Getting matched-offer, APR and repayment data out of ALO Kash

Every ALO Kash loan journey — matching, side-by-side comparison, signing, repayment — happens inside the phone app, against a backend that stores the matched offers, the chosen lender, the application state and the repayment schedule per user. That is what makes it worth integrating. A lender, a comparison site, or an accounting tool wanting those numbers does not need the borrower to screenshot anything; the structured data already moves over the app's own calls, and that traffic is what we map.

The app, operated out of Bangladesh (the support address hello@synapsetech.cloud in its listing points to a Dhaka operator, Synapse Tech), connects borrowers to licensed financial institutions. Loans run from Tk 1,000 to Tk 60,000 over 91 to 360 days, with a maximum APR the app states as 14.6 percent — figures taken from its own Play Store description, not an external register.

What ALO Kash stores, and where it lives in the app

The surfaces below come from the app's described flow — sign up and verify, explore offers, apply and get funded — rather than a generic loan-app checklist. Each row is something a borrower sees on screen and the backend therefore holds.

Reaching the data: the routes that fit ALO Kash

Authorized interface mapping

We capture the app's mobile traffic under your authorization, recover the auth chain — an OTP-backed bearer token plus its refresh — and rebuild the calls as a clean, documented client. Reachable this way: the matched offers with their full terms, application state, repayment schedules and KYC status. Effort is moderate. Durability holds between app releases and is re-checked when the app updates. We arrange the consenting test account and the capture environment with you during onboarding.

Consented live session

A consenting account drives the real flow, so the mapping stays honest against live offers and an actual repayment schedule. This is what keeps the mapped client from drifting, and it covers anything that only appears once a borrower is verified.

In-app document capture

Signed agreements and repayment schedules also surface as documents inside the app. Where you need the legal artifact rather than the structured fields, we pull those directly. It is the lightest path and pairs with the structured reads.

Most of the value here moves over the app's own calls, so that mapped-traffic route is what we would actually build ALO Kash on. A consenting account runs next to it — less as a backup than as a live check on whether the offer data matches what a real borrower sees — and we reach for document capture only when the signed agreement itself is the thing you need.

A look at the offer-fetch call

Illustrative shape, reconstructed from how the flow behaves. Field names are confirmed during the build against a consenting account, not lifted from any published spec.

POST /v1/session/verify-otp        # OTP + 2FA, issues a short-lived bearer
  { "msisdn": "+8801XXXXXXXXX", "otp": "######" }
  -> { "token": "<jwt>", "expiresIn": 900 }

GET  /v1/offers?profileId=…        # the matched set, re-ranked per profile
  Authorization: Bearer <jwt>
  -> { "offers": [
         { "lender": "…", "apr": 14.6, "dailyRate": 0.04,
           "amount": 40000, "tenureDays": 180,
           "totalRepayment": 42564, "expiresAt": "…" }
       ] }

# On 401 the client refreshes the bearer once, then retries.
# The normalizer reconciles totalRepayment against the app's own formula:
#   interest = principal * annualRate * days / 365
# and snapshots each offer with expiresAt, since the set is not stable.

What lands at the end of the build

• An OpenAPI/Swagger spec for the surfaces we map — the OTP/token session, the offers read, application and repayment reads.
• A protocol and auth-flow report: how the OTP-backed bearer is issued and refreshed, the headers, and the exact request shape for the offer set.
• Runnable source for the key reads in Python or Node.js — sign in to a consenting account, pull the offer set, normalize APR, daily rate, tenure and total, and fetch the repayment schedule.
• A normalizer that reconciles each offer's total against the app's stated interest formula so your numbers match the app's.
• Automated tests against recorded fixtures, so a field-name change fails loudly instead of returning quiet garbage.
• Interface documentation plus data-retention and consent-logging guidance fitted to the Bangladesh rules below.

Things we plan around in ALO Kash

The offer set is not a fixed list. The matching engine re-ranks it per profile and individual offers expire, so we design the pull to capture each offer with its lender id, APR, schedule and expiry at fetch time and snapshot it — treating it as a stable table would hand you terms a borrower can no longer take.

The repayment math is the app's own. It publishes a maximum APR of 14.6 percent, a maximum daily rate of 0.04 percent and a worked example, so we encode that formula in the normalizer and reconcile our computed total against the figure the app shows, getting the rounding to agree rather than guessing.

Offers only appear after identity verification, so we shape the session so the consenting account clears KYC once and the integration then runs against that verified state. And because these apps ship updates often, when field names move we re-capture the traffic and diff it against the stored schema before any stale value reaches your system. Access to the test account and capture environment is arranged with you at onboarding — part of the work, not a checklist you clear first.

Consent and the Bangladesh rule set

There is no consumer-permissioned open-banking regime in the market to connect through — Bangladesh does not run the kind of AIS consent scheme the UK or Brazil operate. The basis we work from is the account holder's own authorization. Two rule sets shape how that work is done. Bangladesh Bank's e-Loan framework, issued in May 2026, sets the expectations for fully digital lending: eKYC, OTP with two- or multi-factor authentication, credit-history checks across banks and mobile financial services, and a requirement that customer and loan data sit in data centres physically inside Bangladesh. The draft Personal Data Protection Act 2023 — given cabinet in-principle approval in late 2023, but not yet enacted — points to where consent and cross-border transfer rules are heading. We treat both as operating posture, not as a hurdle for you: access is authorized and logged, the dataset is minimized to what the integration needs, extraction and storage stay in-country where the rules call for it, and an NDA covers the engagement when you want one.

Keeping offers fresh

Loan offers in ALO Kash are time-bound. The matching engine re-ranks them and individual offers carry an expiry, so an integration that reads the list once and caches it will hand you terms that have lapsed. We poll on a cadence you set, snapshot each offer with its expiry, and mark the ones that age out — what your system reads is an offer a borrower could actually accept right now, not a stale row.

How the build is priced

A working ALO Kash integration lands as runnable source plus an OpenAPI spec and tests, and the source-code route starts at 300 USD that you pay only after delivery, once you have checked it does what you need. The other way is to skip owning the code: call our hosted endpoints for ALO Kash and pay per call, nothing upfront. Either path runs on a 1–2 week cycle. Tell us the app and what you want out of its data on our contact page and we will scope it back to you.

Screens from the app

Marketing screenshots from the listing, useful for confirming the surfaces named above. Select to enlarge.

How this mapping was put together

Checked in June 2026: the app's own Play Store description for its loan parameters and data flow, plus primary reporting and reference material on the Bangladeshi rules that govern digital lending and personal data. The store metadata supplied lists the package id com.fwampy.zrudox; the loan figures cited above are the app's own stated terms, not independently verified against a register.

• Bangladesh Sangbad Sangstha — Bangladesh Bank's comprehensive e-Loan guidelines (May 2026)
• The Business Standard — how mobile wallets drive the rise of digital nano loans
• DataGuidance — an overview of Bangladesh's Data Protection Act, 2023

OpenBanking Studio integration desk · mapping notes, 18 June 2026.

Other Bangladesh lending apps in the same data picture

A unified integration usually spans more than one of these, since borrowers and lenders rarely sit on a single platform. Named for context, not ranked.

• bKash Loan (with City Bank) — in-app instant loans of around Tk 50,000, holding disbursement and repayment records tied to the bKash wallet.
• Nagad — a mobile financial service with wallet balances, transfers and a growing micro-credit ledger per user.
• eRin (TSLC Global) — salaried-worker personal loans of roughly Tk 10,000 to 50,000 over 3 to 6 months, storing application and repayment data.
• BRAC Bank Shubidha — end-to-end digital unsecured lending at larger ceilings, holding full loan-account and schedule data.
• Dhaka Bank e-Rin — a digital nano-loan service inside the bank's app with per-user loan and repayment records.
• Sonali Bank Digital Nano Loan — state-bank nano loans with eligibility and repayment ledgers per account.
• GotoCash — a short-term personal-loan platform holding application status and repayment schedules.
• City Alo (City Bank) — a personal-loan product with offer, EMI and account data behind login.

Questions integrators ask about ALO Kash

Which parts of ALO Kash actually hold structured data worth pulling?

The matched-offer set is the richest surface — each offer carries a lender, an APR, a daily rate, an amount, a tenure in days and a total repayment figure. Around it sit the application records (which offer was chosen and its state), the repayment schedule once a loan is live, and the identity-verification status from signup. Those are the surfaces we map first.

Is there an open-banking consent scheme in Bangladesh we would connect through?

Bangladesh has no live consumer-permissioned open-banking data scheme today, so the dependable basis is the account holder's own authorization. We run the integration under that consent, in line with the data-handling expectations in Bangladesh Bank's digital-lending framework and the consent principles in the draft Personal Data Protection Act 2023, which has cabinet in-principle approval but is not yet enacted.

ALO Kash re-ranks offers per profile — how do you keep the data accurate?

We snapshot each offer with its lender, terms and an expiry timestamp at fetch time rather than treating the list as fixed, and we poll on a cadence so expired offers drop out. When an app update shifts field names, we re-capture the traffic and diff it against the stored schema before anything stale reaches you.

Can you reproduce the repayment total ALO Kash shows a borrower?

Yes. The app states its own method — interest equals principal times the annual rate times days divided by 365, with a worked example of Tk 40,000 over 180 days at 13 percent giving Tk 2,564 in interest. We encode that formula in the normalizer and reconcile our computed total against the figure the app displays so rounding lines up.