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Super app fraud can wipe out years of growth in one bad quarter. In one Solashi build, we cut chargeback losses from $670,000 to $90,000. That is a 86% drop. The next year, the same app is on track for zero loss.
The context matters. The app runs at 4.6 million registered users. Also, it serves 270,000 daily unique users. At that scale, one weak layer bleeds millions each quarter.
This guide walks through the blueprint. First, why super app fraud now threatens ecosystem survival. Second, the six live layers Solashi ships. Third, the ML score that catches what rules miss. Finally, the compliance base that makes the whole stack audit-ready.
Why Super App Fraud Now Threatens Ecosystem Survival
Overall, three signals put super app fraud at the top of every CTO’s list.
First, chargeback losses keep rising. In fact, global chargeback losses will grow from $33.79 billion in 2025 to $41.69 billion in 2028. Also, US merchants now lose $4.61 for every $1 in chargebacks.
Second, fintech breach costs run above the average. Notably, finance firms hit $5.90 million per breach on average. Moreover, 41.8% of fintech breaches come through third parties.
Third, most teams catch super app fraud too late. Specifically, only one-third of financial firms detect fraud at onboarding. As a result, the rest catch it deep in the transaction flow when losses are already large.
So the risk math is clear. Weak super app fraud defense hits three lines at once. It hits P&L through chargebacks. Also, it hits trust through breach headlines. Finally, it hits growth through slow onboarding.
The Six-Layer Super App Fraud Blueprint
Basically, Solashi’s super app fraud blueprint runs on six layers. Each layer catches a different attack path. Together, they cut fraud loss over 80%.
Every layer sits on a shared base called the Fraud Profile. In short, each user gets a live profile. It logs any active restrictions. It also tracks fraud history. So when a layer flags a case, the system disables the risky feature at user level. The other features stay live.
Layer 1: Real-Time Carrier and Country Screening
To start, most cash-out fraud starts from outside the target market. So Solashi pulls carrier and country data at each app session. Then the system checks it against a blacklist. If the user matches, the system logs them out at once.
The impact is direct. On average, this layer blocks thousands accounts per month. In one peak month, it blocked >14,000 accounts in a single push.
The layer also fixes false positives fast. For example, when a real user travels, the next login refreshes the carrier data. If the new region is clean, the block lifts on its own. Also, staff can lift blocks by hand for edge cases.
Layer 2: Enforced 3DS on Suspicious Top-Ups
Next, top-up fraud is where most cash-out attacks start. So Solashi tracks failed top-ups per device. If a user hits X failed top-ups in a row, the system flips 3DS on for the whole device.
The catch rate is strong. In detail, this layer blocks about 100,000 failed transactions each month. That is 4% of all top-up traffic. Also, the rule filters system errors first. It only fires on real fraud codes like “fraudulent” or “missing_card”.
Layer 3: Device Change ATO Defense
Then account take-over attacks often start when the victim gives up their password. So the attacker signs in on a new device. Solashi’s stack flags this at login.
When a new device joins, the system does two things. First, it turns on 3DS for top-ups on that device. Second, it kills auto top-up on the whole account. So even if the attacker has the password, they cannot drain the wallet in silence.
On average, this layer catches 75,000 device changes per month. Human review then clears real cases like phone upgrades.
Layer 4: Auto Top-Up Eligibility Gate
Also, auto top-up is the fastest cash-out path once an account is breached. So Solashi puts a hard gate on turning it on.
To enable auto top-up, the user must pass four checks. First, eKYC status must be Approved. Second, the account must have a Trusted Device set. Third, the account must not sit on any blacklist. Fourth, the user must have real fuel purchase history.
Basically, this last check is key. In practice, a real driver buys fuel often. In contrast, a cash-out fraudster does not. So the fuel history rule filters out most bot accounts on its own.
Layer 5: Inter-Wallet and Shared Wallet Cash-Out Rules
Meanwhile, fraudsters love inter-wallet transfers. They move stolen funds to a wallet they own. Then they cash out from there.
Solashi’s rule is simple. If a user runs X inter-wallet transfers to the same wallet in Y time, the system kills that feature on the account. Likewise, shared wallets get their own rule. If an owner adds and drops many members in one day or week, the system freezes the wallet.
Both rules target clear intent. So there is no auto-lift path. However, staff can lift by hand after review.
Layer 6: Card Linking Pattern Rules
Finally, stolen card fraud shows one clear pattern. The fraudster tries many cards in a short window. So Solashi flags this at the link step.
The rule is direct. If a user tries to link X different cards in Y time, the system kills card passthrough. Meanwhile, the system also filters out real bank errors first. So it only fires on true fraud patterns.
The detection rate sits at ~1.5%. In detail, that is 540 of 35,000 card links per month.
Predictive Super App Fraud Defense With Machine Learning
Rules catch known patterns. However, real fraud shifts fast. So Solashi adds a machine learning layer on top of the rules.
The model is called the DCC Fraud Score. It runs each day. For every user, it outputs a score from 0.0 to 1.0. In short, that is the chance the user will commit card fraud in the next window.
When a score crosses 0.9, the system acts on its own. First, it turns on 3DS for all card actions. Second, it kills auto top-up. So high-risk users hit friction before they can move funds.
The model reads dozens of signals. To keep it simple, here are the ones that matter most.
First, account tenure. Young accounts drive more fraud than old ones.
Second, device sharing. If many user IDs sit on one device, risk is high.
Third, spending channel. Real users buy fuel. They also shop at real stores. In contrast, fraudsters live on inter-wallet transfers and voucher buys.
Fourth, top-up failure rate. High failure rates in the last 7 days point to card testing.
Fifth, eKYC signals. Fast eKYC attempts right after signup often mean scripted flows.
Sixth, location and telco data. Access outside the target region raises the score.
This ML layer sits inside our broader AI SDLC delivery process. So the model retrains on new fraud patterns each cycle. As a result, the stack learns faster than fraudsters ship new tricks.
The Coinbook Rescue: Super App Defense Starts With Infrastructure
Basically, super app fraud defense only works on stable infrastructure. So most rescue projects start with the base first. The Coinbook case shows how.
Coinbook is a crypto exchange that Solashi took over from a legacy vendor. The old build was slow and low quality. Also, it left many attack paths open.
Solashi’s rescue ran on three tracks at once.
First, a full rebuild on Java and AWS. Solashi shipped a 27 FTE ODC to run this. The new stack scales cleanly. It also hardens the attack surface.
Second, hardened auth. Solashi rolled out multi-factor login with Google Auth and SMS. So account takeover drops hard even before fraud rules kick in.
Third, the compliance layer. Solashi wired in Sygna for travel rule compliance. We also stood up eKYC with BPO for human review. So both the tech and the ops sides pass audit.
Finally, quality governance runs on Tech Lead sign-off. Nothing ships to production without it. So fraud paths that slip past code review still get caught before release.
Compliance as the Foundation of Super App Fraud Defense
Overall, tech alone does not build trust. Enterprise buyers want proof. So Solashi’s super app fraud stack sits on two ISO certifications.
First, ISO 27001 is our Information Security Management System cert. It shows we run controlled data flows. Also, it proves that access control, audit trails, and breach response run on live procedures.
Second, ISO 9001 is our Quality Management System cert. It shows the same rigor on process. So handover, testing, and documentation all run to standard.
This posture ties into our broader stance on enterprise data privacy. In short, we build so the client keeps full control. This principle sits at the core of our #BuildForHandover series. Basically, the client should own everything at day one and at exit day.
A Super App Blueprint Checklist for Enterprise Buyers
Basically, use this list when you scope a super app fraud build. Weak answers signal risk.
First, does the vendor ship a multi-layer fraud stack instead of a single firewall?
Second, does the stack include both real-time rules and a daily ML score?
Third, does the vendor tune rules on your fraud codes rather than copy a generic template?
Fourth, does the stack cover ATO defense on device change?
Fifth, does the vendor gate auto top-up on eKYC, Trusted Device, and real spend history?
Sixth, does the vendor hold ISO 27001 with a current audit trail?
Seventh, does the vendor wire in travel rule tools like Sygna for crypto flows?
Finally, does the vendor hand over the fraud stack docs, code, and runbooks at project end?
Notably, Solashi says yes to each of these by default. Also, ISO 9001 and ISO 27001 back this stance.
Frequently Asked Questions
What is super app fraud protection in practice?
Basically, super app fraud protection covers the rules and models that block fraud in a super app. In detail, the stack runs on live user profiles, rule triggers, and ML scores. Scope covers login, top-up, card linking, transfers, and cash-out paths.
How much can super app fraud protection cut chargeback losses?
Usually, a strong stack cuts chargebacks by more than 80%. For example, one Solashi super app build dropped losses from $670,000 to $90,000 in two years. Also, it is on track for zero loss in the upcoming years!
Does super app protection slow down real users?
No. In fact, a good stack filters system errors first. So it only fires on real fraud patterns. As a result, most real users never see a step-up check.
What makes ML-driven super app fraud protection better than rules alone?
Rules catch known patterns. However, fraud shifts fast. So an ML model like the DCC Fraud Score picks up new patterns each day. In short, rules and ML together beat either one on its own.
What does Solashi bring to a super app project?
Notably, Solashi ships a full six-layer stack plus an ML score. Also, ISO 9001 and ISO 27001 back the build. In addition, the Coinbook rescue shows we can stabilize legacy fintech systems too.
Turn Super App Fraud Losses Into a Trust Moat
In short, super app fraud defense is the trust moat around your ecosystem. Weak stacks bleed cash. They also bleed users. Strong stacks turn safety into a growth signal.
Solashi builds this moat for you. Basically, we ship the six-layer rule stack. We also ship the ML score and the ISO-backed process. In addition, we hand it all over at exit so you own it fully.
Ready to start your software development process? Book a 20-minute consultation with Solashi and let us show you how we work.
