Cash flow underwriting has had a remarkable few years. It went from a concept mainly used by non-bank lenders for higher-risk segments to something JPMorgan Chase announced it would deploy in September 2025.
Datos Insights' survey of 150 U.S. consumer lenders found that 58% are less confident making consumer lending decisions based on traditional credit files and scores than they were a year ago. That is a remarkable shift in sentiment at the top of the market, reflecting a genuine change in how credit risk executives think about the limits of bureau data.
For lenders serving newcomers, this momentum is meaningful. Cash flow underwriting — evaluating a borrower's actual income patterns, spending behavior, and account liquidity rather than a bureau snapshot — is a natural fit for applicants who have limited domestic credit history but active, verifiable financial lives.
Traditional credit evaluation methods struggle to capture the complete financial picture of modern consumers, particularly given the rise of nontraditional income sources and alternative credit products. Newcomers sit squarely in that gap.
However, cash flow underwriting is not a complete solution for this population, and lenders who treat it as one will run into blind spots that cost them, including missed approvals, fraud exposure, and, in the specific gap, it cannot close for African newcomers whose financial lives run through systems that Canadian open banking connections do not reach.
Here is what lenders exploring this approach need to understand before they commit to an implementation strategy.
1. Cash flow data is powerful, but it only covers what's in the connected account
The core mechanism of cash flow underwriting is straightforward. With the borrower's consent, the lender accesses transaction data from their bank account and analyzes income stability, expense patterns, account liquidity, and repayment signals.
Key data points include income stability through regular paycheck or recurring earnings, expense behavior across rent and utilities, and liquidity through average daily balances and savings trends. Advanced analytics can turn these data points into a forward-looking risk profile, enabling lenders to extend credit to consumers who might otherwise be overlooked.
The limitation that does not get discussed enough: this only captures what flows through the connected account. A newcomer who banked primarily through mobile money in Nigeria (M-Pesa in Kenya, MTN MoMo in Cameroon or Ghana, OPay, or Kuda) before arriving in Canada has a rich transaction history that no Canadian open banking connection will retrieve.
That history sits in a different system, in a different country, behind an entirely separate API. The open banking infrastructure that makes cash flow underwriting work in Canada was built around Canadian financial institutions. It does not have native reach into African mobile money platforms or digital banks.
This means a Cameroonian engineer who arrived three months ago and opened a Canadian bank account has roughly 90 days of transaction data in the Canadian system. This is not enough to establish the income stability, expense consistency, and liquidity patterns that give cash flow underwriting its predictive power.
The lender runs the analysis and gets a thin, inconclusive picture, but the model says there is not enough history to make a confident call. Therefore, the borrower is either declined or offered a conservative product for the same structural reason they would have been declined through a bureau check.
Cash flow underwriting, used alone, solves the domestic thin-file problem. It does not solve the cross-border data problem.
2. The consent architecture matters more than most lenders anticipate
One of the practical challenges of cash flow underwriting that does not show up in vendor pitches until implementation is the friction in the consent flow. The borrower must understand what they are authorizing, grant access to their account data, and complete the connection through a third-party aggregator, all before the lender makes any decision.
Experian noted that the data-sharing consent process needs to be as friction-free as possible to encourage consumers to start it in the first place.
For newcomers, this friction is often higher. Many arrive from countries where sharing bank account access with a lender is not an established norm. The concept of open banking is familiar in Canada and the UK, but still unfamiliar in many of the countries newcomers are arriving from.
A consent flow that assumes a borrower already understands why a lender wants to read their bank transactions will see higher drop-off rates among newcomer applicants than among domestic-born ones.
This is not a reason to avoid cash flow underwriting; it is a reason to design the consent experience deliberately. Plain-language explanations of what data is being accessed and why, translated into the borrower's preferred language where possible, and clearly tied to a benefit the borrower can see (a faster decision, access to a product they otherwise wouldn't qualify for) reduce drop-off and increase completion rates.
Lenders who treat the consent step as a legal formality rather than a user-experience problem will consistently undercount the population that cash-flow underwriting is supposed to help.
3. Income normalization is harder for newcomers than for established borrowers
Cash flow models are calibrated around patterns that reflect stable, recurring financial behavior over time. For a borrower who has been in Canada for several years, the model can distinguish genuine income stability from a one-time deposit, identify real expense patterns versus anomalous months, and flag liquidity trends that predict repayment stress.
For a newcomer in their first six months, the pattern recognition is working with too little signal. In 2026, adoption of cash flow lending is not a question of belief; everyone believes in the value of cash flow data. It is a question of sequencing.
Every lender is headed toward broader, lifecycle-wide integration, but the path matters. For newcomers specifically, the question is whether to use cash flow data as a primary underwriting signal, a supplementary one, or as a bridge to extend the dataset when other signals are also present.
The answer for most newcomer applications is that cash flow data works best as the third layer in the decision-making process, not the first. Identity verification establishes who the person is. Foreign credit history provides the longitudinal repayment record that domestic cash flow analysis cannot yet offer.
And cash flow data then adds real-time income and liquidity signals that confirm the foreign record remains current and relevant. Used in isolation, cash flow underwriting for a newcomer with three months of Canadian banking history asks a model to draw a conclusion from a dataset that is structurally too small to support confidence.
4. The African newcomer gap is specifically where cash flow underwriting falls shortest
Cash flow underwriting is particularly well-suited for immigrants who arrive without a domestic credit footprint, but only where the connected bank account reflects their real financial life. For African newcomers, this condition is often not met in the first months after arrival for the reasons described above: their financial history was in mobile money, digital banks, or informal savings structures that Canadian open banking connections cannot reach.
The scale of this gap is significant and growing. JPMorgan Chase's deployment of cash flow underwriting was explicitly paired with cross-border credit bureau data, deploying Nova Credit's Credit Passport alongside Cash Atlas specifically because cash flow underwriting alone does not resolve the newcomer credit problem for borrowers with foreign financial histories.
The largest bank in North America reached that conclusion and structured its implementation accordingly. It is a strong signal about what a complete solution for newcomers underwriting requires.
Cash flow underwriting can bring more people into the financial system by assessing their actual financial behavior, and it is particularly beneficial for underserved populations who may not have extensive credit histories yet have stable incomes and responsible financial habits.
For African newcomers, the stable incomes and responsible financial habits are documented in CRC and FirstCentral bureau records in Nigeria, in Metropol data in Kenya, in Ghana's XDS bureau, and in mobile money transaction histories across Francophone Africa. The issue is not the absence of data. It is that the data lives outside the reach of the tools most lenders are currently deploying.
5. The regulatory environment adds a layer of complexity that is specifically relevant for Africa-sourced data
Cash flow underwriting in Canada is regulated by PIPEDA, while open banking frameworks are still evolving. The CFPB's Section 1033 rule in the United States, which would have mandated consumer data portability across financial institutions, was challenged in court and remains in flux as of mid-2026.
For lenders implementing cash flow underwriting domestically, this regulatory uncertainty is manageable; the market is moving forward regardless, and the operational risks of waiting are higher than those of proceeding. For cross-border data, however, the compliance picture is more complex.
Data retrieved from African mobile money platforms or bureau systems is governed by the data protection frameworks of the source country, such as Nigeria's NDPR, Kenya's Data Protection Act, Ghana's Data Protection Act, as well as Canadian privacy law.
A lender who wants to use African transaction data in their cash flow model needs a compliance framework that spans both sides of that transfer, with auditable consent, documented data processing agreements, and country-specific assessments.
Most cash flow underwriting vendors do not provide this. It requires infrastructure that was built specifically for cross-border African data flows, with the regulatory groundwork already in place.
6. The signals cash flow models treat as noise may be the strongest data for African newcomers
Standard cash flow underwriting models are built on the assumption that the most predictive signals are income regularity and expense-to-income ratios derived from bank account transactions.
For most domestic borrowers, this assumption holds. For African newcomers who were heavy mobile money users before arriving in Canada, some of the most predictive behavioral signals (consistent remittance sending, savings discipline, merchant payment history) are not captured by what the Canadian bank account shows and are the kind of data that standard models lack representation for.
With over 1 billion registered mobile money accounts globally and 763 million in Africa, mobile money is the primary financial transaction layer for hundreds of millions of people across the continent.
A borrower who sent consistent monthly remittances to family over five years, demonstrating income stability, payment commitment, and financial planning capacity, has made a track record that any reasonable underwriting model should find meaningful. But that track record does not appear in a Canadian bank account, in a bureau check, or in a standard cash flow model.
This is the specific signal gap that a combined cross-border identity verification, bureau retrieval, and open-banking cash-flow layer is designed to close.
Each of the three data types contributes something the others cannot: verified identity and fraud signals, longitudinal repayment history, and current income and liquidity behavior. None of the three, used alone, gives a complete picture of an African newcomer applicant. Together, they do.
7. The institutions moving fastest are pairing cash flow with cross-border data
Adoption of cash flow data in 2026 is about sequencing. The winners won't be the teams with the most data; they'll be the teams that turn better data into consistent, auditable decisions at scale.
The pattern emerging among the institutions that are furthest along is not cash-flow underwriting as a standalone. It is cash flow underwriting paired with verified cross-border credit data, deployed together as a layered underwriting model.
JPMorgan Chase's implementation pairs Cash Atlas for bank transaction analysis with Credit Passport for cross-border bureau translation, because lenders quickly found that cash flow underwriting alone does not fully address the newcomer credit problem for borrowers with foreign financial histories.
The same logic applies at every scale. A credit union deploying cash flow underwriting for newcomers from Cameroon or Nigeria will find that it improves their ability to assess borrowers who have been in Canada for 12 or more months and have built up meaningful transaction history. For borrowers in their first six months, the model needs more time to work with.
This is where Zeeh's FlowScore sits in a complete underwriting stack. It is not positioned as a replacement for bureau data or as the sole decision signal. It is the cash flow layer that confirms and extends what ZeehID's verified identity and CreditBridge's normalized African bureau data have already established.
Income is arriving consistently, expenses are stable, and account liquidity is adequate for the proposed product. The foreign repayment record, now validated by current cash behavior, becomes more confident rather than just historical. The underwriter is not guessing; they are confirming.
For lenders building or refining their newcomer underwriting strategy in 2026, the practical conclusion from these seven points is the same as that reached by the largest institutions.
Cash flow underwriting is a necessary part of a modern credit model, and, for the African newcomer population specifically, it works best as a single layer within a broader cross-border data infrastructure rather than as the complete solution. Deploying it alone leaves a gap, and deploying it alongside verified identity and normalized foreign credit data closes it.
Zeeh provides lenders with the complete data stack for African newcomer underwriting: ZeehID for identity verification, CreditBridge for normalized African bureau data, and FlowScore for cash flow analysis, delivered through a single API integration. Book a demo to explore them.
