Fintech Software Development

Financial platforms must prioritise transactional integrity, consented data access, and rigorous auditability. Architectures handle high‑throughput events with low latency and idempotent operations to avoid duplicates. Identity, authorisation, and strong customer authentication sit across critical journeys, and every sensitive action is logged. Integration spans core systems, payment gateways, and third‑party providers via API gateways with versioning and policy enforcement. Delivery pipelines add secure SDLC controls, segregation of duties, and traceable change management suited to regulated contexts. Reliability, observability, and disaster recovery are built in from the start. They include clear RTO and RPO targets. The result is resilient, compliant software that supports payments, account services, reporting, and partner ecosystems at scale.

We implement an API‑first approach with explicit contracts, schema validation, and consistent versioning. Gateways apply rate limiting, threat protection, and consent enforcement, while service‑to‑service authentication secures internal traffic. For open banking/PSD2, we deliver consent flows, TPP onboarding and verification, and testable sandboxes, aligning with ecosystem terminology such as AISP, PISP, and SCA from open banking resources. Read/Write API behaviours are observable end‑to‑end, with tamper‑evident audit trails for access requests and payment initiation. Progressive rollout and conformance testing reduce integration risk. This balance preserves developer experience and interoperability, while maintaining robust control across data sharing and payment flows.

Security is embedded from discovery to operations. We run threat modelling for domain‑specific risks, apply SAST/DAST and dependency scanning, and validate infrastructure as code. Secrets management, encryption in transit and at rest, and least‑privilege access are standard. CI/CD pipelines enforce quality gates and traceability, preventing promotion of artefacts that fail security or compliance checks. In production, SIEM correlates logs from applications, APIs, and infrastructure, with tuned detections for anomalous transfers and privileged activity. Incident playbooks and rehearsals accelerate response and recovery. Audit trails cover sensitive actions to support investigations and regulatory reviews, ensuring continuous assurance without slowing delivery.

We design for reliability at multiple layers. Stateless services and horizontal scaling manage variable loads; durable data stores with replication protect integrity. Queues and event streaming smooth peaks and isolate bottlenecks. We define SLOs for key journeys and use error budgets to guide releases. Load and chaos testing validate capacity and failure modes before traffic ramps up. Disaster recovery is verified through drills, with RTO/RPO targets agreed early. Cost-to-serve is tracked through telemetry and rightsizing. This keeps things efficient while maintaining a good user experience. Clear runbooks and dashboards keep operations predictable as volumes grow.

We treat data as a product with defined ownership, versioned contracts, and pipeline checks. Lineage tracks each transformation from source events to analytics and reporting. Quality rules monitor completeness, accuracy, and timeliness, with alerts routed to issue queues. Personally identifiable and sensitive fields are classified, masked where appropriate, and governed by retention policies. Reconciliation processes compare operational events with ledger entries and settlements to prevent drift. For reporting, we maintain traceability back to raw data, enabling defensible figures and faster audits. This governance model reduces defects and supports trustworthy insights.

Small teams own clear domains and release via trunk‑based development with short‑lived branches. Test suites cover unit, contract, integration, and end‑to‑end paths; environment parity reduces surprises. CI/CD pipelines enforce gates for security, performance, and compliance. Feature flags, canaries, and blue‑green deployments limit blast radius. After release, we keep an eye on key indicators like latency, error rates, and abandonment. This helps us spot problems early. Reference components for consent management, audit logging, and payment connectors speed delivery while preserving consistency and control.

We implement explicit payment state models and idempotent operations to avoid duplicates. Connectors to schemes and gateways run behind an API gateway with retries, back‑off, and circuit breaking. Events capture authorisations, captures, refunds, chargebacks, and fees for downstream reconciliation. Exceptions surface to operator queues with full context and audit history. Reporting paths are validated against control totals, maintaining financial accuracy and regulatory confidence. This approach reduces operational risk and improves transparency across the payment lifecycle.

Yes. We focus on high‑value applications such as anomaly detection, risk scoring, document processing, and personalisation. Data pipelines include lineage and quality checks; model governance covers versioning, performance monitoring, and bias evaluation. Sensitive features are minimised; human‑in‑the‑loop is used where necessary. Models are deployed behind auditable services with rate limits and rollback strategies. This enables meaningful automation and insight while preserving explainability, control, and compliance.