Industries Finance & Fintech
Move money faster,prove every decision.
AI Engineering and software development for fintech companies.
Banks, fintechs, and payment infrastructure teams needs to be aligned at all times, thus businesses that introduce AI into their data financial systems will hold leverage in the years to come.
Five DegreesCore Banking Loan Management
We helped build the loan management platform behind a Dutch core banking provider whose software ran at 40+ banks across Europe and North America. Five Degrees is now Akkuro by Topicus.
- $45B
- Annual loan volume
- $13B
- Deposits managed
- 133B+
- Assets under management
Why fintech companiestrust us with delivery.
A duplicated payment, a fraud model that quietly decayed, or a report figure nobody can trace back to source ends up as someone's money and, eventually, someone's question. This is why we pay special attention to the most sensitive parts of the system.
The ledger is the only source of truth
A retry that double-charges never surfaces as a bug. It surfaces as a morning where the books don't balance, which is why money movement has to be idempotent, replayable, and reconcilable to the cent.
Regulators read your architecture
If every audit is a scramble for evidence, the rules were bolted on too late. PCI DSS, PSD2, AML and KYC, SOC 2, DORA, and the EU AI Act have to shape your data boundaries and audit trail from day one.
Fraud adapts faster than your roadmap
Yesterday's precision is today's false-positive complaint, because the adversary ships weekly and labels arrive late. The retraining loop needs the same engineering care as the model.
Every decision has to be defensible
Decline someone for credit and you owe them a reason a regulator will accept. Model risk management, bias testing, and documentation are what let a model ship and stay in production.
Legacy is the integration surface
Integrations drag here because the system of record is core banking, card rails, SWIFT and ISO 20022, and a mainframe nobody wants to touch. Whatever you build has to meet them where they run.
What fintech demandsfrom engineering and AI.
01Decisioning inside the latency budget
Card authorisation and instant payments leave a fraction of a second, end to end, for everything: features fetched, model scored, rules applied, answer returned. Meeting that budget takes feature stores built for reads, models sized for the path they run on, and a fallback for the moment a dependency slows down.
02Onboarding that keeps the customer
Identity checks, document intelligence, and sanctions and PEP screening decide whether a customer finishes signing up or abandons halfway. Meeting that demand means automating the clear cases end to end and tuning alert queues so analyst hours go to cases that were genuinely suspicious.
03A back office that scales without headcount
Reconciliation, disputes and chargebacks, treasury operations, and regulatory reporting still run on spreadsheets and people at most institutions, and that cost grows with every new customer. The work is high-volume and rule-heavy, which makes it exactly where automation and agentic workflows return the most.
04One number, one lineage
A figure in a regulatory return, a risk dashboard, and a customer statement should trace back to the same event. That takes a data foundation where core banking, cards, CRM, and the warehouse resolve to one customer and one lineage, so reconciliation stops quietly consuming the team.
Words from the front lines of Fintech.
Get In Touch
Fintech companies trust us because of our governed approach to AI. Our best case study here is that we helped build the loan management platform behind a provider serving more than forty banks, where an agreement can run for thirty years and every change to it is provable.
Nortik’s Impact
Core banking loan management for Five Degrees, the Allmarkets trading platform behind forty exchanges, and a cross-chain token bridge for Kite Network.
Discover more of our insights.
Recent Posts
Fraud, KYC, and disputes: the unglamorous AI that pays for itself in fintech
Fintech AI headlines chase robo-advice and chatbots. The returns live in the back office: KYC document review, transaction enrichment, dispute handling, and fraud ops, with the audit trail regulators expect.
What an AI agent actually costs to run in production
Token bills are the visible line item. The real costs hide in the architecture: retries, tool calls, human review loops. A breakdown from live systems.
Why AI prototypes fail in production (and how to ship one that doesn't)
Most AI prototypes stall somewhere between the demo and the deploy. The problem is rarely the model. It's the evals, guardrails, and infrastructure nobody scoped.
Frequently asked questions
Everything teams usually want to know before building financial software. Not here? Ask us directly.
Yes. We built Allmarkets end to end over three years: a crypto and stock market trading platform connected to forty exchanges, answering for all of them through one API and swallowing fifty million data points a day.
We also helped build the loan management platform inside Five Degrees, a Dutch core banking provider whose software ran at more than forty banks. Between them that is the shape financial software takes: market data at volume on one side, and money movement that has to reconcile to the cent on the other.
We build to PCI DSS, SOC 2, PSD2 and strong customer authentication, and increasingly DORA, with the segregation, logging, and change control your auditors will actually test.
We are engineers, not your compliance function. We work alongside your risk, compliance, and internal audit teams, or an advisor you appoint, and make sure the software and its evidence trail hold up under their process.
Wherever possible we develop against tokenised, masked, or synthetic data, with production card and customer data reaching only the environments and people that genuinely require it, under your access controls.
Data residency, retention, and deletion get decided before the first line of code, because retrofitting them means rebuilding the data model.
That is usually the bulk of the work. We build against core banking systems, card processors, ISO 20022 and SWIFT messaging, and whatever batch windows and file interfaces are still load-bearing.
We integrate with what you have. You should not have to finish a core migration first, because the new capability ships alongside the system of record, not after it.
Every model ships versioned and reproducible, with drift and performance monitoring, champion-challenger comparison, and the documentation your model risk process expects, so we can show exactly how it behaved months after release.
Where a decision affects a customer, explainability is built into the output, because 'the model said so' is not an answer you can send to an applicant or a regulator.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.