AI Underwriting Private Credit in Emerging Markets: The 2026 Inflection

Private credit in Emerging Markets 2026 has reached a structural turning point. This analysis serves two readers at once: mid-market sponsors seeking non-bank debt, and institutional fund managers weighing where to allocate EM credit. Artificial intelligence (AI) underwriting has expanded the bankable borrower universe, while tariff-induced bank retrenchment has permanently redirected mid-market demand toward non-bank lending.

Private credit is direct lending from institutional fund managers to companies, structured as term loans, credit facilities, or asset-backed finance that bypasses commercial banks and public financial markets. These managers set their own documentation and governance standards rather than applying conventional regulatory underwriting criteria.

Why Private Credit in Emerging Markets Is Now the Primary Debt Channel

The shift is structural: non-bank credit has replaced bank lending as the primary debt channel for mid-market EM companies in 2026. Two forces converged. Post-2023 capital requirements, the latest in a tightening arc that began after the global financial crisis, cut commercial bank appetite for issuers needing US$5MM to US$50MM, set against trade-induced credit contraction. Lenders built the origination and underwriting infrastructure to serve this segment at scale.

The capital flows confirm the shift. Assets in the asset class recorded rapid expansion, increasing fivefold over the past decade to between US$50B and US$100B. Nonbanks now supply 80% of the region’s capital inflows, twice their share two decades ago, and portfolio debt liabilities average 15% of Emerging Markets GDP, up from 9% in 2006. The structural case for EM private credit predates this credit cycle, yet private credit in Emerging Markets 2026 now serves companies that banks cannot reach at speed.

Why Banks Are Retreating From EM Mid-Market Borrowers

Bank credit has thinned at the wholesale level. In Bank for International Settlements (BIS), which compiles central bank data, cross-border lending to all emerging market and developing economies (EMDEs) expanded by only US$70B, set against US$284B flowing to the United States in Q3 2025. Capital rules introduced after 2023 raised the cost of holding mid-market exposure on bank balance sheets. The issuers most affected are the three- to five-year-old corporates that standard bank models struggle to score and that non-bank credit pipelines are now structured to reach.

What Private Credit Offers Asset Managers That Banks No Longer Provide

Private debt fills the space banks have vacated, and direct lenders alongside allocators have moved with it. Global private debt reached US$1.5T to US$2T in assets at end-2024, a scale now drawing financial stability scrutiny, with the region at only US$50B to US$100B, roughly 4% to 6% of the global asset class. That small share is the opening as institutional investors, including pension funds and other allocators, rotate further into private markets. 81% of limited partners (LPs) surveyed in November 2025 plan to hold or increase their private debt commitments through 2026, rising to 89% over the longer term.

What lets lenders reach companies that banks cannot assess is a newer underwriting method.

The capital base is also changing shape, and the market outlook for 2026 reflects it. Assets in evergreen private debt funds reached US$644B by mid-2025, of which roughly US$520B sits in private wealth-focused fund structures. Allocators now reach the asset class through evergreen funds, BDCs, and interval structures that channel private wealth into middle market lending.

Several of the largest firms expect wealth-sourced assets to reach 50% of their capital, and the largest listed managers hold about US$1.7T in permanent capital, around 41% of their assets, while European fundraising hit a record US$65B in the first nine months of 2025 as firms worked to attract capital.

This widens the pool of private assets and private wealth that can reach EM exposure, though most evergreen vehicles concede that liquidity is managed, not intrinsic: redemptions price at net asset value and sit behind gates that create liquidity constraints when many investors exit at once.

How AI Underwriting Is Expanding Deal Origination in Emerging Markets

AI underwriting enables non-bank lenders to assess Emerging Markets companies without standard financial records, deploying machine learning that reads alternative data. Satellite imagery of physical assets, mobile payment volumes, and supply chain signals generate credit assessments that substitute for or supplement three years of audited accounts.

The method expands deal origination reach to issuers that commercial banks have historically declined. The same data infrastructure supports portfolio monitoring, giving non-bank lenders early warning of cash flow stress and credit deterioration before covenant triggers are reached. These systems can flag deviations in borrower performance indicators and detect negative sentiment in borrower communications before formal default signals appear.

Alternative Data vs. Traditional Documentation: How AI Tools Assess Credit Risk

The infrastructure behind algorithmic credit assessment in this borrower segment is already built. 448 alternative credit scoring firms now operate globally, more than 75% founded in the past decade. The common inputs are unstructured data from mobile money, digital wallets, point-of-sale systems, call and SMS metadata, and geolocation, and data quality determines whether these inputs produce bankable credit assessments. About 60% of these firms supply scoring and lending software to financial institution partners rather than lending directly, embedding credit risk assessment in existing workflows.

Which EM Borrower Profiles Now Qualify for Institutional Debt

Which EM companies qualify for machine-learning underwriting, and therefore become accessible to deal sourcing teams, follows from what the models can read. Issuers with digital revenue trails come first, particularly those in digital lending and payments operators, consumer platforms, and logistics firms whose flows are machine-readable. The addressable pool is large. 70% of micro, small, and medium enterprises (MSMEs) in Emerging Markets lack adequate financing, and the formal MSME gap in EMDEs runs to US$5.2T a year.

The redirection already shows in cross-border flows. In BIS figures, commercial lending to emerging Asia declined 6% year on year at the end of Q3 2025, while non-bank financial institution (NBFI) lending expanded 13%.

“The transactions we are closing in 2026 reach borrowers that bank credit committees could not approve three years ago. AI-powered underwriting has permanently expanded the EM deal pipeline.”
– Bart Turtelboom, Chairman & CEO of Delphos.

Supply is only half the story. A second force is pushing existing bank clients toward non-bank options.

How Tariff Disruption Creates Demand for Non-Bank Lending in Emerging Markets

Tariffs reduce EM issuers’ access to bank credit by raising the revenue volatility that standard credit models penalize. Cross-border and export-linked companies draw higher default estimates under trade conditions that conventional lenders find hard to price. Non-bank lenders who build trade-disruption analysis into their structuring, rather than applying bank criteria, close the transactions banks now decline.

How Trade Fragmentation Reduces Bank Appetite for EM Cross-Border Borrowers

Trade fragmentation withdraws bank capacity where non-bank credit is needed most. The global trade finance gap doubled to US$2.5T between 2017 and 2025, with Emerging Markets the most affected. Tariffs add direct pressure on companies exporting to the United States and indirect pressure through slower growth, softer commodity prices, weaker currencies, and rising investor caution across nearly every Emerging Markets sector. As confidence falls, commercial lenders meet lower loan demand and greater strain on existing repayment capacity, and they tighten further.

What Private Credit Managers Now Require to Close in 2026

What private credit firms require differs from bank criteria and the financial models banks rely on, and the difference favors prepared issuers. These institutions set their own documentation and governance standards and conduct independent due diligence, so companies that study what lenders require from EM sponsors can align before the first meeting. Currency exposure sits near the top, which is why the currency structure of EM debt transactions shapes pricing under tariff-induced volatility. The discipline shows in the figures: Emerging Markets borrowers in private credit carry leverage of 3x to 4x against 6x to 7x in developed market corporate direct, with loan-to-value (LTV) ratios on the underlying assets below 40% versus 50% to 60% in developed markets. The combined effect of low leverage and sub-40% LTV is structural downside protection that compensates for the higher information asymmetry that conventional financial models cannot price.

The market has matured on the lender side, too. Private debt has exceeded broadly syndicated loan issuance for B-rated and lower issuers for four consecutive years, reaching approximately US$146B in 2025 against US$85B in broadly syndicated lending. The US direct lending market carries a concentration that the region avoids: by some estimates, software borrowers account for 40% of all sponsor-backed loans. In the US, private debt rose from about US$8B in 2015 to over US$500B, 19% of direct loans, by end-2025, with one-third of funds holding that exposure.

Set side by side, the EM asset class and US direct lending diverge on leverage, loan-to-value, yield, covenant protection, sector exposure, alternative data acceptance, and scale. These ratios describe the asset class in aggregate. The transaction record shows the structuring at the deal level.

Private Credit in Practice: Three Deals Across Three Emerging Markets

These Emerging Markets private credit structures are not theoretical. They run through the company’s track record across Emerging Markets, spanning three regions and three borrower profiles.

• LendMN Senior Secured Debt: In Mongolia, up to US$20MM. Delphos arranged senior secured loans on institutional terms for Mongolia’s digital lending platform, reaching a borrower profile that bank credit committees could not assess through standard financial records.
• Maxxa Financing: In Chile, US$7MM. Delphos structured non-bank financing for a Chilean platform serving small and medium businesses, filling the mid-market gap where bank appetite had contracted under tightening conditions.
• BBOXX Pan-African Debt Facility: In the Democratic Republic of Congo and wider region, US$10MM. Delphos arranged a debt facility for an off-grid electricity provider operating in frontier markets where bank lending infrastructure is structurally absent.

Structure Your Private Credit Transaction With Delphos

Two structural forces now define the market: AI underwriting-led borrower access and tariff-induced bank retrenchment. For established EM sponsors seeking non-bank debt, Capital Raising structures and arranges private debt facilities and connects them to the institutional lenders whose standards this analysis has described. For fund managers building EM exposure, Transaction Advisory assesses borrower viability and builds capital structures to institutional requirements, supported by 38+ years of execution across 100+ countries.

Contact the Delphos team to align your transaction with institutional private credit requirements →