Graph Intelligence Platform for Complex Data Environments. We deliver next-gen graph intelligence analytics by supplementing 'graph' with a supporting ecosystem
Cape Town, South Africa
Joined November 2018
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Fraud, an African context
Banking fraud in Africa is a complex, evolving challenge shaped by the continent’s unique socio-economic landscape. The rapid digitalisation of financial services for large unbanked populations creates fertile ground for illicit activities. #Innovation
Sophisticated cyber criminals exploit technology gaps, while traditional fraud schemes like account takeover, social engineering & insider collusion remain prevalent. Emerging trends, e.g. mobile money fraud, further complicate the picture. #FraudPrevention #AML #CFT #Compliance
At the heart of the issue is the need for advanced context-aware solutions that can navigate the intricate web of financial behaviours, diverse transaction patterns & disparate data sources. See how we solve this, contact [email protected].
Securing Africa’s financial future
Rules-based tools & siloed systems can’t keep up with the complexity of transactional environments combined with sophisticated criminal activities & growing regulatory requirements. It’s time to embrace connected, intelligent & context-aware solutions that solve your headaches.
You need to leverage behavioural analytics to recognise what "normal" is; data orchestration to bring the right enriched data together at the right time; graph analytics to reveal illicit & X-channel behaviour; ML operationalisation to deploy & monitor AI-driven models at scale.
Are you ready for success?
All these capabilities are built into our #FraudPrevention, #AML #CFT & #Compliance solutions. Act faster, stay compliant & see the full picture. For a simple solution to complex problems, contact [email protected]. #GraphIntelligence #Innovation
AML isn’t just an endgame
With stricter regulations & financial crime rising, think beyond #Compliance to smarter, faster & safer customer journeys. Contact [email protected] for a demo.
#Innovation #CFT #FraudPrevention #RiskManagement #OperationalResilience #RegTech
Your recipe for AML success! Now garnish
No plate is truly complete without its garnish. We add that critical layer by integrating AML analytics into CDD — combining the basics with deeper intelligence.
#GraphIntelligence #AML #CFT #CDD #Compliance #Innovation #FraudPrevention
Smarter tools. Sharper insights: While CDD gives foundational customer data, AML analytics applies ML, graph analytics & behaviour modelling to detect hidden patterns, anomalies & risks that rule-based systems miss. Ready for the full platter? Contact [email protected].
Your recipe for AML success! Step 4: Cooked to perfection every time
Key to embedding AI into #AML workflows is ML operationalisation: deploying, managing & monitoring models at scale. Essentials to get right: explainability, auditability, continuous learning & ML Ops readiness.
But don’t stress about that, our AML solution allows you to build, test & operationalise ML models easily & efficiently. When operationalised effectively, AI-first AML systems don’t just detect more they adapt faster, explain better & scale smarter. #GraphIntelligence #Innovation
The future of financial crime detection isn’t just automation – it’s AI with accountability. Contact [email protected] to start your ML operationalisation journey. #RiskManagement #OperationalResilience #CFT #FraudPrevention #Compliance
Your recipe for AML success! Step 3: Adding the secret ingredient
Traditional rules-based systems often miss the subtle, non-linear patterns in money laundering. However, our secret AI ingredient, graph analytics & anomaly detection, opens up a new frontier. #GraphIntelligence
Money laundering rarely occurs in isolation. It flows through networks of accounts, customers and entities. Graph models capture these relationships, highlighting hidden intermediaries, complex layering structures & previously undetected rings. #Innovation #FraudPrevention #AML
Anomaly detection adds another layer, identifying behaviours that deviate from normal patterns – without needing predefined rules. To benefit from our secret ingredient, contact [email protected]. #CFT #Compliance #RiskManagement #OperationalResilience
Step 2: Bringing the ingredients together
A rules-based approach to #AML lacks context: transaction-level analysis is just one ingredient. Context-rich data comes from understanding customer behaviour over time, channel usage, peer comparisons & external watchlists. #Innovation
As money laundering happens in networks, storing these “blended ingredients” in a graph database enables easier detection. Graph can map relationships between entities, uncover indirect connections, circular flows & suspicious clusters invisible to traditional systems.
Basically, context-rich data leveraged by graph allows AI to detect patterns that rules miss. To find out what you’re missing, contact [email protected] today for a demo. #GraphIntelligence #FraudPrevention #CFT #Compliance #RiskManagement #OperationalResilience
Your recipe for AML success! Step 1: Prep your ingredients
For AI to thrive, preparation is key. Addressing data quality, governance issues & explainability frameworks increases accuracy & detection: Data readiness; label creation; risk scoring.
Data readiness: Consolidate transactional, customer & contextual data ax silos. Label creation: Create high-quality labels thru historical SARs, alerts & analyst inputs. Risk scoring: Not all customers/products carry equal risk. Scoring entities helps tailor models appropriately.
With these ingredients in place, AI outperforms static rules by uncovering hidden patterns & reducing false positives. Need help prepping? Using proprietary tools & techniques, we do the heavy lifting for you. To discover how, contact [email protected] a demo. #Innovation