The lender’s edge: data strategies for private credit

Learn how to use data to drive strategic growth and transformation in the private lending sector.

In brief

• Alternative asset managers are seeking to transform private credit by equipping operations and back-office functions to support business growth.
• Effective transformation in private credit needs executable data strategies tailored to credit and underlying investment lifecycle challenges.
• Data fluency is the key to establishing trust between front-, middle- and back-office stakeholders during transformation.

The private credit market is now hotter than ever. Since the global financial crisis, banks, wealth managers and FinTech startups are competing with alternative asset managers to generate returns from private debt. The expansion of semiliquid private credit vehicles, such as business development companies (BDCs), is not solely driven by the competitive yields they offer in contrast to traditional bank lending, but also by the increasingly sophisticated channels and distribution networks that are enhancing access in the alternative lending space. As the ecosystem continues to expand with nearly two trillion in assets under management (AUM),¹ asset managers are taking on larger transformational initiatives not only to keep up with the growth but also to manage costs and risks. To that end, firms are establishing strategic initiatives that encompass a broad spectrum, from product diversification to the adoption of emerging technologies and data-led programs, to streamline front-, middle- and back-office operations. Asset managers are placing big bets on private credit to boost margins as the liquid market cools down.² With change rapidly sweeping the rather opaque private credit industry, data remains center stage to realize business value.

To become data- and artificial intelligence (AI)-enabled organizations, firms are accelerating investments in their data programs.³ While many of these data-enabled transformations deliver quick wins and efficiencies within a specific function, they frequently fail to scale across the organization. This is primarily due to communication breakdowns, leading to the inability to bring consensus around the appropriate data for use. To overcome this challenge, alternative asset managers need to have an implementation-ready data strategy to optimize the processes and consumption of underlying data across the investment lifecycle in commonly traded private credit instruments.

Data strategy considerations

Private credit data possess unique characteristics and challenges that require differentiated levels of care. The private credit sector deals with a high volume of complex and often unstructured data, including financial statements, market information and credit ratings, to assess lending risk frequently and precisely. As the sector moves toward increased regulation,⁴ there is a need for rapid data processing and interpretation of diverse information, while maintaining robust data quality and security.

The sector faces significant data quality problems, characterized by a lack of universal identifiers for different market participants, the prevalence of unclean data and a multitude of data vendors, which leads to confusion over data ownership and a cluttered data environment. This disarray calls for an up-front commitment to structuring and cleansing data to confirm its integrity and utility. Drafting a tailored strategy is key to navigating this complex data landscape, as it allows for the customization of solutions that address the unique challenges and needs of private credit stakeholders.

To build a custom and implementation-ready data strategy that not only drives intelligence-led investing but also verifies operational efficiency and regulatory adherence, data leaders must revisit the primary components of an enterprise data strategy: data governance, data architecture and engineering, analytics and AI, and data product management.

Data governance

Having reliable data is the first step in making more precise decisions. Data governance is foundational and the first place to start when launching an enterprise data transformation. Efforts within this area will yield a comprehensive line of sight of where the data sits across systems (data taxonomy), amplify business context by making data points searchable (data catalog) and authorize golden data sources to boost data quality and instill trust (data certifications). Moreover, as the industry expands, data leaders must consider the following:

• Regulatory compliance: A governance framework that aligns with data protection regulations is critical for appropriate data handling, including usage, audit trails and retention. This will increasingly become important as regulatory authorities continue to examine the private credit market to understand its implications on the financial markets.
• Data security and privacy: Data access controls should be appropriately defined to protect sensitive data, such as a borrower’s financial information, loan agreement details and banking data from breaches. Asset managers will need to heighten data protection in line with risk management as key players within the industry continue uncovering risks that place outsized pressure on liquidity, valuation or both.

Data architecture and engineering

After gaining an understanding of which systems contain data sets required to make decisions across the investment lifecycle, focus must be shifted to centralize this data in the cloud by streamlining data ingestion, transformations and integrations between various applications and tools used by different personas. Consolidating data by connecting systems to directly communicate with each other emphasizes the shift toward high-fidelity activities taken on by analysts and the like. For example, by moving fund controllers away from spreadsheets that require manual manipulation and creating a single data layer to trace AUM downstream allows for faster, more risk-free reporting in a fiduciary environment. Thus, when designing enterprise data architectures for a private credit shop, data leaders must factor in:

• Source diversity: This encompasses the ability to systematically integrate structured and unstructured data from a variety of sources to assess deals in private markets within a modular architecture.
• Streaming data: This references the organization’s capability to process real-time transactional, deal-sourcing, repayment and market data to assess loan risk, settlement and default. As the maturity wall looms in a volatile macroeconomic environment, investors need to refinance at optimal times to take advantage of changing interest rates.
• Hyper-scalability: The data infrastructure should accommodate growth in data volume and complexity without degradation in performance as new debt instruments are introduced. Transformations that are sequenced and strategically driven at the enterprise level is key to avoid innovating in siloes, which results in disparate platforms that limit data sharing and scale. 

Once data sets are brought together in a methodical way, insights must be derived to accelerate decision-making. Cloud providers are expanding their capabilities to business users across the front and middle offices, highlighting the trend of democratizing AI tools. Aside from rushing to jump the innovation S-curve by incorporating generative AI (GenAI) chatbots to boost productivity, data leaders should not lose sight of solving for business problems with analytics and AI, such as:

• Real-time monitoring: Develop credit scoring models to analyze nontraditional data points, such as social media behavior, or transaction data to predict and identify potential borrower defaults and assess portfolio risk.
• Accelerated due diligence: Use large language models to streamline interpretation of legal documents, such as debt convents, term sheets, and loan and credit agreements. This will help firms close the gap between capital invested and dry powder by assessing more opportunities faster to identify attractive investments.
• Automated reporting: Leverage AI to confirm ongoing compliance with changing regulations by automatically tracking and applying regulatory updates for reports on loan servicing status, default management and payment tracking.

Data product management

A common theme for data products is deriving the most value from information, in the most optimal manner. Especially after analytics and AI are applied, there must be repeatable and scalable methods to supply data to data consumers. As such, focus must be given to the following across the data value chain:

• Customized delivery: Package data into digestible formats to data consumers across the investment lifecycle by considering user needs, such as supplying loan administrators with clear loan disbursement details or collections managers with clear default management updates.
• Agile methodology: Build cross-functional teams to manage iterative data product development and enable continuous improvement in response to evolving industry requirements.
• Business value and monetization: Measure quantifiable improvements in decision-making, risk reduction and operational efficiencies through data delivery. In addition, develop strategies to monetize data products⁴ externally, such as selling insights to other firms or creating benchmarking services for the growing industry.

These considerations provide data leaders with a North Star to meet the distinctive needs of the rapidly growing private credit sector by unifying people, process and technology with data. With a clear data strategy and alignment across the organization, alternative asset managers are equipped with a strong foundation upon which a common data model can be introduced and effectively utilized to drive transformation at scale.

Common data model

Data is used to make decisions across the private credit investment lifecycle by user personas across the front, middle and back offices supporting the deal. Serving dual roles as data stewards and data users in a fast-paced, multifaceted and technology-enabled environment, user personas encounter challenges in communicating how data is interpreted consistently across the organization.

In order to drive the digital transformation agenda and gain competitive advantage, data leaders must establish a common data model to connect functions within the private credit business. A common data model is a logical data model covering essential data elements, including transaction, position, master data and reference data domains to support reporting and data operations, allowing for information exchange to occur between data sources, applications and entities. A common data model also allows for information exchange between individuals as it establishes a standard terminology, elevating data fluency for all stakeholders.

A common data model not only fosters effective collaboration and efficient decision-making but also reduces complexities in data management. It is the basis to drive consistency and accurate usage by clarifying the terms for entity, fund, deal and tranche for downstream consumption within an enterprise; for example, tax and fund reporting. Data points used across the investment lifecycle specific to front, middle and back offices include:

Select data elements used by the front office:

• Deal identifier
• Fund identifier
• Tranche
• Potential borrower name
• Investment opportunity source
• Investment type (direct lending, mezzanine debt, etc.)
• Due diligence status
• Deal stage/status

Select data elements used by the middle office:

• Risk assessment result
• Provisional loan terms (amount, interest rate, maturity, covenants)
• Debt service coverage ratio
• Deal structuring status
• Internal approval status

Select data elements used by the back office:

• Loan agreement document ID
• Loan servicing status
• Payment status (current, late, default)
• Principal repayment amount
• Interest payment amount
• Collateral valuation
• Compliance check status
• Recovery process status (if applicable)

Common data attributes:

• Entity identifier
• Industry of the borrower
• Geographic location of the borrower
• Credit rating
• Loan purpose
• Financial performance metrics (revenue, EBITDA, net income, etc.)
• Legal and compliance document IDs
• Default risk level
• Portfolio management metrics
• Yield to date
• Book value
• Book yield
• Duration

During transformation, common data models serve as a catalyst for apprising data culture, an often forgotten but crucial component of data strategy. Data fluency turns data into stories, allowing analysts, developers and legal advisors to communicate using the same language and taxonomy.

Conclusion

In summary, a well-executed data strategy propels the private credit business to new heights. The unique data characteristics of this rapidly growing alternatives sector stress the need for seamless integration and interpretation for stakeholders, systems and workflows. As discussed in this paper, following a decision-based approach offers data leaders a methodical yet flexible method to manage and leverage data considerations across the investment lifecycle while modernizing the organization. As alternative asset managers embark on their ambitious transformation journeys, harnessing data strategically will provide lenders with the edge needed to grow the business.

Exhibit 1: Key personas across a typical private credit deal lifecycle by data sets used to make strategic decisions 

Keith Caplan, Sanjesh Dubey, Samrudhi Vaghmare are authors of the article. 

Phil Andriyevsky, Samer Ojjeh, Gavin Kaimowitz, Adam Gahagan contributed to the article.