Research. Rigor. Results.
ILA applies cutting-edge artificial intelligence and academic rigor from Harvard Business School to consumer marketplace lending — delivering higher yields, lower duration, and near-zero correlation to traditional asset classes.
Sep 2018 – Oct 2025 · Gross returns · Past performance not indicative of future results
Marketplace lending addresses the core tensions in modern portfolio construction — generating higher yields at shorter durations with historically near-zero correlation to equities, bonds, and most other asset classes.
A diversified portfolio of marketplace loans can yield 7–10% annually — significantly above traditional fixed income — while borrowers continue amortizing regardless of macro conditions.
Loans of 36–60 months with ~12-month Macaulay duration means far less interest rate sensitivity than investment-grade or high-yield bond portfolios — protecting investors from rate volatility.
Consumer loans have exhibited the lowest correlation to all major asset classes over the last decade. Small-dollar, amortizing payments create an inherent inelasticity to macroeconomic cycles.
The consumer finance market is approximately $1.3 trillion with expected annual growth of 7–8%. The market created post-GFC rewards sophisticated investors who can identify mispriced risk.
Return correlations vs. consumer loans (2014–2024)
| Asset Class | Correlation to Consumer |
|---|---|
| U.S. Large Cap Stocks | –0.14 |
| U.S. Small Cap Stocks | –0.12 |
| Investment-Grade Bonds | –0.20 |
| High-Yield U.S. Bonds | –0.17 |
| International Bonds | –0.17 |
| REITs | –0.28 |
| Gold | –0.16 |
| Consumer Loans | 1.00 |
Source: Vanguard, LendingClub. Monthly returns Sep 2014–Aug 2024. Past performance not indicative of future results.
Duration vs. peers (years)
ILA's four-step investment process continuously optimizes loan selection using real-time performance feedback, proprietary machine learning, and alternative data sources — capturing alpha invisible to traditional underwriters.
Compute historical IRR, delinquency rates, and return correlations through in-depth loan tape analysis. Identify platform differentiators and opportunistic secondary offerings across the marketplace lending universe.
Gather 500+ data points per loan — credit bureau data, application data, and proprietary external inputs including IRS data and local-area unemployment statistics. Train state-of-the-art neural network models for each platform and loan grade.
The PLS model ranks platforms, selects best-in-class securities, and optimizes portfolio weights for ideal risk/reward. Proprietary loan ordering algorithms execute purchases automatically via direct platform integrations.
Assess portfolio daily through numerous risk factors. Continuously retrain the AI model with expanded, updated inputs. Evaluate external signals and feed findings back to Step 1 to keep the process self-improving.
ILA's Proprietary Loan Scoring model goes far beyond traditional credit metrics, integrating three distinct data dimensions that traditional underwriters ignore.
The model captures non-linear interactions between traditional variables — account utilization, inquiry history, DTI ratios — that FICO scores treat as independent signals but that in combination reveal far more about repayment behavior.
Over 200,000 distinct employee titles appear on loan applications. ILA tokenizes each job title, identifying words strongly correlated with default outcomes — "Director," "Engineer," and "University" are low-default signals; "Driver" and "Owner" are high-default signals.
A proprietary Deep Neural Network assigns risk scores to geographic regions using IRS databases, local unemployment statistics, and historical delinquency data — linked to borrowers via zip code. ILA is invested across 3,100+ distinct zip codes with less than 0.15% in any single area.
Illustration purposes only. Does not represent actual borrowers.
Loan selection effectiveness — average current rate
Back-tested using LendingClub 36-month loans. Current Rate = 1 – Delinquency Rate.
Since September 2018, the ILA Capital US Prime Fund has delivered cumulative gross returns of 64.95% — more than double the next-best comparator, HYG — with a Sharpe ratio of 5.05, dwarfing every traditional fixed-income benchmark.
All returns shown gross before management and other fees. Past performance is not indicative of future results. Gross Sharpe ratio, cumulative return and max drawdown computed from gross returns.
| Fund / Benchmark | Cumulative Return | Sharpe Ratio |
|---|---|---|
| ILA Capital (gross) | 64.95% | 5.05 |
| VSCSX (Short Corp Bond) | 25.30% | 0.89 |
| HYG (High Yield ETF) | 35.31% | 0.51 |
| BND (Total Bond Market) | 15.43% | 0.35 |
| AGG (Core U.S. Aggregate) | 15.82% | 0.36 |
Current Portfolio Characteristics
ILA Capital US Prime Fund, as of December 2024.
The ILA strategy — built on the same proprietary PLS model and disciplined investment process — is available through three distinct vehicles designed for different investor types and objectives.
An Irish-domiciled pooled fund for qualified institutional and international investors, launched in 2018. The flagship vehicle through which ILA has delivered its track record — combining whole loan purchases across LendingClub and Prosper with the full PLS model stack. Offers direct exposure to ILA's AI-driven loan selection with the flexibility of a private fund structure.
Available NowIn collaboration with Performance Trust Capital Partners, BCM/ILA has structured a $100 million consumer loan ABS offering four rated note classes alongside an equity tranche. Loans are selected exclusively using the PLS model and subject to strict eligibility and concentration criteria. The rated note structure offers institutional and qualified investors tranche-specific risk/return profiles with KBRA-rated credit enhancement — a unique entry point for capital that benefits from the ILA edge without the return variability of whole-loan ownership.
Available NowBrookmont Capital Management is pursuing a registered, listed interval fund structure to make the ILA marketplace lending strategy accessible to a broader range of qualified investors — including RIA clients and family offices who require a 1940 Act-registered vehicle. The interval fund will deploy the same ILA PLS model and investment philosophy, with periodic liquidity windows and the transparency of a registered fund. This vehicle is designed to democratize access to a strategy historically available only to the largest institutional investors.
Pending RegistrationEach access vehicle deploys ILA's identical proprietary PLS model and investment process. Vehicle selection should be driven by investor type, regulatory status, liquidity preferences, and return objectives. Contact Brookmont Capital Management for vehicle-specific offering documentation.
ILA's founding partners bring together Harvard Business School academic rigor and more than 28 years of investment management expertise — collaborating for over 15 years to build the definitive AI-driven consumer lending platform.
L.E. Simmons Chaired Professor of Finance at Harvard Business School and Research Associate at the National Bureau of Economic Research. Faculty Co-Chair of the HarvardX Fintech course and the HBS Executive Education course on Family Office Wealth Management. Research published in top finance journals; advises the SEC, USPTO, and has testified before the U.S. Congress. PhD and MBA in Finance from the University of Chicago; BSE and BA in Economics from The Wharton School, University of Pennsylvania.
Principal and CIO at Brookmont Capital Management, an asset manager with over $1 billion in AUM. Founder of Impact Shares, a nonprofit asset manager, and Chairman of the Board for three mutual fund complexes totaling over $8 billion in assets. 28+ years in financial services across hedge funds and private equity, including senior roles at Highland Capital Management. Former CPA. MS in Management Information Systems and BS in Accounting from Texas A&M University.
Director of Technology at Vela Wood; prior experience as In-House Counsel and Software Engineer at Arrived, and as an Associate at Gibson Dunn. Bridges the legal, engineering, and systems architecture dimensions critical to building robust automated loan-selection and ABS reporting infrastructure. JD from NYU School of Law; BS in Biochemistry from the University of Texas.