In financial services, a wrong answer is a compliance event, not just poor UX. We build fraud detection, risk scoring, and document intelligence that hold up to regulators and auditors, explainable, real-time, and built for SOC 2, PCI, and model-risk scrutiny from day one.

Get a production-readiness review What makes fintech SI hard

01 · Use cases

Six ways we deploy SI in fintech.

From real-time fraud scoring to document intelligence, every engagement is designed around the regulatory and data constraints of financial services.

01 · Use case

Fraud & Anomaly Detection

Real-time transaction scoring models with explainability for regulatory review. Sub-100ms inference with full audit trails.

02 · Use case

Financial Document Intelligence

RAG pipelines over contracts, prospectuses, and filings. Extract clauses, compare terms, answer questions over your document corpus.

03 · Use case

Regulatory Compliance Assistants

LLM assistants trained on your compliance policies. Accurate, auditable answers for your compliance team, with citations.

04 · Use case

Credit Risk Models

ML models for credit scoring, underwriting, and default prediction. Interpretable outputs required by regulators, not black boxes.

05 · Use case

Customer-Facing SI Advisors

Personalised financial guidance interfaces with guardrails. Scope-limited conversations that stay within regulated advice boundaries.

06 · Use case

Automated Reporting

SI pipelines that generate regulatory reports, investor summaries, and portfolio commentary from structured data.

Straight answers

The short version.

How do you build explainable fraud detection for fintech?

Explainable fraud detection pairs a real-time scoring model with a record of why each transaction was flagged, so analysts and regulators can review every decision. A sound build starts with the transaction history and the labels the business already trusts, then adds features that capture behaviour across accounts, devices and counterparties. Scoring runs inline with the payment flow and writes a full audit trail: the inputs, the score, the threshold and the reasons behind it. Analysts see a short explanation next to each flag rather than a bare number, and their overrides feed back into the next training cycle. Because fraud patterns shift with markets, the model needs drift monitoring, alerts on score distributions and a regular review of thresholds. Octalcode built this pattern for Kavan Pay AI, a cross-border payments client: a hybrid LLM and graph model that scores risk in real time and explains every decision for compliance.

How do SI compliance assistants answer questions about financial policies?

An SI compliance assistant answers by retrieving the relevant passages from a firm's own policies and regulations, then writing a response that cites each source it used. The design rests on retrieval-augmented generation (RAG): policies, procedures and regulatory guidance are split along their real structure, indexed with both keyword and vector search, and tagged with version and effective date so outdated rules never surface. Every answer shows its citations, and when retrieval finds no supporting passage the assistant says so instead of guessing. Scope limits keep it inside the questions it is meant to handle, and anything touching advice or an exception routes to a named person on the compliance team. Each question, the retrieved sources and the final answer are logged for audit. Before launch, an evaluation set of real compliance questions with known correct answers measures retrieval recall and answer accuracy, and the same set runs again on every change.

02 · What makes fintech SI hard

Four challenges we solve first.

Hallucinations cost money
Financial SI requires factual grounding. Every output needs citations and a correction path, and we build that infrastructure before shipping.
Explainability is a compliance requirement
Regulators want to know why a model made a decision. Black-box SI is not acceptable for credit, fraud, or advice.
Data is sensitive and siloed
PII, transaction history, and KYC data require secure pipelines and strict access controls from the start, not as an afterthought.
Model drift in market shifts
Fraud patterns and market conditions change. Without monitoring, your model degrades silently while your team assumes it's working.

Proof, not promises

Twenty-plus SI systems shipped to production. One playbook, six industries, and a team that stays past launch.

SI systems live in production: 20+
Senior engineers & researchers: 21+
Upwork rating, 3 verified reviews: 5.0
Founded in Lahore: 2022

Verified Upwork clientSI product engagement · 5-star review

03 · Common questions

Before you brief us.

Can you build SI that meets financial regulations (FCA, SEC, FINRA)?: We build with compliance requirements as first-class constraints: explainability, audit trails, scope-limiting guardrails, and human-in-the-loop checkpoints. We work with your legal team, not around them.
How do you handle sensitive financial data?: All data pipelines are designed with least-privilege access, encryption at rest and in transit, and no training on client data without explicit agreement. We can deploy entirely within your cloud environment.
What's a typical fintech SI engagement?: Most start with a 2-week scoping sprint to identify the highest-value SI use case, followed by a build sized to that use case. Fraud detection and document intelligence are the most common first builds.
Can you work with our existing banking data infrastructure?: Yes. We've integrated with core banking systems, data warehouses (Snowflake, BigQuery), and financial data APIs. We don't require you to migrate data to work with us.

Available for new engagements

Book a free fintech SI scoping call.

30 minutes, one of our seniors, no slide deck. By the end of the call you will know whether we are the right team, and if not, who is.

Get a production-readiness review Email info@octalcode.com

Or call +1 (512) 710-5701

Let's scope it.Instant answers · free project scoping