Executive Summary
Bangladesh's financial services sector is being reshaped by three forces: the rapid scaling of mobile financial services and digital banks, the regulatory professionalisation of the Dhaka and Chittagong stock exchanges, and the emergence of a serious local quant and algorithmic-trading community. SGT Systems Limited's Financial Automation — Algorithmic Trading and FinTech service is engineered to help brokers, asset managers, banks, MFS operators and emerging FinTechs build the production-grade infrastructure that this environment now demands.
This service is for CTOs, heads of trading, heads of compliance, treasury heads and FinTech founders who are responsible for systems where milliseconds, data integrity and regulatory accuracy translate directly into money. We provide end-to-end engineering: low-latency trading infrastructure, exchange and market-data connectivity, automated trading engines, KYC / AML pipelines, payment-gateway integrations and the supporting cybersecurity, observability and DR posture.
This paper describes the methodology, the technology stack, the engagement model and the kind of outcomes a typical financial-services engagement produces. It assumes a reader who already understands their own business and is evaluating SGT Systems as an engineering partner.
Industry Context
The global algorithmic-trading market crossed USD 19 billion in 2024 and is projected to exceed USD 42 billion by 2032. The global FinTech market is materially larger and growing in double digits, driven by mobile money, embedded finance, real-time payments and the rapid digitisation of compliance.
Bangladesh's specific position is interesting. The DSE has rolled out its next-generation matching engine and FIX-based order entry; the BSEC has formalised algorithmic-trading rules and now requires registered algo strategies, risk controls and audit trails; the Bangladesh Bank has issued comprehensive guidance for digital banks, MFS, payment service providers (PSPs) and payment system operators (PSOs); and the National Payment Switch Bangladesh has been operating a real-time domestic rails for several years.
On the consumer side, mobile financial services have crossed 220 million registered accounts and a daily transaction value in the tens of thousands of crores; bKash, Nagad and Rocket dominate, with digital banks (Kori, Nogod, NRBC and others) now scaling rapidly. The KYC / AML burden under the Bangladesh Financial Intelligence Unit (BFIU) is significant and unsustainable without automation.
On the institutional side, a growing community of brokerages and proprietary trading desks are deploying market-making, arbitrage and execution-algo strategies. The infrastructure they need — co-located low-latency Linux, FIX gateways, deterministic kernel tuning, in-memory order books, real-time risk — is exactly what we build.
Challenges We Solve
- Latency drift and missed fills. Off-the-shelf trading software running on un-tuned hardware delivers inconsistent execution latency, causing missed fills and slippage that erode strategy profitability.
- Brittle exchange connectivity. FIX session management, sequence number recovery, drop-copy handling and failover discipline are easy to get wrong and expensive to debug under load.
- Manual KYC bottlenecks. Onboarding queues of days or weeks kill conversion. Automated document capture, OCR, liveness, watchlist screening and risk scoring collapse that to minutes.
- AML and fraud blind spots. Rule-based AML engines generate volumes of false positives; ML-augmented behavioural models cut alert volume materially while improving true-positive rate.
- Payment-gateway integration sprawl. Every PSP, MFS and card network speaks slightly differently. A unified payment-orchestration layer is essential to maintain agility.
- Regulatory reporting burden. BFIU, BSEC, NBR and Bangladesh Bank reporting consumes significant manual effort. Automated extraction from the system of record cuts cost and error rate.
- Cybersecurity and DR posture. The Bangladesh Bank ICT Security Guideline and PCI-DSS, where applicable, impose specific architectural requirements that off-the-shelf solutions rarely meet out of the box.
Our Approach
Discovery & Requirements Gathering
We begin with a structured discovery led by senior engineers with capital-markets or banking background. We examine your strategy, your existing infrastructure, your data feeds and your regulatory perimeter, then produce a written Current-State Assessment with a target architecture and a quantified case for change.
Solution Architecture
For trading clients we design from the wire up: NIC selection, kernel bypass (Onload, DPDK) where justified, in-memory order book, deterministic GC strategies in the matching path, FIX session farm, market-data normaliser and a low-latency risk pre-trade. For FinTech and banking clients the architecture is event-driven: Kafka backbone, microservices in Go / Python / Java, polyglot data tier, idempotent APIs, exactly-once payment semantics.
Hardware Selection & Procurement
For trading we specify dual-socket Xeon or EPYC servers with high single-thread clock, Mellanox / Solarflare NICs, NVMe storage tuned for sequential journal write, and a clean clock-distribution architecture (PTP grandmaster, boundary clocks). For FinTech we typically deploy on AWS, Azure or local-cloud (BD Cloud, Bangladesh Data Centre Company) in active-active configuration with cross-region DR.
Implementation & Integration
Implementation follows trunk-based development with mandatory code review, static analysis and a CI pipeline that runs unit, integration, performance and security tests on every commit. Production deployment is gated through a UAT environment that mirrors production scale; trading strategies are validated against historical tick data and shadow-traded against live markets before being given real capital.
Deployment & Commissioning
Go-live is staged: paper-trade for one to two weeks, then live with small notional, then progressive size-up against pre-agreed risk gates. For FinTech systems we operate dark launches and percentage-based traffic shifting via feature flags so that customer impact of a regression is bounded.
Operations & Optimisation
The optional Managed Service includes 24x7 NOC monitoring, SLA-backed incident response, weekly performance reviews and a quarterly architecture-and-risk review with your CTO and CRO. For trading clients we provide a daily latency and PnL-attribution report; for FinTech clients we provide a daily reliability scorecard with SLO compliance, error budgets and a forward-looking risk register.
Technology Stack
| Layer | Technologies |
|---|---|
| Trading Infrastructure | Linux with PREEMPT_RT or tuned-latency kernel; Mellanox / Solarflare NICs with Onload kernel bypass; PTP grandmaster clocks; in-memory order books in C++ / Rust; Aeron messaging; LMAX-style disruptor pipelines |
| Exchange & Market Data | FIX 4.2 / 4.4 / 5.0 SP2 sessions; QuickFIX/J and custom session managers; ITCH / OUCH parsers; REST and WebSocket clients for crypto and global venues; drop-copy and recovery handling; market-data normaliser and tick recorder |
| FinTech Backend | Go and Java microservices on Kubernetes; Kafka and Redpanda event bus; PostgreSQL and CockroachDB; Redis for sessions and idempotency; gRPC and REST APIs; event-sourced ledgers for payments |
| KYC / AML & Fraud | Document OCR (Tesseract, AWS Textract, Azure Form Recognizer); liveness and face-match (commercial SDKs); UN, OFAC and BFIU watchlist screening; transaction-monitoring engine with rule and ML layers; case-management workflow |
| Payments & Connectivity | Connectors to bKash, Nagad, Rocket, Upay, Tap; NPSB and BACPS rails; Visa / Mastercard via local acquirers; SWIFT for cross-border; ISO 20022 message handling; payment orchestrator with retry, idempotency and reconciliation |
| Observability | Prometheus + Grafana, OpenTelemetry tracing, ELK / OpenSearch for logs, latency histograms at every hop, business-KPI dashboards for desk heads, real-time PnL and exposure dashboards |
| Security & Compliance | TLS 1.3, mTLS internal, HSM-backed key management (Thales, Utimaco), tokenisation for card data, PCI-DSS aligned where applicable, ISO 27001 aligned operating procedures, alignment with Bangladesh Bank ICT Security Guideline, full audit logging with WORM storage |
Engagement Model
| Phase | Duration | Deliverables | Payment Trigger |
|---|---|---|---|
| 1. Discovery | 3–4 weeks | Current-state assessment, target architecture, regulatory gap analysis, business case | Fixed fee on report acceptance |
| 2. Detailed Design | 4–6 weeks | Component-level design, capacity model, security & DR design, test strategy | 30% of phase on sign-off |
| 3. Build & UAT | 3–6 months | Production-grade build, CI/CD pipeline, performance & security test reports, UAT sign-off | Milestone-based on test acceptance |
| 4. Go-live | 4–8 weeks | Staged production go-live, paper-trade or dark-launch, progressive ramp | On production cut-over |
| 5. Hyper-care | 90 days | 24x7 on-call response, daily performance & reliability reports, optimisation tickets | Included in build |
| 6. Managed Service (optional) | Annual | 24x7 NOC, SLA, capacity planning, quarterly architecture & risk reviews | Quarterly in advance |
Case Study Example
A Dhaka-based brokerage with a growing proprietary desk engaged SGT Systems to rebuild their algorithmic-trading stack. The legacy setup was a Windows-based vendor platform running market-making on DSE equities with end-to-end (wire-in to order-out) latency averaging 9 milliseconds and a 95th-percentile of 38 milliseconds — competitive when the desk was founded but no longer.
We rebuilt the stack on tuned Linux with Solarflare NICs and Onload kernel bypass, an in-memory order book in C++, a custom FIX session manager with strict sequence-recovery semantics, and a real-time pre-trade risk layer that enforces BSEC-mandated controls in well under a microsecond. Market data is normalised and fanned out via Aeron to the strategy engines.
Six months after go-live the measured outcomes were: median wire-in to order-out latency reduced from 9 ms to 78 microseconds, 99th-percentile latency from 38 ms to 240 microseconds, fill ratio on top-of-book quotes improved by a factor of 4.3, and the desk's daily Sharpe on the market-making book improved by 0.8 against the prior six months. The compliance team also gained a real-time exposure dashboard that has since been adopted by the firm's CRO.
Why SGT Systems
Financial-systems engineering is a niche where the cost of being wrong is uniquely high. Three things distinguish our practice.
First, the seniority of the engineering bench. Our financial-systems leads have shipped low-latency trading code, payment ledgers and KYC pipelines into production at firms where downtime is measured in lost crore. We do not learn FIX session recovery on your project.
Second, regulatory fluency. BSEC's algorithmic-trading framework, the Bangladesh Bank ICT Security Guideline, BFIU reporting standards, the Payment and Settlement Systems Regulation 2014 and PCI-DSS are part of our standard delivery vocabulary. The compliance officer is in the room from week one, not on a Friday afternoon at go-live minus two weeks.
Third, operational discipline. We operate under written SLAs with audited evidence, change-management runbooks, war-game exercises and a defined post-incident review process. Our NOC runs out of a Tier-3 facility in Bangladesh with documented on-call rotations and named senior engineers on every severity-1 page.
Pricing & Procurement
Financial-automation engagements are quoted by scope. Indicative ranges: BDT 28–75 lakh for a discovery and detailed-design phase; BDT 1.2–4.5 crore for a full algorithmic-trading platform build (excluding hardware); BDT 35 lakh–2 crore for FinTech back-end builds depending on scope. KYC / AML, payment orchestration and regulatory reporting modules are quoted per module.
Quotes are issued in BDT and USD with milestone-based payment tied to written acceptance of each deliverable. For trading clients we offer an outcome-aligned managed-service model where part of the ongoing fee is tied to system latency and uptime SLA. For FinTech clients we offer per-transaction pricing on the payment-orchestration and KYC layers. AMC and managed-service pricing typically lands between 16 and 22 percent of capital cost per year for trading platforms, and is volume-based for FinTech.
Frequently Asked Questions
Are you BSEC and Bangladesh Bank conversant?
Yes. Our senior consultants include former exchange and bank infrastructure engineers, and we operate against BSEC's algorithmic-trading framework, the Bangladesh Bank ICT Security Guideline, BFIU's reporting standards and PCI-DSS where card data is in scope.
Do you take latency-sensitive co-location work at the DSE?
Yes. We have engineering experience with DSE co-located infrastructure, including the network and clock-distribution design that is required to make a co-located setup actually deliver the latency advantage the rack rental is paying for.
How do you protect customer data and meet data-residency rules?
We default to deploying within Bangladesh — either on-premises, in our clients' own data centres, or in Bangladesh Data Centre Company / BD Cloud regional facilities. Where regional cloud is required (typically for disaster recovery) we restrict it to tokenised or anonymised data and document the data flows in a formal DPIA.
Can you integrate with our existing core banking?
Yes. We have built connectors to Flexcube, Finacle, T24 and several locally developed core-banking systems. Integration is typically via ISO 8583 for card flows, ISO 20022 for payment messaging, and REST / SOAP for product and customer master data.
What is your testing discipline?
Every build runs through unit tests, integration tests against a mocked exchange or PSP, performance tests at 2x expected peak, security tests (SAST, DAST, dependency scanning) and a manual regression on a UAT environment that is a scaled clone of production. For trading code we additionally backtest against historical tick data and shadow-trade live for a defined period.
How do you handle production incidents?
Under managed service we operate a 24x7 NOC with documented runbooks, severity levels and SLA-backed response. Severity 1 incidents trigger immediate phone-call escalation to the client's on-call CTO or COO; post-incident reviews are delivered within five working days with a written remediation plan.
Do you support cryptocurrency exchange or wallet integrations?
We have engineering capability to integrate with global cryptocurrency exchanges over REST and WebSocket APIs and to operate the surrounding infrastructure. We will only undertake such work where it is permitted under the client's regulatory perimeter; the regulatory position in Bangladesh on cryptocurrency remains restrictive, so we typically engage on this only for offshore or licensed-elsewhere entities.
What is your stance on AI and ML in trading and fraud?
Pragmatic. ML adds genuine value in fraud detection, AML alert reduction, customer segmentation and certain execution-algo signal layers. It is usually a poor fit for the deterministic, latency-critical path of a matching-or-routing engine. We deploy ML where the data and the use-case justify it, with full model-governance, drift monitoring and explainability for compliance.
Next Steps
The right first step depends on your context. For trading clients, a 90-minute technical deep-dive with our senior engineers; for FinTech and banking clients, a half-day workshop with your product, engineering and compliance leads. Both are offered free of charge for qualified prospects. Reach out via the contact page with a short description of your business and the systems you are looking to build or rebuild, and a member of our financial-systems team will respond within one business day.