Risk, fraud, and alpha: analytics built for modern financial institutions
Lognatech helps banks, asset managers, payment providers, fintechs, and capital markets teams turn data into decisive advantage. We build the real-time risk engines, fraud detection systems, and predictive models that improve capital efficiency, reduce losses, and uncover opportunities the market has not priced in yet.
What we deliver for financial institutions
Four core capabilities, each designed around the realities of regulated financial services. Every solution is built with explainability, auditability, and compliance in mind from the first commit.
Risk intelligence
Credit, market, and liquidity risk models that improve capital allocation, sharpen pricing, and satisfy regulators without slowing the business down.
Fraud and AML
Real-time transaction scoring, network analysis, and alert triage that stop fraud and money laundering before the money moves, not months later.
Alpha generation
Signal research, factor modelling, and alternative data pipelines that give portfolio managers an information edge grounded in robust backtests.
Financial intelligence
Executive dashboards and self-service analytics that show P&L, exposure, and performance across every desk, product, and geography in real time.
Where we make the biggest difference
These are the highest-value use cases our financial clients ask us to solve first. Each one is measured against a clear business case: loss reduction, capital efficiency, revenue uplift, or regulatory readiness.
Credit, market, and liquidity risk
We build risk models that combine traditional financial data with alternative signals, behavioural history, and real-time market data. Credit scoring improves for thin-file customers. Market risk becomes more responsive to regime shifts. Liquidity stress testing runs in minutes instead of days.
Every model is built with explainability and auditability so it satisfies internal model risk teams and external regulators without slowing down decision-making. We integrate directly with your origination, trading, and treasury systems so the outputs land where they matter.
Discuss risk analyticsReal-time fraud detection and AML
We build streaming pipelines that score every transaction, login, and payment event in milliseconds. Behind the scoring sits a graph of relationships between accounts, devices, merchants, and geographies, so organised fraud and coordinated money laundering patterns surface automatically.
Alerts are pushed into your case management workflow with the supporting evidence already attached. False positives drop sharply, genuine threats are caught earlier, and your investigators spend their time on the cases that actually matter.
Discuss fraud and AMLAlpha generation and quantitative research
We build the data pipelines, feature stores, and research platforms that let quant teams move from idea to backtest in hours instead of weeks. Alternative data is normalised, validated, and made available alongside traditional fundamentals in a single governed environment.
Signal research runs on reliable, reproducible infrastructure. Factor models are tested with proper out-of-sample validation and transaction cost modelling. Portfolio managers get an information edge grounded in rigour, not hype.
Discuss alpha researchExecutive dashboards and financial intelligence
We deliver a single source of truth for P&L, exposure, revenue, cost, and performance across every desk, product, and geography. Dashboards update in near real time and are built for the C-suite as well as desk heads and finance teams.
Regulatory reporting, IFRS 9 and IFRS 17, Basel, and internal management reporting all draw from the same governed data. Decisions get faster, reporting becomes consistent, and the business finally trusts the numbers it sees.
Discuss financial intelligenceDomain depth, engineering rigour, regulatory awareness
Financial analytics is not a generic data science problem. It needs people who understand capital, risk, regulation, and the systems that banks and asset managers actually run on. Our teams bring all four.
We speak your language
Basel, IFRS 9, IFRS 17, MiFID, Dodd-Frank, KYC, AML, RWA, VaR, LCR. We work with these concepts every day and build models and pipelines that respect them.
We work with your systems
Core banking platforms, trading systems, risk engines, market data feeds, and legacy mainframes. We integrate rather than replace, so you get value from your existing investments.
Compliance is a first-class concern
GDPR, POPIA, DIFC, SOC2, and local financial regulation. Every model is built with explainability, auditability, and data residency in mind from the first commit.
Talk to our finance team40%
faster reporting and reconciliation cycles
31%
reduction in operational bottlenecks
93%
predictive accuracy on production risk models
24/7
monitoring and support on live systems
How we engage with financial institutions
We work in short, focused phases so you see value quickly and can scale investment based on proven results, not promises. Every phase produces artefacts your model risk and internal audit teams can review.
1. Discovery
Two to four weeks to understand your data, systems, and priority use cases, and to agree the business case with clear success metrics.
2. Prototype
Four to six weeks to build a working model or pipeline on your real data so you can see the outcome and validate the economics before committing further.
3. Production
One quarter to take the prototype into production, integrated with your systems, with monitoring, governance, and documentation in place.
4. Scale
Ongoing optimisation and expansion into adjacent use cases, with a dedicated team that knows your book and your systems.