Take full control of finance and risk with AI and blockchain.

Both, though the entry point differs. A smaller lender or insurer usually starts with AI risk tools, such as fraud detection and automated underwriting, since those work against your existing transaction and claims data without needing institutional-scale infrastructure. Full on-chain settlement tends to make more sense once your volume or counterparty complexity justifies the infrastructure investment.

For assets that currently require manual reconciliation between counterparties, such as trade confirmations or custody transfers, on-chain settlement removes that reconciliation step entirely because both sides are reading from the same verified record instead of separately confirming it after the fact. Whether it is worth the switch depends on your current settlement volume and how much manual reconciliation you are actually doing today, which is a concrete cost comparison worth doing before committing.

Our settlement engines are built for institutional-grade throughput, 15,000 transactions per second, the same class of scale as global stock exchanges, so this is not scoped for typical mid-market volume by default; it is built to that ceiling from the start.

Model-driven decisions are only defensible if you can show your work. Every underwriting or claims decision is logged with the inputs and criteria behind it, so outcomes can be audited by a regulator or your own compliance team, not just trusted as a black box. High-stakes decisions typically route through human approval thresholds you define, rather than running fully autonomously from day one.

Alongside. Automating KYC and AML checks and regulatory reporting at the protocol level removes the manual, repetitive parts of compliance work, such as checking documents, flagging thresholds and filing standard reports, but the judgment calls on edge cases and regulatory interpretation stay with your compliance team, who now spend their time on those instead of routine checks.

Data privacy is designed in, not added afterward. Your data is scoped, encrypted, and never used to train a shared or public model, and specific data protection requirements, whether GDPR, DPDP or sector-specific rules, are addressed during scoping so the architecture matches your regulatory obligations from the start rather than being retrofitted.

Most existing fraud tools flag transactions against fixed rules, which is why sophisticated fraud slips through as rules age. Models trained on your own transaction and claims history catch shifting patterns in real time rather than a fixed rulebook, and the same underlying approach extends to risk-based pricing and sentiment-aware service, so it is less a point tool and more infrastructure that improves as it sees more of your data.

False positives are a real cost of any fraud system, automated or manual, so the model is tuned against your own historical data to minimize them rather than applied as an off-the-shelf threshold. Flagged transactions typically route to a human review step rather than auto-declining, so a false positive costs a review delay, not a blocked legitimate customer by default.

Yes, we engineered BEX Global, the world's first blockchain-based stock exchange, end to end, alongside 15+ years shipping production financial software. This is not a first attempt at institutional-grade fintech infrastructure.