation risk. The tools exist; the challenge is integrating them effectively to balance high returns with resilience.