Case Study · FINTECH · CREDIT

FLIN: A trusted credit journey for Indonesian consumers

Identity, credit-bureau data, privacy, and payments composed into one staged mobile experience.

The story

What FLIN needed, and what we built.

A useful credit product has to make complex financial information understandable without weakening identity, consent, privacy, or compliance. KUMO designed and built FLIN as a progressive trust journey for Indonesian consumers: verify the communication channel, capture identity from the device, confirm financial context, introduce credit information in a privacy-aware free state, then open a controlled route to a fuller paid report. Each stage carries its own validation, consent, and fallback rules, so bureau data becomes a navigable experience rather than a raw file.

What we delivered

Six areas of production work.

WhatsApp OTP authentication

A low-friction sign-in built on WhatsApp number verification: international phone validation, a six-digit OTP, ten-minute expiry, resend cooldown, rate limiting, and clean routing of new users to onboarding and returning users to their dashboard.

Live KTP and selfie identity verification

KYC identity verification designed around live device capture rather than gallery upload: guided KTP photography and a live selfie, blur and clarity prompts, retake and preview, and OCR-derived identity confirmation with sensitive source fields kept read-only.

Financial profile and debt confirmation

Structured collection of employment, income, expenses, and secured and unsecured obligations, formatted in Indonesian rupiah and validated before progression, so credit information is read against real financial context rather than a bare bureau response.

Free and paid credit-report states

A shared report structure with two data depths. The paid journey adds multi-page consent, an in-app payment SDK, background retrieval of additional CLIK data, recalculation on the richer FP/AFPI and CLIK data set, and a PDF copy delivered by email for the compliance flow.

Personalised recommendations and guided applications

Recommendation cards driven by calculator logic feed into dedicated Dana Talangan and debt-mediation journeys that separate education, data capture, documents, consent, review, and submission at every step.

Privacy masking and an explicit state machine

Sensitive values default to a masked presentation, and the product runs on explicit identity, report, data-source, recommendation, and application states, so every screen makes the user's position and next action clear.

Highlights

Project highlights.

WhatsApp OTP Low-friction authentication
Live KTP + selfie KYC identity verification
FP/AFPI + CLIK Credit-bureau integration
State machine Explicit user states
"Building our fintech app for Indonesia and Philippines markets. Senior team, fast moves, and quality you can trust on regulated workflows."

Rohit Bhageria

Founder, FLIN

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