Dovly AI API integration (credit score & dispute data)

1. Lender pre-qualification engine

A digital lender plugs the Dovly AI score sync into its pre-qualification API. When the user's TransUnion score crosses a threshold (e.g. crosses 660 FICO-equivalent), the platform fires a score_threshold_crossed event into the lender's offer engine, which produces a soft-pull personal-loan or credit-card invitation. Data involved: TransUnion score, score delta, utilization snapshot. This maps directly to OpenFinance "personal financial data sharing" patterns under CFPB §1033.

2. Credit-counseling SaaS sync

A non-profit credit-counseling platform mirrors each client's Dovly dispute lifecycle into its case-management system. Webhooks deliver dispute.submitted, dispute.under_review, and dispute.resolved events; counselors see Dovly's automated work and avoid duplicating letters. Data involved: dispute events, score history, tradeline status changes. This is an OpenData scenario: structured, consented, machine-readable export of credit-remediation activity.

3. Financial-wellness benefit (employer / neobank)

An employer benefits platform or neobank embeds a "credit health" widget. The widget pulls the Dovly score, top three credit-report negatives, and the next AI-recommended action, surfacing it inside the existing HRIS or banking app. Data involved: score, top-negative tradelines, recommended actions. Maps to OpenFinance "categorized financial data" — score, debt, and remediation status side by side.

4. Fraud & identity-incident pipeline

A fintech ingests Dovly Premium identity and dark-web alerts into its fraud SIEM. New identity.alert events open a Jira ticket and step up MFA in the partner banking app for 14 days. Data involved: alert source, exposed field, severity. This is a classic OpenData security use-case: lift alerts from one consumer app into an enterprise security plane.

5. Marketing & retention analytics (data warehouse)

A consumer finance marketing team lands Dovly score and goal-profile data in Snowflake or BigQuery via a nightly extract. They build cohorts ("users targeting a mortgage in 12 months"), trigger product educational sequences, and measure conversion from score lift to product activation. Data involved: profile, goals, score trend. The flow is consent-scoped and respects FCRA permissible-purpose boundaries.

Deliverables checklist

• OpenAPI 3.1 spec for the Dovly AI integration surface
• Protocol-analysis report: auth flow, token rotation, request signing
• Runnable source (Python & Node.js) for score sync, report export, dispute webhook
• Postman / Insomnia collection plus integration tests
• FCRA permissible-purpose checklist and consent-logging template
• Sample warehouse loader (Snowflake / BigQuery) for score history

Tech example 1: score & report sync (pseudocode)

// POST /api/v1/dovly/score-sync
POST /api/v1/dovly/score-sync
Content-Type: application/json
Authorization: Bearer <ACCESS_TOKEN>

{
  "user_ref": "usr_8af21",
  "bureau": "transunion",
  "include": ["score", "tradelines", "inquiries"],
  "since": "2026-04-01"
}

200 OK
{
  "score": { "value": 712, "delta": +18, "as_of": "2026-05-09" },
  "tradelines": [ /* normalized accounts */ ],
  "inquiries":  [ /* hard & soft pulls */ ],
  "next_cursor": null
}

Tech example 2: dispute lifecycle webhook

// Outbound webhook → your endpoint
POST https://your.app/hooks/dovly
X-Dovly-Signature: t=1715299200,v1=9f1c…

{
  "event": "dispute.resolved",
  "user_ref": "usr_8af21",
  "dispute_id": "dsp_4f7e2",
  "bureau": "transunion",
  "reason_code": "NOT_MINE",
  "outcome": "removed",
  "score_change": +24,
  "occurred_at": "2026-05-09T14:02:11Z"
}

Tech example 3: auth + error handling (Node.js)

// Refresh access token and retry on 401
async function call(path, body) {
  let token = await getToken();
  let res = await fetch(BASE + path, {
    method: 'POST',
    headers: { Authorization: 'Bearer ' + token,
               'Content-Type': 'application/json' },
    body: JSON.stringify(body)
  });
  if (res.status === 401) {
    token = await refreshToken();
    res = await fetch(BASE + path, { /* …retry… */ });
  }
  if (!res.ok) throw new DovlyError(res.status, await res.text());
  return res.json();
}

Regulatory framing

Credit data in the United States is regulated primarily by the Fair Credit Reporting Act (FCRA, Regulation V), with additional obligations under GLBA Safeguards for financial-data custodians. Any Dovly AI integration we build for you is scoped to FCRA permissible purposes (consumer-initiated, employment, credit transaction, account review) and ships with a documented consent log so each pull is auditable.

For consent-driven sharing flows we align with the CFPB Section 1033 Personal Financial Data Rights framework, finalized in October 2024 — the same "open banking" rule that recognizes a consumer's right to authorize a third party to fetch their financial data in a standardized, machine-readable form.

Operational controls

• TLS 1.2+ in transit, AES-256 at rest, key rotation on a 90-day cadence
• Per-user consent grants, revocations, and expiry stored as immutable events
• PII minimization: pseudonymous user_ref with a one-way mapping table held client-side
• Audit log for every API call (caller, scope, purpose code, response hash)
• NDA, DPA, and security questionnaire (SIG / CAIQ) responses on request

Credit Karma

Holds TransUnion and Equifax score and report data for tens of millions of US users, plus tax and offer signals. Users who also work with Credit Karma often need a unified score-history export across both platforms.

Kikoff

Credit builder line and rent-reporting flows. Its payment-history data complements Dovly's dispute lifecycle when consolidating score-lift attribution into one warehouse.

Self

Credit-builder loan and secured-card data with monthly bureau reporting. Often paired with Dovly disputes in coaching-style apps that want a 360° view of building plus repairing.

Chime Credit Builder

Secured-card payment data inside the broader Chime banking app — a natural neighbor when fintechs want banking, spending, and credit-score data in the same pipeline.

Credit Sesame

Credit score and identity-monitoring alerts. Sits in the same workflow as Dovly Premium's dark-web feed for fraud-team consolidation.

SavvyMoney

B2B credit-score widget embedded by credit unions and banks. Pairs well with Dovly export when partners want both consumer and channel views.

TomoCredit

Cash-flow underwriting card with score-impact data. Cross-referencing TomoCredit utilization with Dovly score trends supports better affordability models.

Arro

Gamified credit-builder card with bureau reporting. Surfaces complementary on-time payment data for a unified credit-health profile.

CoolCredit

AI credit-repair platform regularly compared with Dovly. Shared dispute-event schemas help teams compare resolution speed and outcome across vendors.

Lexington Law

Attorney-driven credit-repair service. Its case data, when consented, fills gaps where users move between DIY (Dovly) and concierge models.

About us

OpenFinance Lab is an independent technical studio focused on consumer-fintech API integration. Our engineers have shipped reverse-engineered and authorized data connectors for credit, banking, brokerage, and payments apps in North America, Europe, and Asia. For Dovly AI specifically, we combine mobile protocol analysis with a strict FCRA-aware delivery checklist so partners can ship credit-data features without re-architecting their security posture.

• Credit, banking, lending, and insurtech API specialists
• Enterprise gateway design, OAuth flows, and webhook security
• Python / Node.js / Go SDKs plus integration test harnesses
• End-to-end pipeline: protocol analysis → build → validation → compliance
• Source code delivery from $300 — runnable API source and docs, pay after delivery upon satisfaction
• Pay-per-call API billing — access our hosted endpoints, pay only per call, no upfront commitment

Contact

Tell us the target app (Build & Fix Credit - Dovly AI) and the specific data scope you need (score, report, disputes, alerts). We will reply with a fixed-price quote or a pay-per-call rate card.

Contact page

Engagement workflow

1. Scope call: confirm data scope (score, report, disputes, identity alerts) and target consumers.
2. Protocol analysis & API design (2–5 business days)
3. Build, signing, and internal validation (3–8 business days)
4. Docs, Postman collection, sample loaders (1–2 business days)
5. Typical first delivery: 5–15 business days; FCRA / §1033 hardening adds 1–2 weeks.

FAQ

What data can you extract from Dovly AI?

How long does a Dovly AI integration take?

Is the integration FCRA and Section 1033 compliant?

Can the Dovly score data feed downstream lenders or CRMs?