Personal Finance App Development

We build finance tracking apps that solve bank integration, transaction categorization, and multi-currency challenges. Clients expect automatic expense categorization, but banks provide data in inconsistent formats and even a single error in amount storage can cause reporting discrepancies. Our team of 8 engineers, each with 5+ years in financial mobile apps, has delivered 15+ projects in 5 years, including solutions with Open Banking, ML categorization, and sync via iCloud/Google Drive. We guarantee your app will pass App Store and Google Play review on the first attempt.

Why bank integration is the toughest challenge — mobile app development

We use three approaches:

• Open Banking / PSD2 (Europe). Standardized APIs: Nordigen (GoCardless), Salt Edge, TrueLayer. Authorization via OAuth2 with redirect back through Deep Link (ASWebAuthenticationSession on iOS, Custom Tabs on Android). Transactions arrive in JSON, but each bank interprets fields differently: amount can be string or number, pending transactions get duplicated. We write adapters per bank.

• Statement import (CSV/OFX/MT940). If no bank API, we parse statements. MT940 format varies per bank. For OFX we use ready libraries; for CSV, a configurable parser with column mapping. This approach saves up to 30% of budget compared to Open Banking.

• Manual entry. The foundation of any app. Quick entry via lock screen widget (iOS 16+ WidgetKit with intent) or Dynamic Island on iPhone 14 Pro. The user doesn't need to open the app to record an expense.

How we do transaction categorization

On-device ML is our choice. CoreML on iOS, TensorFlow Lite on Android. We train a custom model on labeled transactions: input creditorName and transactionAmount, output category. CoreML model runs 3x faster than cloud alternatives and works offline. If confidence is below 0.7, a rules-engine suggests the user confirm the category. This way we retrain the model without collecting data on the server.

Multi-currency: the golden rule

We store amounts in minor units (integers) to avoid floating-point precision issues when accumulating transactions. Exchange rates are cached from ExchangeRates API or Fixer.io, updated once a day. For reports, we convert using the rate on the transaction date, not the current rate.

Security: what's under the hood

Transactions are encrypted in the database via SQLCipher (React Native / Flutter) or NSFileProtection.completeUnlessOpen (iOS). Biometric lock via LocalAuthentication / BiometricPrompt. Bank API tokens are stored in Keychain (iOS) / Android Keystore. No UserDefaults or SharedPreferences. The Open Banking standard is described in the PSD2 Directive.

What's included in the work

• Requirements audit and stack choice (iOS/Android/Flutter/React Native).
• Accounting model design (double-entry or simplified).
• Development and testing on bank sandbox accounts.
• Push notification integration (APNs/FCM) and deep linking (Universal Links/App Links).
• In-app purchases (StoreKit 2 / Billing 6) for premium features.
• Publication to App Store Connect and Google Play Console, including TestFlight setup.
• Documentation and training for your team.

How we conduct bank integration: step by step

1. Analyze bank API documentation.
2. Develop adapter in Swift or Kotlin.
3. Test on sandbox accounts.
4. Validate on production environment.
5. Monitor and support.

Process

We start with an audit: which banks, target countries, need for multi-currency, monetization model. Design the data schema, develop, test on real bank accounts in sandbox mode, then on production. After publication — support and refinements.

Cost and timeline

Cost is calculated individually after requirements analysis. Approximate timelines: basic version — 5–8 weeks, full feature set — 16–24 weeks. Leave a request on our website to discuss your project. We'll get back to you within a day and offer the optimal solution.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

• Development and approval of a tracking plan with product and marketing teams.
• SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
• Setup of funnels, cohorts, dashboards, and alerts.
• Automation of symbolication and .dSYM upload via Fastlane.
• Documentation of events and parameters.
• Team training on the analytics platform.
• Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.