Personal Finance App Development Company

We integrate with Plaid, MX, Finicity (Mastercard Open Banking), and Yodlee to pull transaction history, balances, and account metadata across checking, savings, credit cards, loans, and investment accounts. OAuth-based connection flows mean banking credentials are never stored in your system. Institution coverage is scoped by market: Plaid for US consumer apps covering 12,000+ institutions, open banking APIs under PSD2 for UK and European products.

Transaction enrichment cleans raw merchant strings into recognisable business names, assigns location metadata, and detects recurring transactions. The enrichment layer is built to your product's category taxonomy, not the aggregation provider's defaults, so budget tracking reflects how your users think about money rather than how merchants register their accounts.

Built for fintech startups building a money management product, neobanks adding financial wellness to their core account, and financial wellbeing platforms that need a reliable, low-maintenance bank connection layer.

Budget configuration covers fixed monthly limits per category, rollover rules for underspent budgets, and shared budgets for joint account holders. Spend limits trigger alerts at configurable thresholds, not just at 100%, so users get a warning while there's still room to adjust. Alert logic accounts for transaction timing: pending transactions are flagged separately from cleared ones so the numbers don't mislead.

Recurring transaction detection identifies subscriptions, direct debits, and regular bills, and separates them from discretionary spending in the budget view. Users see what's committed versus what's controllable, which is the distinction that makes budgeting advice actionable.

Built for consumer finance apps where budget accuracy is a retention driver, employer financial wellness platforms where users need to see their discretionary spending relative to their pay cycle, and neobanks adding a spend management layer to their current account product.

Spending analytics surfaces patterns across rolling time windows rather than calendar months, so a user who gets paid on the 15th sees their spending cycle correctly rather than split across two calendar periods. Category trend charts show month-over-month movement. Merchant-level drill-down shows every transaction at a specific retailer or service across the full account history.

Merchant intelligence maps raw transaction strings to clean business names using a combination of a curated merchant database and machine learning trained on your user population's transaction patterns. Location data from merchant records enables geo-based spending maps for users who want to see where they spend geographically.

Built for personal finance apps competing on insight depth, financial wellness platforms that report spending trends to HR dashboards, and any product where the quality of the analytics is a primary reason users choose it over a free alternative.

Savings goals connect to real account balances rather than manual deposit tracking. Users set a target amount and a target date; the app calculates the weekly or monthly deposit required and shows whether current saving behaviour is on track. Round-up rules move micro-amounts to a goal pot on every qualifying transaction without requiring manual transfers.

Milestone alerts fire at 25%, 50%, 75%, and 100% of goal completion, creating reinforcement moments. Goal categories cover common targets (emergency fund, holiday, home deposit, car) with custom naming for any user-defined goal. Multiple simultaneous goals are prioritised by target date with a recommended split of available saving capacity across goals.

Built for fintech startups whose retention depends on users achieving visible financial progress, neobanks adding savings tools to differentiate from current account competitors, and financial wellness apps where demonstrating behavioural change is the core product value.

Bill detection identifies recurring outgoings from transaction history: subscriptions, direct debits, loan payments, and utility bills. Each detected bill is presented to the user for confirmation, with estimated next payment date and amount derived from the transaction pattern. Manual bill entry covers payees that haven't yet appeared in the transaction history.

Payment alerts fire at configurable lead times: three days before a bill is due, on the day, and when the payment clears. Insufficient funds alerts check projected balance against upcoming bills and warn when the account is likely to go into the red before the payment date. Subscription audit tools highlight recurring charges the user may have forgotten, often revealing $50 to $150 per month in payments they'd like to cancel.

Built for money management apps where bill visibility reduces the anxiety that brings users back weekly, consumer credit apps where overdraft risk detection is a key trust feature, and financial wellness platforms where reducing payment failures is a measurable outcome for the employer client.

AI insight generation scans transaction history for patterns a user wouldn't find by browsing their feed: a price increase on a subscription that crept up $3 per month over a year, a spending category that's consistently 20% higher in the final week of the month, a vendor charge that duplicated without an obvious reason. These observations are surfaced as notifications or as a weekly insight digest rather than embedded in the transaction list where they'd be missed.

Natural language query lets users ask questions about their finances in plain English: "how much did I spend on food last month", "which subscriptions am I paying for that I haven't used in 90 days", "am I spending more this year than last year". The query layer runs against the user's own enriched transaction data rather than a general knowledge model, so the answers are specific to their financial situation.

Built for personal finance apps where AI features are a product differentiator rather than a marketing claim, financial wellness platforms that need to demonstrate measurable changes in user financial behaviour, and any consumer finance product where engagement depth beyond basic account viewing is the retention strategy.