We promise fast payouts, but that leaves very little time to catch fraudulent claims. Today our claims specialists spot suspicious cases by hand, which does not scale. A good solution must flag risky claims within seconds, before money moves, plug into our app backend, explain why a claim was flagged, and keep pricing transparent.
Fintech · Wealth & Investing · 50–200 · Asia-Pacific #b042_14
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We are a listed micro-investing app that rounds up everyday spending into diversified portfolios, holding assets through independent custodians.
- Industry
- Fintech · Wealth & Investing
- Size
- 50–200 employees
- Region
- Asia-Pacific
- Tools budget
- $1K–5K per month
- Needs
- Fraud prevention. We alert customers on unusual activity, but our rules generate noise and miss newer account takeover patterns.Today: customer alerts and ID checks layered with multi-factor login, backed by an insurance policy · Urgency: this yearAutomated reconciliation. Matching our ledger against several custodians and asset types each day is slow, spreadsheet-driven work.Today: finance matches client ledger balances to units held by separate trustees and a crypto custodian · Urgency: this year
- Who decides
- cfo finance · security review
- Expects from suppliers
- Behavioural fraud signals for small recurring transactions; Daily reconciliation across multiple custodians; Reasonable pricing for a listed small-cap
- Not a fit
- Card-only fraud tools; Reconciliation tools that ignore digital assets
- Stack
- cloud: AWS-class hyperscaler, core banking / ledger system
- Buying triggers
- security incident risk, cost pressure
Needs
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Cooperation
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Our treasury staff still match prefunded partner wallet balances against partner statements by hand in spreadsheets. Every partner formats statements differently and currencies vary widely. We want an automated matcher that ingests many statement formats, connects to our ledger through APIs, and explains unmatched items in plain terms so ops can resolve them fast.
We wish for a fraud monitoring tool that plugs into our cloud warehouse and turns raw transaction data into always-on detection, replacing our ad hoc queries. We would judge it by how fast analysts can tune rules without engineers, whether alerts stay explainable, European data residency, clear pricing, and a team trial that proves value.
Our billing runs on metered consumption, and partner royalties flow out of that same usage. Every period we must reconcile each metered record to invoices and royalty payouts, then hand auditors clean evidence. Sampling won't do. A good fit reconciles every record at huge scale, keeps a full audit trail, and supports our controls testing.
As a card provider for small and mid-sized businesses, we struggle to spot misuse on employee cards without blocking legitimate spend. Our analysts review rules by hand, and false alarms frustrate honest customers. A good solution must adapt quickly, explain its decisions, keep data in Europe, and fit our existing cloud setup.
We wish a tool existed that watches failed postings and cash application exceptions inside our customers' accounting systems, explains the cause in plain language, and proposes or applies the fix with approval. We would judge it by fewer manual fixes per week, faster time to clear exceptions, and native connectors to mid-market ERPs.
We open our platform to anyone, and bad actors keep abusing free credits and stolen payment methods. We need risk scoring at sign-up and at payment, decided in real time, with low false positives for honest developers. Retail-style fraud tools and manual review queues won't work. Clear APIs our engineers can wire into billing are essential.
We are rebuilding how we watch card transactions for fraud, and we must decide what to build ourselves versus buy. Our merchants are small and local, so patterns look different from big retail. A good solution must score in real time, combine explainable rules with models, and be proven on small-merchant portfolios. Enterprise-only pricing is a no.