Industries
Fintech & banking
Payment and lending platforms, KYC and scoring, ledgers that reconcile, and AI that reads documents and flags risk — built to audit, encrypt and survive regulators.
What are the typical challenges in fintech and banking software?
The hardest part of a fintech product is rarely the payment itself; it is keeping the product correct while regulation, partners and fraud patterns change underneath it. PSD2 (Directive (EU) 2015/2366) introduced strong customer authentication and open-banking APIs, and DORA (Regulation (EU) 2022/2554) has applied to EU financial entities since 17 January 2025, adding ICT risk management, incident reporting and third-party oversight to the backlog. Meanwhile onboarding still stalls on manual KYC, ledgers drift from processor and bank statements, and rules written last quarter miss this quarter’s fraud.
- Regulatory requirements (PSD2, AML, GDPR, DORA) change faster than the product roadmap
- Manual KYC and document checks stretch onboarding to days
- Reconciliation breaks between ledgers, processors and banks
- Fraud patterns evolve; static rules lag behind
What do we build for fintech and banking?
We build the transactional core and the surfaces around it: payment gateways, wallets, lending and BNPL platforms, double-entry ledgers with reconciliation and regulatory reporting, onboarding flows wired to KYC/AML providers, and mobile banking or investment apps. Services run on Python (FastAPI, Django) or PHP (Laravel, Symfony) over PostgreSQL with strict transactional guarantees; every external processor or KYC vendor sits behind an isolated adapter so that a provider change never touches the ledger. Where card data is in scope we design to PCI DSS v4.0, whose future-dated requirements became mandatory on 31 March 2025 (PCI SSC, 2024).
- Payment gateways, wallets, lending and BNPL platforms
- Core ledgers, reconciliation and reporting systems
- Customer onboarding with KYC/AML providers
- Mobile banking and investment apps
Where does AI move the numbers in fintech?
AI pays off in fintech where volume meets judgement: credit and risk scoring, document-heavy KYC, transaction monitoring and support. According to the McKinsey Global Payments Report (2024), global payments revenue reached roughly $2.4 trillion in 2023, so even fractional fraud and false-positive improvements are material. The constraint is explainability: the EU AI Act (Regulation (EU) 2024/1689) lists creditworthiness assessment of natural persons as a high-risk use, which means logging, human oversight and documentation are part of the model, not an afterthought. We therefore ship interpretable models or per-decision explanations, and keep a human in the loop for decisions with legal effect. See AI & machine learning.
- Credit scoring and risk models with explainability
- Document AI for KYC packages, statements and contracts
- Transaction anomaly and fraud detection
- Support assistants grounded in policies and product terms
Rules vs machine learning for fraud detection: which do you need?
Most teams need both. Rules are transparent, cheap and instant to change, so they remain the right tool for hard policy limits and sanctions screening. Machine-learning models catch novel patterns and reduce false positives on volume, but need labelled history, monitoring and a governance file. The usual path is rules first, a shadow-mode model second, and a blended decision once the model has proven itself on your own data.
| Criterion | Rules engine | ML model |
|---|---|---|
| Time to first value | Days; no historical data needed | Weeks to months; needs labelled history |
| Novel fraud patterns | Missed until a rule is written | Detected as anomalies, with false positives |
| Explainability | Full, by construction | Requires SHAP-style explanations or interpretable models |
| Operating cost | Grows with rule count and analyst time | Grows with retraining, monitoring and drift handling |
| Regulatory posture | Easy to audit | Auditable with model cards and logging (AI Act, 2024) |
Why Glanit for fintech?
Because compliance, audit trails and encryption go into the first sprint, not the last. Over 16 years and 500+ projects we have learned that a ledger that cannot be reconciled and a model that cannot be explained both end the same way: with a stalled launch. GDPR (Regulation (EU) 2016/679) sets the data-protection baseline for every EU-facing product, and we design retention, erasure and access control to it from the start. Every model we deliver comes with a model card and per-decision explanations a compliance officer can read, and every integration comes with timeouts, retries and monitoring. Security testing is covered under security services.