Getting structured account data out of the CFBank Mobile app

CFBank Mobile runs on the mFoundry mobile banking platform — the white-label stack FIS bought in 2013, per Bank Automation News — themed for CFBank, National Association, the OCC-chartered boutique bank behind it (FDIC certificate #28263, per FDIC BankFind). Behind the login sits the data most integrations actually want: per-account balances, a searchable transaction ledger, internal transfers, scheduled bill payments, and mobile check deposits. The work here is reaching that data on the account holder's behalf, in a form a system can poll and keep in sync.

The route I would lead with is a consent-backed integration directly against the app's own traffic, with an aggregator connection held in reserve where normalized multi-bank data matters more than app-specific fields. The rest of this page maps the surfaces, the routes, and what we hand over.

What account data CFBank actually holds

The listing is specific about what the app shows, which makes the mapping straightforward. Each row below ties a data domain to where it surfaces in the app and what an integrator does with it once it is queryable.

Authorized ways into CFBank's data

Three routes genuinely apply here, plus one that is worth naming only as a future possibility. For each I have noted what is reachable, the effort, how durable it is, and what we set up to run it. Access to a consenting account or sandbox is arranged with you during onboarding; it is part of the job, not something you hand us first.

1 — Consent-backed interface integration (the spine)

We map the app's session and the calls behind the screens — login through the mFoundry flow, the balance read, the transaction search — and wrap them as a clean client that runs under the account holder's authorization. Reachable: everything the app itself shows. Effort is moderate; the mFoundry stack has a recognizable shape across the institutions it powers, which shortens the mapping. Durability is good as long as we re-validate against CFBank's release each time the app updates. This is the route I would build the integration on.

2 — Consumer-permissioned aggregator

Plaid, MX, or Mastercard's Finicity can return balances and transactions under the customer's consent without us touching the app directly. MX in particular has deep coverage of mid-sized and regional banks, per the aggregator comparisons I read. We confirm CFBank's coverage on whichever network you prefer, then wrap it. This is the better choice when you need normalized data across many banks and can live without app-specific fields.

3 — Native export (fallback)

Where the online-banking side offers a statement or transaction download, we can take OFX/QFX/CSV as a low-frequency batch feed. Lower fidelity and slower than the live routes, but useful for backfill or a one-time historical load.

4 — Standardized data-rights access (not yet)

A standardized US bank-data rule could one day make this a documented endpoint. It is not there today; see the consent section for why we do not build against it.

How a transaction pull looks in practice

The app already lets a customer search transactions by date, amount, or check number, so the delta feed builds on filters that exist rather than inventing new ones. A cursor stores where the last run stopped; the next poll resumes from there.

# Delta pull against the CFBank Mobile session (mFoundry / FIS stack).
# Auth is the app's own login -> session token, replayed under the
# account holder's consent. This is interface integration, not a bypass.

session = cfbank.login(member_id, secret, device=enrolled_device)   # MFA handled in-flow

def pull_transactions(account_id, cursor):
    page = session.post("/txn/search", json={
        "accountId":  account_id,
        "fromDate":   cursor.posted_date,   # the app's own date filter
        "sortBy":     "postedDate",
        "pageToken":  cursor.token,         # resume where the last run stopped
    })
    for t in page["transactions"]:
        yield normalize(t)                  # -> posted, amount, check_no, memo, running_balance
    return page.get("nextPageToken")        # null when caught up

The normalized record that comes out the other side is small and stable, so downstream consumers do not have to know anything about CFBank's raw shape:

{
  "account_id":      "opaque-per-consent",
  "posted_at":       "2026-05-30",
  "amount":          -42.18,        // signed, stored in minor units internally
  "check_number":    "1043",        // present only on check debits
  "description":     "...",
  "running_balance": 5120.44,
  "source":          "cfbank.mobile",
  "cursor":          "..."          // stable per-item key for delta runs
}

What lands in your repo

The headline deliverable is code that runs, not a stack of paper. Concretely:

• Runnable clients in Python and Node.js: login and session handling on the mFoundry stack, balance reads, the transaction delta-pull, and status checks for transfers and bill pay.
• A polling sync runner that holds a cursor and asks only for what changed since the last run.
• A test suite with recorded fixtures. When CFBank ships an app update that moves a field, the suite flags the exact mapping that broke before bad data reaches you.
• The normalized transaction schema above, plus the mapping from CFBank's raw fields to it.

Secondary, and still included: an OpenAPI description of the endpoints we wrap, an auth-flow report covering the session, device-enrollment and MFA chain, written interface documentation, and data-retention guidance for what you keep and for how long.

Engineering specifics we plan around

A few things about CFBank shape the build. We account for these up front rather than discovering them late.

• It is a themed instance of a shared platform. Because CFBank Mobile is an mFoundry tenant, we map the session, device-enrollment and MFA behavior as that platform implements it, then re-validate against CFBank's specific tenant configuration on each release. Generic mFoundry knowledge gets us most of the way; the tenant details are where we confirm.
• The delta cursor has to expect late items. We key the cursor on posted date plus a stable per-item key, which matters because banks backfill and re-post transactions after the fact; a naive “read from the newest row” would quietly miss them.
• Check-deposit images are sensitive. Mobile deposit can carry check images. By default we capture only status and capture metadata, keep image bytes out of logs, and treat the image itself as in scope only when you ask for it.
• Personal and business are different apps. CFBank Business is a separate product with its own roles and entitlements, per the App Store listings. We scope per app, because the business product's permission model does not match the personal one.

Consent and where the rules sit

The dependable basis for this work is the account holder's own authorization. CFBank is a national bank supervised by the OCC and FDIC-insured (cert #28263, per FDIC BankFind), so we operate from consented access: consent is recorded, revocation is honored, and we minimize what we pull to what the integration needs. Where a project uses an aggregator, the same consumer-permissioned consent and revocation apply on that network. NDAs are signed where a client needs them, and access is logged.

For a national bank like CFBank, a standardized federal data-access rule would be the tidy long-term answer, and the CFPB's Personal Financial Data Rights work points that direction. It is not in force today — a federal court halted enforcement in late 2025 and the Bureau reopened the rule for reconsideration, per the Federal Register reconsideration notice. So we treat it as where things may go, not as the basis we build on. Today that basis is consent.

What the app surfaces, on screen

The store screenshots line up with the data domains above — the accounts list, transaction search, transfers, bill pay, and the locator. Tap any image to enlarge.

Other bank apps teams pair with CFBank

Integrators rarely connect one bank in isolation. These US bank apps come up alongside CFBank in the same regional and community-banking category; a unified integration usually has to read several of them through one normalized model.

• Huntington Mobile — large Ohio-based regional bank; balances, transactions, card controls, and deposits behind a single login.
• KeyBank Mobile — Cleveland-headquartered regional; account, transaction, transfer and bill-pay data.
• Fifth Third Mobile Banking — Cincinnati regional; accounts, transactions, payments, and mobile deposit.
• U.S. Bank Mobile — national footprint; balances, statements, transfers, and bill pay.
• PNC Mobile — broad consumer and business banking; account and cash-flow data.
• Chase Mobile — the largest US consumer bank app; deep account, card, and transaction history.
• Wright-Patt Credit Union — large Ohio credit union; member account, transaction, and transfer data.

Questions integrators ask about CFBank

How do you keep a CFBank feed current without re-downloading every transaction?

We store a cursor from the last successful run — the latest posted date plus a stable per-item key — and the next poll asks CFBank only for what landed after it. Banks backfill and re-post items, so the cursor is built to pick those up rather than only reading from the newest row down.

Can you read the personal and business CFBank apps the same way?

They are separate products. CFBank Business is its own app with its own roles and entitlements, so we scope and build per app rather than assume one client covers both. Most of the session and transaction logic carries over; the access model does not.

Do you connect through an aggregator like Plaid or MX, or straight at the app?

Both are on the table. An aggregator such as MX, which has strong regional-bank coverage, gives you normalized balances and transactions quickly when CFBank is in its network. Going at the app directly returns the full-fidelity fields the app itself shows — including the check-number filter — and does not depend on a third party's coverage.

What is the legal basis for pulling CFBank account data?

The account holder's own authorization. CFBank is an OCC-chartered national bank, and we work from consented access with consent records kept and revocation honored. The CFPB's Personal Financial Data Rights work might standardize bank access later, but it is not in force today, so we do not build against it.

What is a realistic timeline for a first working pull?

Plan on one to two weeks. For CFBank that means a working login through the mFoundry session, balance reads, and a cursor-based transaction pull, handed over with the test fixtures and a short interface write-up.

What I checked, and when

I read the Play Store and App Store listings for the stated feature set, confirmed the institution and its national charter on the FDIC's record, checked CF Bankshares' corporate profile for ownership, traced the app's package to the mFoundry platform (acquired by FIS), and reviewed the current status of US data-rights rulemaking. Checked June 2026. Primary sources:

• FDIC BankFind — CFBank, National Association (cert #28263)
• CF Bankshares Inc. — corporate profile
• Bank Automation News — FIS to buy mFoundry
• Federal Register — Personal Financial Data Rights reconsideration

OpenFinance Lab — interface mapping, June 2026.

A first CFBank pull — login, balances, and a working transaction delta-feed — typically lands in one to two weeks. Take it as runnable source you keep (from $300, invoiced only after delivery once you are satisfied), or call our hosted endpoints and pay per call with nothing up front. Tell us the app and what you need from its data, and we arrange the access and compliance side with you: Start a CFBank integration