A practitioner's reference for credit professionals evaluating AI tools and workflows.
Published by Accretive AI. Last updated: April 2026.
About This Glossary
Most "AI for finance" glossaries are written by technology vendors who don't understand credit. Most credit glossaries are written by bankers who don't understand AI. This glossary is written for the audience that lives at the intersection: credit analysts, portfolio managers, COOs, and fund principals at private credit and ABL managers who are being asked to evaluate AI tools without a clear vocabulary to do so.
Every term below is defined twice — once as it's used in private credit, and once as it applies when AI is inserted into the workflow. The goal is to make vendor conversations, internal debates, and investment committee memos on AI tooling measurably sharper.
If a term is missing that you'd find useful, email KD@goaccretive.ai.
Table of Contents
- Asset-Based Lending & Borrowing Base Terms
- Cash Flow Credit & Covenant Terms
- Credit Modeling & Portfolio Operations Terms
- AI Workflow Terms for Private Credit
- Frequently Asked Questions
Asset-Based Lending & Borrowing Base Terms
Advance Rate
The percentage of a borrower's eligible collateral that a lender will fund against. Typical ABL advance rates: 80–90% on eligible accounts receivable, 50–65% on eligible inventory (higher on finished goods, lower on raw materials or work-in-process), and 70–85% on net orderly liquidation value for machinery and equipment.
AI relevance: Advance rates are stored in credit agreements and re-tested at every borrowing base certificate. AI systems that claim to monitor borrowing bases must read advance rate logic from the executed credit agreement (including any amendments) rather than from a simplified summary. Tools that flatten advance rate logic into a single number per collateral class miss tiered structures and category-specific carve-outs.
Borrowing Base
The calculated maximum amount a lender will fund against a borrower's eligible collateral at a point in time. The formula is straightforward in concept — sum of (eligible collateral category × advance rate) — but every real credit agreement contains definitional complexity: what counts as eligible, what reserves apply, what dilution assumptions are baked in, and how the calculation changes as the borrower's business evolves.
AI relevance: A borrowing base tracker is not a spreadsheet — it is a living application of the credit agreement's definitions to a monthly or weekly stream of borrower-provided data. AI tools that monitor borrowing bases must handle eligibility screens (cross-aging, concentration, foreign, government, intercompany), reserve calculations, and the specific timing conventions each agreement uses. Flattening this into a generic "AR × 85%" ignores where the actual risk sits.
Eligibility Criteria
The contractual tests that determine whether a given asset counts toward the borrowing base. For AR, typical criteria include: not past due beyond a specified threshold (usually 90 days from invoice or 60 days past due), not cross-aged (if an account has aged receivables, fresh invoices to the same account may be ineligible), not owed by an affiliate or government entity, not concentrated beyond a cap (often 15–25% of total eligible AR), and not subject to contra accounts or offsets.
AI relevance: Eligibility is where manual borrowing base monitoring fails most often. Every credit agreement has its own eligibility definition, and borrowers frequently include ineligibles in their reported AR balances. AI workflows that monitor eligibility must be configured to the specific credit agreement — generic templates produce generic errors.
Concentration Limits
Contractual caps on how much of the borrowing base any single obligor or obligor group can represent. A typical ABL agreement might cap any one account at 15–25% of total eligible AR (with a named exception list for large, investment-grade obligors). The purpose is to limit single-obligor risk inside a pool that is nominally diversified.
AI relevance: Testing concentration limits requires pulling the aging file, grouping by obligor (including affiliated entities), and comparing each group's share to the contract cap. AI systems that automate this must handle obligor grouping rules (parent-subsidiary relationships, common-control affiliates) as specified in the credit agreement.
Dilution
A measure of how much of a borrower's AR balance fails to convert to cash — typically expressed as a trailing 12-month ratio of credits, write-offs, and discounts to gross sales. Dilution of 2–4% is common and benign; dilution trending above 6% or spiking in a single period warrants investigation. Dilution reserves are then sometimes required to be taken against the borrowing base.
AI relevance: Dilution is computed from data the borrower reports each month. An AI workflow that ingests sales journals and credit memos alongside the AR aging can compute dilution more consistently than manual monitoring. The challenge is categorization of credits — a true bad debt write-off is different from a seasonal return allowance, and the credit agreement may specify which categories count.
Dominion of Funds (Cash Dominion)
A control structure in which the borrower's customers remit directly into a lender-controlled lockbox, and the lender sweeps cash daily to pay down the revolver before releasing any residual to the borrower. "Full dominion" (always in effect) is a stronger structure than "springing dominion" (triggers only on covenant breach or availability threshold).
AI relevance: Dominion reconciliation is a classic back-office pain point — matching lockbox deposits to specific invoices and customer remittances, handling short pays, and reconciling against the borrower's cash application. AI workflows that automate this produce measurable labor savings but must be configured to the specific bank's data format.
Excess Availability
The amount of remaining borrowing capacity on a revolver: (borrowing base − outstanding revolver balance − letters of credit − reserves). Excess availability is the most watched number in ABL. Many agreements include a "liquidity covenant" — minimum excess availability that must be maintained — as the primary financial covenant, in lieu of or in addition to leverage/coverage tests.
AI relevance: AI dashboards for ABL portfolios should surface excess availability as a time-series with forward projections based on known seasonality and draw patterns. Point-in-time snapshots miss deterioration trends; 12-month forward liquidity projections are materially more useful for credit committees.
Net Orderly Liquidation Value (NOLV)
The estimated cash recovery value of inventory or equipment if liquidated in an orderly sale (not a fire sale), net of liquidation costs. NOLV is typically established by third-party appraisers and refreshed every 12–18 months. NOLV advance rates are usually expressed as a percentage of NOLV, not cost.
AI relevance: NOLV is a data input, not something AI should estimate. The AI workflow's job is to ingest the appraisal, apply the contractual advance rate, and flag when a new appraisal is overdue or when inventory composition has shifted enough that the prior NOLV assumption may no longer hold.
Springing Covenants
Financial covenants that apply only when a trigger condition is met — typically when excess availability falls below a specified threshold (often 10–15% of the facility). Until triggered, the borrower is not tested on leverage or fixed charge coverage. Once triggered, standard covenant tests begin.
AI relevance: Monitoring springing covenants requires continuous measurement of the trigger condition even when covenants are not currently tested. An AI workflow should flag borrowers approaching the springing threshold before covenants become active, not after. This is a lead indicator most credit systems miss.
Inventory Turnover
The ratio of cost of goods sold to average inventory, expressed in turns per year. A healthy ABL-eligible borrower typically turns inventory at least 2.0x per year; below 1.5x is a yellow flag that may indicate obsolete stock or weakening demand. Turnover by category (raw materials, WIP, finished goods) often reveals more than the blended number.
AI relevance: Turnover trending is a core monitoring metric that AI can compute from the inventory perpetual file each month. The value is in the trend and category decomposition, not the point-in-time figure. AI workflows that compute only blended turnover miss the signal.
Cash Flow Credit & Covenant Terms
Adjusted EBITDA
EBITDA plus a set of add-backs defined in the credit agreement. Typical contractually permitted add-backs include: one-time restructuring costs, transaction fees, stock-based compensation, non-recurring litigation expense, and projected cost synergies (usually capped at a percentage of EBITDA and limited to 24 months). Every credit agreement defines Adjusted EBITDA slightly differently; there is no GAAP equivalent.
AI relevance: Adjusted EBITDA is one of the most gamed metrics in private credit. AI workflows that populate credit models from borrower-provided compliance certificates must read the add-back definition from the credit agreement and cross-check reported add-backs against the contractual definitions. Generic "EBITDA extraction" tools miss this entirely.
Cash Interest
Interest actually paid in cash during a period, distinct from accrued interest or PIK interest. In credit models, cash interest is what matters for coverage calculations (FCCR, DSCR) and for the true cash burden of the capital structure.
AI relevance: A credit model that computes interest as a simple "debt × rate" formula is wrong whenever PIK, delayed-draw, or step-ups apply. AI-generated credit models must distinguish cash interest from total interest and apply the correct formulas separately — this is a common error in AI-built models that have not been reviewed by a credit professional.
Covenant Cushion (Headroom)
The gap between a borrower's actual performance and the covenant threshold, expressed either in absolute terms or as a percentage. A leverage covenant at 5.0x with actual leverage of 4.25x has 15% headroom. Headroom trending toward zero over multiple quarters is the canonical early warning sign.
AI relevance: AI monitoring systems should compute headroom every quarter and track the trajectory, not just the point-in-time value. A borrower at 20% headroom with a quarterly rate of decline of 3 percentage points is materially different from one holding steady at 20%. Trajectory-aware flagging is where the differentiated value shows up.
Financial Covenants (Maintenance)
Contractual financial tests that must be satisfied every period (usually quarterly). Common maintenance covenants in cash flow deals: total leverage (debt / EBITDA), fixed charge coverage (FCCR), interest coverage, and minimum liquidity. Maintenance covenants are the lender's primary ongoing control mechanism. A breach is a default, though typically subject to equity cure rights.
AI relevance: Covenant testing is mechanical but demands precision. AI workflows that test covenants must use the exact contractual definitions (including add-back rules and covenant-specific carve-outs) and must flag a breach or near-breach before the compliance certificate is due, not after.
Financial Covenants (Incurrence)
Tests that apply only when the borrower wants to take a specific action — incur additional debt, make a dividend, execute an acquisition, or make a restricted payment. Incurrence tests do not test ongoing performance; they test whether the borrower has room in the "baskets" to do what it wants to do.
AI relevance: Incurrence covenant tracking is a different workflow from maintenance covenant tracking. It requires tracking basket capacity over time (how much the borrower has used, how much remains) and testing specific proposed actions against remaining capacity. AI workflows that monetize restrictive payment and acquisition capacity are underbuilt in most portfolios.
Fixed Charge Coverage Ratio (FCCR)
A measure of a borrower's ability to cover its fixed financial obligations with cash earnings. The standard formula is (EBITDA − unfinanced capex) / (cash interest + scheduled principal + cash taxes). An FCCR at or above 1.10x is common as a covenant threshold in middle-market deals.
AI relevance: FCCR is sensitive to every input in the numerator and denominator. AI-generated credit models must use the contractually defined FCCR formula (including whether capex is fully deducted or only the "unfinanced" portion) rather than a generic template. This is one of the most common errors in AI-built models.
Free Cash Flow (FCF)
Cash available to debt holders after all operating cash needs are met. FCF is not EBITDA minus capex. The proper FCF waterfall: Adjusted EBITDA → cash-based adjustments → Cash EBITDA → less cash interest → less cash taxes → less capex → less mandatory amortization → less change in net working capital → Levered Free Cash Flow.
AI relevance: Any AI tool that computes FCF as EBITDA − capex should be considered incorrect until proven otherwise. This shortcut, common in generic financial analysis templates, produces materially misleading results in levered credit situations. Credit-specific AI workflows must implement the full waterfall.
Leverage Ratio (Total, Senior, Net)
The most common covenant in cash flow credit. Three primary variants: (1) Total Leverage = Total Debt / EBITDA; (2) Senior Leverage = Senior Secured Debt / EBITDA; (3) Net Leverage = (Total Debt − Cash) / EBITDA. The covenant definition in the credit agreement specifies which variant applies and how each numerator component is defined — especially what counts as "Debt" (letters of credit? synthetic lease obligations? contingent obligations?) and what cash netting is permitted.
AI relevance: Misreading which leverage definition applies is a recurring AI error. The contractual definitions almost always differ from the GAAP or investor-presentation definitions. AI workflows must read the definitions from the specific credit agreement, not infer them from financials.
PIK Interest (Payment-in-Kind)
Interest that accrues and is added to the loan principal balance rather than paid in cash. PIK is common in unitranche and mezzanine structures and in stressed-credit amendments. PIK does not reduce cash availability at the borrower but does increase debt balances and accrued interest over time.
AI relevance: Credit models with PIK tranches require separate cash interest and accrued interest calculations. A model that applies a single blended rate to total debt will compute cash interest incorrectly. AI-generated models must implement PIK logic explicitly — this is another frequent source of model error when AI builds from a simple template.
Unitranche
A single-facility debt structure that combines first-lien and second-lien economics into one blended tranche, typically with a single rate and a single lender group. Unitranche is the dominant structure in middle-market direct lending below $500M of debt. Amendments, covenant waivers, and recoveries are governed by an agreement among lenders (AAL) behind the scenes.
AI relevance: Unitranche modeling is simpler than split-lien (one tranche, one rate, one covenant package), but the AAL introduces intercreditor complexity that surfaces only in stress scenarios. Standard credit templates accommodate unitranche easily; recovery modeling in stress scenarios is where AI tools with generic templates get it wrong.
Credit Modeling & Portfolio Operations Terms
Compliance Certificate
The document a borrower delivers each quarter (or monthly in ABL) certifying compliance with financial covenants, with supporting calculations. Format varies by credit agreement but typically includes: EBITDA buildup with add-backs, leverage and coverage calculations, and officer's certification. This is the single most important borrower reporting deliverable in direct lending.
AI relevance: Compliance certificates are the natural input for AI-powered portfolio monitoring workflows. Extraction is straightforward; the value is in cross-checking the borrower's reported covenant math against the contractual definitions and against the lender's own credit model. AI tools that only extract numbers without re-computing miss most of the risk.
Credit Model (Lender's)
An Excel-based financial model maintained by the lender for each borrower, used to project performance, test covenants, and inform ongoing credit decisions. Well-built credit models include: actuals ingest from compliance certificates, lender case projections, management case projections (usually identical to lender case until stress emerges), a downside case, and full debt schedule with covenant testing.
AI relevance: AI-generated or AI-updated credit models are now viable but require strong structural discipline. The most common failure mode: AI builds a model with hardcoded values where formulas should exist, making future updates manual. Credit-specific AI workflows must enforce formula-driven structure, dynamic covenant testing, and validated debt waterfalls — not just plausible-looking Excel output.
Credit Policy
A fund's internal rules governing what deals are eligible, what terms are acceptable, what concentration limits apply at the portfolio level, and what monitoring cadence is required. A credit policy is the governance document that translates LP mandates into actual investment behavior.
AI relevance: For AI workflows to operate within a fund's actual decision framework, the credit policy must be encoded as part of the system's configuration. A "credit screening" agent that does not apply the fund's specific credit box (industry exclusions, size thresholds, pricing floors) is running generic logic on fund-specific deals — which is what creates the AI-is-useless-for-us objection.
Data Room
A curated repository of transaction documents provided by a seller or borrower during a financing process. Typical contents: CIM, financial models, audited and interim financials, legal documents, commercial diligence reports. Data rooms have evolved from physical rooms to Intralinks / Datasite virtual platforms.
AI relevance: AI-powered data room analysis is one of the most mature use cases in credit. The challenge is not extraction — it's relevance filtering. A good credit analyst reads what matters and ignores what doesn't; AI tools often treat every document equally, burying important signals in exhaustive summaries.
Direct Lending
Private credit extended directly by non-bank lenders (usually private credit funds) to middle-market companies, typically first-lien senior secured. The term is often used loosely to encompass unitranche, first-lien, and second-lien structures. U.S. direct lending AUM exceeded $1.7 trillion by 2026.
AI relevance: Direct lending workflows are a superset of the canonical credit lifecycle — origination, screening, underwriting, closing, monitoring, amendment, exit. Each stage has distinct AI opportunities. The term is useful for buyer segmentation but unhelpful for workflow scoping, which requires stage-level specificity.
Loan Management System (LMS)
The system of record for loan-level data, payment processing, and accounting integration. Common platforms in middle-market credit: Allvue, Everest, FIS's ACBS, Tandem, LoanIQ (larger), QBE (niche). More recent entrants include Hypercore AI, which combines loan management with an AI-native admin agent. Back office teams live in the LMS; underwriting teams rarely touch it.
AI relevance: Integration with the LMS is where portfolio monitoring AI either creates real operational leverage or becomes another data island. Most AI tools in the category produce outputs that still require manual re-entry into the LMS — which is where the promised time savings evaporate. Direct LMS integration is the Architect-tier workflow that separates embedded AI systems from document processing tools.
Portfolio Monitoring
The recurring process of ingesting borrower financial reports, updating credit models, testing covenants, preparing quarterly variance memos, drafting management questions, and producing portfolio-level reporting. A well-run credit fund with 25 borrowers spends 200–300 analyst hours per quarter on monitoring.
AI relevance: Portfolio monitoring is the single highest-leverage AI target in mid-market private credit because it is recurring, structured, and labor-intensive. Quality AI monitoring workflows reduce analyst time by 50–70% while improving consistency. The word "monitoring" hides workflow complexity — AI tools that claim to "monitor portfolios" often cover only a narrow slice (document extraction, or dashboard visualization) and leave the harder steps to humans.
Variance Analysis
The quarterly process of comparing actual results to budget, to the prior quarter, to the lender case projections, and to the prior year. Good variance analysis explains why the numbers moved, not just that they moved. This is a core analyst deliverable and a common pain point — most funds' variance analysis is less rigorous than it should be because it is time-expensive.
AI relevance: AI-generated variance analysis is viable when the system has access to the credit model, the compliance certificate, management commentary, and accumulated borrower memory (known patterns, prior add-backs, seasonal effects). Without these inputs, AI variance narratives read as generic. With them, they read as analyst work-product.
AI Workflow Terms for Private Credit
Agent (in Credit Workflows)
An AI system configured to complete a specific, multi-step workflow autonomously — for example, a "Borrowing Base Monitoring Agent" that ingests the monthly borrower package, tests eligibility criteria, computes the borrowing base, compares against reported, and surfaces exceptions. An agent is differentiated from a single-prompt tool by having memory, tools, and a defined scope of decisions it is authorized to make.
Key distinction: In credit workflows, "agent" should imply a deterministic scope — the system does a defined thing reliably, not an open-ended "credit analyst AI" that produces different outputs each time. Credit work demands repeatability.
Agentic AI
A broader category term for AI systems built around one or more agents that can complete workflows autonomously rather than serve as chat interfaces. "Agentic AI" has become the dominant vendor positioning in private credit as of 2026, with Gartner publishing Cool Vendor reports specifically for Agentic AI in Banking and Investment Services.
Practical implication: Most vendors now call their products "agentic." Not all of them actually are. A useful test: does the system complete a workflow end-to-end without a human stepping in, or does it produce outputs a human then assembles into a deliverable? The former is agentic; the latter is an assisted copilot.
Amendment Ingestion
The workflow of reading an executed amendment or waiver, identifying what changed from the prior agreement, and updating the lender's internal configuration (covenant schedules, thresholds, add-back definitions, reporting requirements) accordingly. Manually, this is a painful cross-reference process that many portfolios do inconsistently.
AI relevance: Amendment ingestion is a high-value agent workflow because it attacks a specific, recurring operational gap. A well-built amendment ingestion agent reads the amendment, produces a diff against the prior credit agreement, proposes configuration updates, and waits for human confirmation. The value is in the structured diff and configuration update — not in summarization.
Borrower Memory
A structured data layer that accumulates everything the lender knows about a specific borrower across quarters: confirmed add-backs, known seasonal patterns, management commentary history, covenant trajectory, and resolved flags from prior periods. Borrower memory is what transforms an AI monitoring agent from a generic analyzer into a borrower-specific analyst.
Operational detail: Borrower memory is typically a structured JSON or database record per borrower, injected into the AI system's context at runtime. It is built once during onboarding from the credit file and then updated automatically after each monitoring cycle. It is one of the strongest switching-cost mechanisms in AI credit workflows — the longer the system runs, the more valuable the accumulated memory becomes.
Chain of Custody (AI Audit Trail)
The documented record of what AI did, what data it used, what human reviewed the output, and what decisions resulted. For credit funds subject to SEC Rule 204-2 recordkeeping (Registered Investment Advisors), the chain of custody requirement applies to AI-generated work product that informs investment decisions just as it applies to any other analytical output.
Practical implication: Most "AI for finance" tools produce outputs without a native audit trail — the analyst copies text from a chat window into a memo. Registered advisors using AI in the investment process should be able to reconstruct, months later, what data fed into the AI, which version of the prompt or skill was used, and who reviewed the output.
Cross-Model Verification
The architectural pattern of having one AI model produce an output (extraction, analysis, memo draft) and a different AI model from a different vendor family independently verify it. The verifier reads the original source and the first model's output and reports discrepancies, omissions, and potential errors.
Why this matters: Same-model self-verification shares systematic blind spots — the model will not flag errors that its own architecture is biased toward producing. Cross-model verification (e.g., Claude produces, Gemini verifies; or vice versa) breaks this correlation and is the structurally correct pattern for high-stakes analytical work. For credit, where errors in extraction or calculation can be costly, cross-model verification is the appropriate baseline.
Input Layer (Prompt & Skill Architecture)
The encoded instructions, examples, constraints, and domain knowledge that a practitioner builds to direct an AI system toward producing consistently high-quality outputs in a specific domain. The term comes from Zack Shapiro's thesis: "The quality of what comes out is almost entirely a function of what I put in. The magic lives in the input layer." In credit, the input layer is where 15 years of domain expertise get encoded into reusable instructions.
Why this matters: Generic AI wrappers (a pretty UI over a generic model) are commoditized — anyone can build them. The differentiated value in vertical AI is in the input layer: the specific prompt architectures, skill files, configuration files, and domain judgments that someone with the right expertise built over time. The input layer is the defensible IP, not the model choice. See The Input Layer Thesis for Private Credit for the full argument.
Skill File
A structured markdown document that encodes a specific operational pattern — for example, how to build a credit model with proper FCF waterfalls, dynamic covenant testing, and no hardcoded calculated values. Skills are loaded at runtime and applied by the AI system automatically when the relevant task is detected. Skills transform ad hoc prompting into consistent, version-controlled instruction sets.
Operational detail: A credit model standards skill might specify: never define FCF as EBITDA − capex; always implement the full waterfall; never hardcode cash interest when a formula is available; always build a dynamic debt schedule. Applied consistently, skills encode a practitioner's work standards into every output the AI produces.
Tool Use (Function Calling)
The pattern of giving an AI model access to specific tools (read a file, write to a spreadsheet, query a database, call an API, execute a calculation) and allowing it to decide which to invoke and when. Tool use is what transforms AI from a chat interface into an operational agent.
For credit: Tool use lets an AI agent read a compliance certificate (PDF extraction tool), populate a credit model (Excel write tool), compute variance (calculation tool), and log outputs (storage tool) in a single orchestrated run. The quality of a credit-AI workflow is largely a function of how well its tools are designed and constrained.
Frequently Asked Questions
What is the best AI tool for private credit portfolio monitoring?
There is no single best tool. The right system depends on portfolio size, reporting cadence, existing infrastructure, and the specific workflows being targeted. For mid-market funds with 10–75 borrowers, a configured workflow on a general-purpose AI platform (Claude, ChatGPT Enterprise, or similar) with credit-specific prompt architecture and borrower memory will typically outperform a dedicated SaaS platform on both cost and fit. For larger funds with 100+ borrowers and integrated LMS infrastructure, dedicated platforms become more viable. See our vendor landscape page for specifics.
Can AI replace a credit analyst?
No, and the question usually signals a misunderstanding of where the value lives. AI systems can substantially reduce the time analysts spend on mechanical work — data extraction, model population, standard variance analysis, document diff generation — while preserving the judgment work that requires human credit experience. Well-designed AI workflows typically compress a 60-hour quarterly monitoring cycle into 20 hours without reducing output quality.
Is AI-generated credit analysis safe to use in an investment process?
It is safe when the AI workflow has been architected with appropriate controls: credit-specific skills, structured borrower memory, cross-model verification, explicit human review gates, and a persistent audit trail. It is unsafe when analysts paste into a generic chat interface and accept outputs without verification. The safety distinction is almost entirely about the workflow architecture, not the underlying model.
Does AI understand credit documents?
Modern AI models handle well-structured credit documents (compliance certificates, financial statements, standard credit agreements) with high accuracy when prompted correctly. Complex legal language (intercreditor agreements, multi-party waivers, novel deal structures) requires either careful prompt design or cross-model verification to avoid errors. Out-of-the-box AI performance on credit documents varies significantly by document type and model — benchmarking on your own document set is recommended before production use.
What is the difference between an AI platform and an AI workflow service?
An AI platform is software your team learns and operates — you pay a subscription and your analysts use it. An AI workflow service is an engagement where external practitioners configure, build, and maintain AI-driven workflows for you — your team consumes outputs without learning the tool. For smaller funds without dedicated technology resources, the service model generally produces faster time-to-value and lower total cost of ownership. For larger funds with internal teams, the platform model may be preferable.
What should a private credit firm ask an AI vendor before signing?
Seven questions that surface the difference between a polished demo and a production-grade system. Can the tool read my specific credit agreement, or does it apply generic templates? Can it handle amendments and covenant changes over time, or does it rebuild each period from scratch? What is the audit trail format, and does it satisfy Rule 204-2 for Registered Investment Advisors? Does the model verify its own outputs, or is human review the only check? What data does the system retain, and where? What happens to my configurations if I switch vendors? What is the vendor's ownership and funding status, and what happens to my data if they are acquired? Any vendor that cannot give clear answers is telling you something.
About Accretive AI
Accretive AI encodes senior private credit expertise into AI workflows for private credit and ABL fund managers, primarily $200M–$20B AUM. Our team has 15+ years at leading private credit managers and billions deployed across direct lending, ABL, leveraged finance, and special situations. Accretive delivers finished workflows — portfolio monitoring, deal screening and underwriting, Excel credit modeling, borrowing base tracking, and borrower memory systems — without clients needing to learn or operate AI tools themselves.
Contact: KD@goaccretive.ai Web: goaccretive.ai
This glossary is maintained as a living reference. Suggestions, corrections, and requests for additional terms are welcome.