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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Agent banking commission fraud is an emerging threat in Africa’s growing banking sector. Agents manipulate transactions, e.g. fabricating or splitting them, to inflate commission. Decentralised agent banking, combined with limited oversight & cash-based economies, makes it easy.
The solution? Strengthen transaction monitoring & implement robust, fraud-aware analytics to prevent these activities before they erode trust & financial stability. To see how we solve this contact [email protected].
Securing Africa’s financial future
#FraudPrevention
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