End-to-end digital lending platform with ML credit scoring, instant KYC/AML verification across 38 US states, e-signature workflows, and same-day loan disbursement — processing $120M in loans with a 5-minute average approval time.
SwiftCredit's manual credit review process took 3–5 business days. Their loan officers reviewed applications in spreadsheets, called bureaus for data, and approved loans via email chains. Competitors were approving in minutes. They needed a full digital lending platform — not a workflow tool, a complete underwriting engine with ML credit scoring built in.
We built LendFast in 14 weeks: an end-to-end platform covering application intake, ML credit scoring, KYC/AML screening across 38 states, e-signature, and bank disbursement — achieving $120M in cumulative loans by Month 8 with a 0.4% default rate well below the 1.8% industry average.
Loan officers manually pulled Experian/Equifax reports, calculated DTI ratios in Excel, and made approval decisions via email. The process was slow, inconsistent, and created compliance risk through undocumented decision rationale.
SwiftCredit operated in 38 US states, each with different consumer lending regulations, usury caps, and identity verification requirements. A single compliance layer needed to handle all 38 state rule sets dynamically at application time.
30% of SwiftCredit's target market were thin-file borrowers — recent graduates, new-to-credit immigrants, and gig workers — with insufficient bureau data for traditional scoring. Standard FICO models rejected them automatically, leaving revenue on the table.
Origination required connecting to credit bureaus, KYC vendors, bank disbursement partners, e-signature platforms, and loan servicing systems — 12 integrations in total, each with different API styles, authentication methods, and SLAs.
Synthetic identity fraud — where fraudsters combine real and fake information to create fictitious identities — is invisible to standard bureau checks. SwiftCredit had experienced significant losses from this vector in manual operations.
Every declined application under FCRA requires a specific adverse action notice citing the exact reasons for denial in consumer-friendly language. Manual generation was error-prone and exposed the company to regulatory liability on every declined file.
Trained an XGBoost credit scoring model on 5 years of bureau data plus behavioral signals — payment patterns, account age, inquiry velocity, and utilization trends. The model outperforms traditional FICO scoring on default prediction by 34%, delivering 92% auto-approval accuracy.
Integrated Jumio biometric identity verification with automated OFAC/BSA sanctions screening. A state-aware compliance rule engine dynamically applies the correct regulatory requirements for each applicant's state of residence at submission time — no manual routing required.
For thin-file applicants, we integrated Plaid to access bank transaction history — analysing income stability, expense patterns, and cash flow consistency as alternative creditworthiness signals. This opened approvals for 28% of applicants who would have been automatically declined by bureau-only scoring.
Built a single API abstraction layer wrapping all 12 integration partners. Each partner has a standardised adapter handling authentication, retry logic, rate limiting, and circuit breaking. New partners can be onboarded by implementing a single interface — not by modifying core lending logic.
Deployed a fraud detection ensemble combining device fingerprinting, application velocity rules, cross-reference checks against known fraud databases, and an ML anomaly detection model. Synthetic identity fraud is detected via inconsistency scoring across identity attributes — catching 91% of fraud cases in testing.
Built an automated adverse action letter generator that maps model decision factors to FCRA-compliant consumer language, selects the correct notice format per state, and sends via email with documented delivery confirmation. Regulatory risk from manual adverse action errors eliminated entirely.
Live in production within the 14-week deadline. First 1,000 applications processed in week one, with a 4.8-minute average approval time. The ML model scored applications in 340ms on average, and KYC cleared 97% of applicants automatically.
$12M disbursed by Month 3, with the 92% auto-approval rate eliminating the need for loan officer review on the vast majority of applications. Thin-file approval rate reached 28% of applicants who would previously have been auto-declined.
$120M cumulative disbursement at a 0.4% default rate — less than one-quarter of the 1.8% industry average. SwiftCredit raised a Series A on the strength of LendFast's metrics, with the ML credit model cited as a core competitive differentiator by investors.
"LendFast went from requirements to live lending in 14 weeks. The ML credit model beats our old manual process on both speed and default rates — we went from 3-day approvals to 5 minutes, and our default rate is less than a quarter of industry average. The KYC automation handles compliance across 38 states without us touching it. We raised our Series A on these numbers."
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