Float Credit for Couples

Overview

Float Today was a consumer fintech startup focused on helping couples, families, and trusted connections understand credit information together. The product paired credit-score monitoring with a privacy-controlled sharing model — letting users share a lightweight emoji signal, a credit-score range, or their full score with a partner — so credit conversations could happen without all-or-nothing exposure of financial data.

Through GoatBytes.IO, I helped build the Android application — credit-score insights, controlled sharing, financial tips, and a loan and credit-card marketplace — on a Kotlin / MVVM stack with credit-data API integration, Google Tink for sensitive-data handling, and Lottie for onboarding polish. The page is preserved as a historical case study; the original product appears to no longer be publicly available.

Why credit conversations are hard

Credit decisions sit underneath some of the most consequential moments in adult life — buying a home, financing a car, combining finances, paying down debt — but the data behind them is private, confusing, and emotionally charged. Many couples avoid the conversation entirely until a lender forces it.

Float approached that gap as a product question rather than a knowledge question. Instead of pushing users toward all-or-nothing disclosure, the app made sharing a graduated choice — a lightweight signal, a range, or a full score — and built tips, monitoring, and a marketplace around the resulting conversation.

• 90%of U.S. lenders use FICO scores in lending decisionsFICO
• 3major nationwide consumer reporting agenciesCFPB
• 1 in 3couples report finances as a top relationship stressorAPA Stress in America
• ~26%of U.S. consumers have a thin or no credit fileCFPB, Data Point: Credit Invisibles

What sets it apart

My role

As Android Developer through GoatBytes.IO, I helped build Float Today’s Android application — a fintech product where consumer credit data, privacy controls, and sensitive sharing all had to feel approachable. The work covered credit-data API integration, MVVM architecture, secure local data handling with Google Tink, motion and onboarding with Lottie, and feature delivery for credit insights, tips, and the marketplace surfaces.

• Built Android features for Float Today’s consumer fintech application — credit monitoring, sharing controls, tips, and marketplace
• Integrated credit-score and financial-data workflows through third-party credit-data APIs
• Implemented MVVM architecture with Android Architecture Components for testable, layered separation
• Wired Retrofit and RxJava for authenticated credit-data flows
• Used Google Tink and AndroidKeyStore primitives for secure local handling of sensitive financial data
• Implemented privacy-controlled sharing UX — emoji, score range, and full-score levels
• Brought Lottie-driven onboarding and motion into the product experience
• Collaborated within a startup delivery model with product, design, backend, analytics, and executive stakeholders

Capabilities

• Credit-score monitoring
• Privacy-controlled sharing
• Trusted-contact connections
• Credit tips & education
• Score-change insights
• Loan & credit-card marketplace
• Couples & family workflows
• Financial wellness content
• Score history
• Onboarding & motion
• Score-update notifications
• Acquisition & funnel analytics

Distributed across

Built on

Languages

Kotlin

Architecture

MVVMAndroid Architecture ComponentsLayered domain/data/presentation

Networking

RetrofitRxJavaOkHttp

Security

Google TinkAndroidKeyStoreCertificate-aware networking

UI & motion

Android JetpackMaterialLottie

Domain

Credit-score data integrationPrivacy-controlled sharingCredit tips & educationLoan & credit-card marketplace

Platforms

Android

Further reading

Public sources framing Float Today’s product positioning, the engineering engagement, and the consumer-credit context the app sat inside.

• Float — Mobile App Case StudyUptech StudioProduct-side write-up of Float, including VantageScore tracking, three-level sharing, and analytics tooling for the startup.
• Building the Future of Fintech with FloatGoatBytes.IO case studyConsultancy-side write-up of the Float engagement, covering Tink, AndroidKeyStore, and the analytics strategy.
• Safeguards RuleU.S. Federal Trade CommissionFederal framework for safeguarding customer information at financial institutions — the regulatory backdrop for consumer-credit mobile products.
• Data Point: Credit InvisiblesConsumer Financial Protection Bureau, 2015CFPB research on the U.S. population without scoreable credit files — context for why approachable credit-monitoring products matter.
• Tink — Cryptographic libraryGoogleMulti-language, cross-platform cryptographic library used in Float’s Android app for secure handling of sensitive data.

“Make uncomfortable credit conversations comfortable — privacy-controlled mobile sharing for couples, families, and trusted contacts.”