Why This Shift Matters Now

Three key forces are converging: more refined data from open banking and real-time payments, scalable AI models that can effectively analyze streams of transactions, and clearer regulations from authorities. Together, they are changing the landscape of lending, payments, and fraud prevention, while also raising important questions about fairness, privacy, and accountability.

Embedded Lending: From Gimmick to Trusted Utility

Not long ago, embedded lending meant clunky financing buttons at checkout. Today, it’s about invisible underwriting that occurs right at the moment of need. Marketplaces can advance funds to sellers, platforms can finance inventory, and consumers receive tailored offers based on their cash flow instead of harsh one-size-fits-all credit scores.

Cash-Flow Underwriting and Alternative Data

AI systems can analyze bank transactions, payroll histories, and platform data to assess repayment capabilities. This innovative approach, known as cash-flow underwriting, opens doors for borrowers who are responsible but lack traditional credit histories. Companies like Petal have pioneered this with CashScore, which evaluates cash flow patterns from linked bank accounts rather than just using bureau data (Petal).

Credit bureaus and model providers have also rolled out products designed to use richer data responsibly, such as Experian Lift solutions for broader credit access and programs like UltraFICO that allow consumers to opt-in with their bank account data (Experian, FICO).

Where Embedded Lending is Making an Impact

• SMB capital embedded in e-commerce platforms and point-of-sale systems.
• Earned wage access and instant payouts for drivers or couriers that smooth cash flow.
• BNPL (Buy Now, Pay Later) options tailored to repayment behaviors instead of merely using a bureau score.

When done well, embedded lending feels more like a safety feature than a sales tactic: it comes with appropriate limits, clear disclosures, and dynamic credit options that adapt as circumstances change.

Real-Time Risk and Fraud: AI at Lighting Speed

As payments occur within seconds, risk decisions need to keep pace. AI models now evaluate transactions, sessions, and devices as interconnected streams rather than isolated events. Graph models link signals across accounts and merchants, allowing large models to enrich decisions with context. Network-level tools from payment networks add an extra layer of security.

• Card networks leverage AI to score transactions at the time of authorization. For instance, Visa’s Advanced Authorization processes hundreds of data points in real time (Visa).
• Mastercard has introduced Decision Intelligence Pro, combining advanced AI with network insights to detect more fraud while minimizing false declines (Mastercard).
• Emerging account-to-account and instant payment rails are implementing fraud controls, with regulators advocating for stronger reimbursement and liability frameworks, especially in the UK regarding authorized push payment scams (PSR).

On the front lines, the surge in deepfake technology and AI voice cloning has led to a rise in social engineering attacks. Measures like liveness detection, multi-factor authentication, and consumer education remain absolutely crucial (FTC).

Payments and Data Rails Catch Up: The New Infrastructure

For AI to be effective, modern infrastructure is essential. Two major developments are underway:

• Real-time payments: Initiatives like FedNow in the U.S. and the RTP Network from The Clearing House enable instant settlements, which sets a demand for immediate risk decisions (Federal Reserve, The Clearing House).
• Rich, structured data: ISO 20022 is standardizing payment message fields, making it easier for AI to analyze transactions with added context (SWIFT).

Open banking and open finance are also contributing timely, permissioned data into risk engines. Europe is transitioning from PSD2 to a broader open finance framework, while the UK and Brazil continue enhancing secure data sharing for payments and credit (European Commission, Open Banking UK, Banco Central do Brasil).

From Copilots to Autonomous Money

We’re evolving from tools that suggest actions to services that can act autonomously on our behalf. The concept of autonomous money refers to software that can monitor account balances, optimize bills and savings, and make decisions within set boundaries. A practical first example is Wealthfront’s Self-Driving Money, which automatically directs cash to savings and investment goals based on user-defined rules (Wealthfront).

On the service side, AI assistants are already managing a substantial portion of customer inquiries. Klarna has reported that its AI assistant is now resolving the majority of chats, significantly reducing resolution times and support costs (Klarna).

Programmability Unlocks New Experiences

As accounts become more programmable, autonomous agents can operate more safely. In the crypto space, account abstraction and smart accounts have facilitated programmable spending limits and automated bill payments. In traditional finance, APIs, tokenized access, and precise permissions are enabling similar capabilities without altering the fundamental money structure (ERC-4337 overview, background).

The near-term approach is pragmatic: incorporating human oversight in automations, suggesting actions, documenting processes for compliance, and gradually shifting towards fully automated execution.

Compliance is Non-Negotiable: New Rules for AI in Finance

Regulators demand innovation to occur within protective boundaries. Three key areas require attention:

1. High-risk AI and transparency: The EU AI Act categorizes credit scoring and specific risk models as high risk, imposing mandates for risk management, data quality, and documentation (European Parliament).
2. Providing rationale: In the U.S., lenders are legally obligated to offer specific reasons for adverse actions even when complex models are employed, as per ECOA and Regulation B guidance (CFPB).
3. Model and third-party risk: Supervisors expect comprehensive model risk management and vendor oversight, building on standards like OCC 2011-12 and the Federal Reserve’s SR 11-7, along with new third-party risk protocols (OCC, Federal Reserve).

Industry frameworks can help teams progress faster with reduced risk. NIST’s AI Risk Management Framework and Singapore’s FEAT principles provide practical steps for ensuring fairness, accountability, and transparency in AI usage (NIST, MAS).

Recent events underscore the importance of these measures. The 2024 bankruptcy of BaaS provider Synapse left some customers with frozen funds, highlighting how third-party risk can ripple through the entire stack (TechCrunch). Regulators are also intensifying scrutiny of sponsor banks in response to notable incidents and data breaches (Reuters).

What Good Looks Like: A Reference Architecture

• Data infrastructure: consented bank and payroll data alongside enhanced transactions, device telemetry, and model-ready features.
• Real-time decision-making: minimal latency models paired with a streaming feature store to enforce strict guardrails.
• Human oversight: established escalation paths and reversible actions for high-risk or high-value scenarios.
• Visibility: dashboards for monitoring fraud rates, approval rates, biases, and customer impact.
• Governance: model cards, traceable features and prompts, challenger models, and documented reasons for adverse actions.
• Security and identity protection: phishing-resistant multi-factor authentication, verified payee protocols where applicable, and liveness detection compliant with NIST 800-63 guidance (NIST).

Limits and Risks to Keep in View

• Bias and data gaps: While cash-flow data expands coverage, it may also mirror income volatility that correlates with protected characteristics. It’s vital to test and monitor for discriminatory impacts.
• Model drift and feedback loops: As user behavior shifts, approve/decline feedback might skew training data. Implement champion-challenger setups and backtesting.
• Adversaries will adapt: Generative AI increases the potential for fraudulent activities. Expect to see more realistic phishing attempts, mule networks, and synthetic identities. A layered defense approach is necessary.
• Automation bias: Users might develop excessive trust in AI. It’s essential to have human input for significant decisions and provide clear, specific explanations when needed.
• Privacy and consent: Keep data usage minimal, respect revocation rights, and ensure full transparency of data lineage.

Getting Started: A Pragmatic Roadmap

1. Identify a high-impact use case: dynamic credit limits, risk assessment for instant payouts, first-party fraud reduction, or proactive bill optimization.
2. Establish a consent-centric data layer: gather high-quality bank data via open banking, enriched and labeled for features.
3. Integrate rules with models: define policies as rules while allowing models to optimize the gray areas effectively.
4. Measure key metrics: track approval rates, loss rates, false declines, manual review workload, and overall customer satisfaction.
5. Start with controlled pilots: employ shadow modes, A/B testing, and have a robust rollback plan in place, documenting everything for auditors from the beginning.

Bottom Line

AI is fundamentally reshaping finance from the inside out. Embedded lending is becoming safer and more inclusive through the use of cash-flow data. Risk decisions are finally aligning with the rapid pace of payments, and the idea of autonomous money is transitioning from concept to everyday practice. Success will come to those who combine strong engineering with effective governance, designing these systems with people as their top priority.

FAQs

What is embedded lending?

Embedded lending refers to credit that is provided at the point of need within a product or platform, such as an advance for a seller in a marketplace or a checkout offer. AI enhances it by utilizing real-time, consented data to determine offer sizes and assess risk.

How does cash-flow underwriting differ from traditional credit scoring?

Unlike traditional credit scoring, which often relies heavily on credit bureau histories, cash-flow underwriting examines bank transactions, income stability, and spending patterns to estimate repayment capabilities. This method can broaden access for thin-file consumers when applied responsibly.

Are instant payments safe?

Instant payment systems like FedNow and RTP are designed with robust controls, but the speed does elevate the risk of fraud. Key measures for safe adoption include real-time AI scoring, verified payee protocols, and user education.

What does autonomous money mean in practical terms?

Picture software that ensures bills are paid on time, moves excess cash to high-yield accounts, and allocates funds for goals without requiring constant user input. Users establish rules, while the system executes tasks with oversight and clear logs.

What regulations apply to AI in lending?

In the EU, the AI Act classifies credit scoring models as high risk, imposing strict requirements for compliance. In the U.S., lenders must offer specific reasons for adverse actions and adhere to risk management guidelines set by agencies like the OCC and the Federal Reserve.

Sources

1. NIST AI Risk Management Framework
2. EU AI Act overview – European Parliament
3. CFPB guidance on adverse action and complex algorithms
4. OCC 2011-12: Model Risk Management and Federal Reserve SR 11-7
5. FedNow Service and The Clearing House RTP Network
6. SWIFT ISO 20022 resources
7. Petal CashScore and Experian Lift
8. FICO UltraFICO
9. Visa Advanced Authorization
10. Mastercard Decision Intelligence Pro
11. UK Payment Systems Regulator – APP scams
12. FTC on AI voice cloning scams
13. Open Banking UK, EU PSD2 and open finance, Brazil Open Finance
14. Wealthfront Self-Driving Money
15. Klarna AI assistant results
16. Synapse bankruptcy and BaaS risk
17. Reuters on sponsor bank oversight
18. NIST 800-63 Digital Identity Guidelines