Traditional credit scoring relies on a limited set of data: payment history on existing credit accounts, amounts owed, length of credit history, credit mix, and new credit inquiries. This approach works for people who have established credit histories. For those without, often called "credit invisible," traditional models provide no path forward.
Alternative data offers a different approach. Cash flow information, utility payment history, rent payments, and other non-traditional data sources can provide signals about creditworthiness that traditional models miss. For gig economy workers with irregular income, for immigrants building new financial lives, for young people just starting out, alternative data can be the difference between access and exclusion.
But alternative data also introduces new risks. The same machine learning models that can identify creditworthy borrowers traditional methods miss can also perpetuate or amplify discrimination. Responsible use requires careful governance.
The Promise of Alternative Data
The core insight behind alternative data is simple: traditional credit data captures only part of a person's financial picture. Someone who pays rent reliably every month demonstrates financial responsibility that traditional credit bureaus may never see. Someone whose bank account shows steady income and careful expense management may be creditworthy even without a long credit history.
Alternative data can include:
Cash flow data calculated from bank account transactions. Income patterns, expense management, and balance maintenance all provide signals about ability to repay.
Utility and telecom payments demonstrate consistent payment behavior on recurring obligations similar to credit payments.
Rent payment history shows reliability on a major monthly expense, often the largest payment a person makes.
Education and employment data can indicate earning potential and stability, though these require careful handling to avoid discrimination.
The impact can be substantial. Lenders using alternative data have reported higher approval rates for traditionally underserved populations without increases in default rates. The data allows them to identify creditworthy borrowers that traditional models would reject.
For the millions of Americans who are credit invisible, and for billions globally without access to traditional credit systems, alternative data can unlock financial inclusion.
The Risks
The same characteristics that make alternative data valuable also make it risky.
Privacy concerns are fundamental. Alternative data often comes from sources people do not expect to inform credit decisions. Cash flow data requires access to bank accounts. Utility data requires relationships with providers. Consumers may not understand or anticipate how their data will be used.
Proxy discrimination can emerge when alternative data correlates with protected characteristics. Geographic data may correlate with race due to residential segregation. Employment data may correlate with gender due to occupational patterns. Machine learning models can find these correlations and use them even when the protected characteristics themselves are not inputs.
Data quality issues are harder to detect with novel sources. Traditional credit data has decades of validation behind it. Alternative data sources are newer, less standardized, and may contain errors or biases that are not yet well understood.
Explainability challenges increase with alternative data. Regulators require that credit denials be accompanied by reasons the applicant can understand and act upon. When decisions are based on complex patterns in cash flow data, articulating clear reasons becomes more difficult.
Regulatory Framework
Financial regulators have recognized both the opportunity and the risk. Guidance from major agencies emphasizes that alternative data can improve credit access while cautioning that consumer protections still apply.
Key principles from regulatory guidance include:
Consumer protection remains paramount. Alternative data does not create exceptions to fair lending and fair credit reporting laws. The same protections that apply to traditional credit decisions apply when alternative data is used.
Consent and disclosure matter. Consumers should understand how their data will be used and have meaningful choices about whether to participate.
Adverse action requirements persist. When alternative data contributes to a credit denial, the applicant is entitled to understand what factors led to that decision.
Responsible use is expected. Organizations should thoroughly assess alternative data sources against existing regulations. This requires compliance management processes that factor in data sensitivity and consumer protection requirements.
Governance for Alternative Data
Meeting these requirements while realizing alternative data's potential requires robust AI governance.
Data Assessment
Before incorporating any alternative data source, organizations should conduct thorough assessment:
Legal compliance review should verify that using the data is permissible under fair lending laws, privacy regulations, and any source-specific restrictions.
Bias evaluation should examine whether the data correlates with protected characteristics and whether using it could create disparate impact.
Quality validation should assess accuracy, completeness, and reliability of the data source.
Consumer impact analysis should consider how use of the data affects consumers, including privacy implications and potential for harm.
Model Development
Machine learning models using alternative data require special attention during development:
Feature analysis should examine what patterns the model is learning. Are the predictive relationships economically meaningful, or is the model exploiting spurious correlations?
Fairness testing should evaluate outcomes across demographic groups. Even if individual features seem neutral, the combination may produce discriminatory outcomes.
Explainability infrastructure should be built in from the start. The ability to explain decisions is not optional when consumers are entitled to adverse action reasons.
Production Monitoring
Once deployed, models using alternative data require continuous monitoring:
Performance tracking should detect drift in model accuracy. Alternative data sources may change over time in ways that affect predictive value.
Fairness monitoring should track outcome disparities across demographic groups. What appears fair at launch may drift toward unfairness as populations or data distributions change.
Compliance monitoring should verify that adverse action reasons remain accurate and actionable.
The Path Forward
Alternative data represents a genuine opportunity to expand credit access to populations that traditional systems have left behind. This is not just good business. It is a step toward a more equitable financial system.
Realizing this opportunity requires taking responsibility seriously. The same technologies that enable expanded access can also perpetuate discrimination in new forms. Organizations that use alternative data without adequate governance create risk for themselves, their customers, and the broader movement toward financial inclusion.
The organizations that get this right will be those that view governance not as a constraint but as an enabler. Strong governance builds trust with regulators, demonstrating responsible practices. It builds trust with consumers, showing that their data is being used fairly. It builds trust with the organization's own leadership, providing assurance that AI-driven decisions are sound.
Alternative data is not inherently good or bad. It is a tool. Whether it advances financial inclusion or creates new forms of exclusion depends on how it is used.
The choice is ours.
