Moving your records out of Mi Ahorro and into your own systems

Every figure Mi Ahorro shows you — the running income and expense log, the progress bar on each savings goal, the balance still owed on a loan — begins life as a record the app writes for one account holder. That dataset is the whole reason to integrate. People reach OpenBanking Studio because their money history is trapped inside one tracker and they want it in a spreadsheet, a dashboard, or another finance tool, in a shape a machine can read. Mi Ahorro markets itself as a way to organize money simply, per its Google Play listing under the package app.diegoramms.com.miahorro. The interface is Spanish; the records inside are plain financial entries. We turn those entries into a queryable feed you control.

What the app records, surface by surface

These rows follow how Mi Ahorro itself frames its features. Each is a real data domain an integrator would map, not a generic placeholder.

Seeing the surfaces

The store screenshots show the live layout we map against. Tap to enlarge.

Authorized routes to the records

Mi Ahorro is a self-entered tracker, which narrows the realistic ways in. Two routes do the work here, with a third that only matters if you want bank reality layered on top.

1 — Consent-based copy of the user's own dataset

The fastest clean copy is the records themselves. With the account holder's consent, we take a structured snapshot of what they entered — from the on-device store, a device backup, or an export the app produces — and normalize it. Low effort, exact fidelity, because we are reading the canonical record rather than scraping a rendering. If the app writes a backup file, we confirm its format and parse it directly. This is the route we lean on for a one-time migration or a periodic refresh you trigger by hand.

2 — Authorized client against the app's data layer

To make the feed repeatable without a person in the loop, we analyze how the app reads and (where it does) syncs its records, then build a client that pulls the same data under the user's authorization. More setup, and it needs a re-check when the app ships a new version, but it gives you a feed that refreshes on a schedule instead of a copy that goes stale. This is the route worth the extra week when Mi Ahorro is feeding a live dashboard.

3 — Bank reconciliation via a regulated consent (only if you need it)

Mi Ahorro's figures are what the user typed, which may drift from what their bank actually recorded. If the goal is to reconcile the two, a regulated Account Information Services consent in the user's own country can supply the bank side — that is a separate, adjacent data flow, not Mi Ahorro's data, and we only wire it in when reconciliation is the point.

For most briefs that land on this app, the honest recommendation is route 1 for the first delivery and route 2 once you know you want it to keep updating; route 3 is a conversation only if bank-versus-ledger accuracy is the actual requirement.

How the extraction looks in code

Illustrative only — the exact store layout and field names are confirmed during the build, never assumed. The pattern is the same whichever layer holds the canonical record: read entries, normalize, expand the recurrence rules, de-duplicate.

# Mi Ahorro records are read with the account holder's consent.
# Field names below are illustrative and confirmed against the install.

from miahorro_client import Vault          # the access layer we stand up for the app

vault = Vault.open(consent_token=USER_CONSENT)   # local store, backup file, or sync session

def normalize_entry(row):
    # the "log income and expenses" rows -> one unified shape
    return {
        "id":        row["id"],
        "direction": "income" if row["type"] == 1 else "expense",
        "amount":    abs(row["amount"]),
        "currency":  row.get("currency", "—"),   # confirmed per install
        "category":  row["category_name"],
        "booked_at": row["date"],                # ISO 8601 after normalization
        "note":      row.get("note"),
    }

ledger = [normalize_entry(r) for r in vault.entries()]
goals  = [
    {"goal": g["name"], "target": g["target"], "saved": g["saved"],
     "remaining": g["target"] - g["saved"]}
    for g in vault.goals()
]

# "automations" (salary, subscriptions, fixed payments) are rules, not rows.
# Expand each into projected entries, then drop any that already match a
# logged entry so a forecast salary is not counted twice.
projected = expand_recurrences(vault.automations(), through="2026-12-31")
cash_flow = dedupe(ledger + projected, key=("amount", "category", "booked_at"))

What lands in your hands

Every engagement ends with code you can run and read, scoped to the surfaces above:

• An OpenAPI/Swagger specification describing the normalized endpoints — ledger entries, goals, loans, recurrence rules.
• A protocol and auth-flow report: how the app's data layer is reached, the session or token chain involved, and where the records actually sit.
• Runnable source for the key reads — the entry extractor, the goal and debt mappers, the recurrence expander — in Python or Node.js.
• Automated tests over the normalizer, including the de-duplication of projected against logged entries.
• Interface documentation plus a short note on data retention and consent records, so the integration is auditable later.

The normalized shape

Two example records after normalization, so you can see what your systems would consume. The raw field names are reconciled to these during the build.

// one ledger entry
{ "id": "e_8841", "direction": "expense", "amount": 23.90,
  "currency": "—", "category": "Suscripciones",
  "booked_at": "2026-05-02", "note": "streaming" }

// one savings goal
{ "goal": "Fondo de emergencia", "target": 1500.00,
  "saved": 640.00, "remaining": 860.00, "pct": 42.7 }

The basis for touching the data

This is one person's record of their own money. The dependable footing is their authorization over data they created — they can hand over a copy of their own ledger, and they can ask us to build the tooling that reads it. Mi Ahorro is not a bank or a regulated account provider, so Open Banking and AIS rules do not govern the app's own records; that machinery only enters if route 3 reaches a real bank.

Where the account holder is in the EU, GDPR adds weight: the right of access (Article 15) and the right to data portability (Article 20) both cover information a person provided themselves, which is exactly a hand-typed income-and-expense log. The market for Mi Ahorro is not precisely disclosed, so we treat the user's consent as the constant and the regional data-protection law as the reinforcement. We work authorized, log what we access, keep the data footprint to the domains you actually need, and sign an NDA where the engagement calls for one.

Things we account for on this app

A manual-entry tracker has its own quirks. These are the ones we handle as part of the build, not hurdles we hand back to you.

• We determine where the canonical record really sits — an on-device database, an encrypted device backup, or a cloud sync — before writing the extractor, because the right approach differs and a wrong guess produces a partial copy.
• We model the recurring automations as rules separate from their expanded postings, then de-duplicate, so a projected salary never inflates the cash-flow total against an already-logged one.
• Savings-goal progress is a derived figure (saved against target); we decide with you whether to carry the stored value or recompute it from linked entries, so the integration stays correct when a user edits history.
• When the app updates its store between versions, we re-validate the mapping rather than assume the old shape — a maintenance pass we plan for up front.

Access to a consenting account or a sample backup is arranged with you during onboarding; the build runs against that, and nothing about it is a wall you have to clear before we begin.

Pricing and how it runs

A working Mi Ahorro integration — the extractor, the normalizer, and its documentation — ships in one to two weeks. You can take it as source you own outright: runnable code plus the interface docs, from $300, billed only after delivery once it runs against your data and you are satisfied. Or you can skip owning the code and call our hosted endpoints instead, paying per call with nothing up front. Tell us the app and what you want out of it, and we scope it from there. Start the conversation at /contact.html.

What was checked, and when

This mapping was built in June 2026 from the app's own store listing and the EU data-rights texts that back the consent route. The store page supplied the feature set and package identifier; the GDPR articles below define the access and portability rights referenced above.

• Mi Ahorro on Google Play (feature list, package ID)
• GDPR Article 20 — right to data portability
• ICO guidance — right to data portability

Mapped by the OpenBanking Studio integration desk · June 2026.

Similar apps in the same data space

Other personal-finance trackers hold comparable per-user records, and the same normalization approach spans them — useful when a single integration has to read more than one tool.

• Monefy — fast manual logging with Google Drive or Dropbox backup; records sit in the user's own synced file.
• Spendee — shared wallets and budgets, with bank-linked and manual entries side by side.
• Toshl Finance — categorized income and expense tracking with budgets and exportable summaries.
• Money Lover — spending manager with accounts, budgets, and recurring transactions.
• Bluecoins — local-first finance and budgeting with detailed reports stored on device.
• Wallet by BudgetBakers — budgets, accounts, and category analytics across manual and connected sources.
• Mobills — bills, cards, and goals tracking popular across Latin American markets.
• Fintonic — aggregation and alerts over linked accounts and spending.
• Mis Finanzas: Gastos en orden — a Spanish-language expense and budget tracker in the same niche.

Questions an integrator asks

Where does Mi Ahorro actually keep the income and expense log — on the phone or on a server?

That is not spelled out in the public listing, so we confirm it during the build rather than guess. A manual-entry app like this can hold everything in an on-device store, mirror it to a backup file, or sync it through a backend. We read whichever layer holds the canonical record, with the account holder's consent, and target the extractor there.

Can the recurring automations such as salary and subscriptions be pulled as rules, not just as posted entries?

Yes. The automations are recurrence definitions — an amount, a cadence, a next date — that the app expands into entries over time. We carry the rule itself and its expanded postings separately, then de-duplicate the projection against entries already logged so a forecast salary is not counted twice.

Which regulator covers extracting my own Mi Ahorro records?

Mi Ahorro is a personal-finance tracker rather than a bank or account provider, so this is not an Open Banking or AIS matter for the app's own data. The dependable basis is your own authorization over records you entered yourself. For a user in the EU, GDPR's right of access and right to data portability reinforce that claim to a machine-readable copy.

If I only need the savings goals and the debts, can the integration skip the rest of the ledger?

It can. We scope the extractor to the domains you name, so an integration limited to savings-goal progress and the loans-and-debts section ignores the full transaction log entirely. Narrowing scope also keeps the data footprint smaller, which we prefer on principle.