Graph Intelligence Platform for Complex Data Environments. We deliver next-gen graph intelligence analytics by supplementing 'graph' with a supporting ecosystem
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.
Complete risk profiles by integrating #AML with #CDD during onboarding. Identify red flags, e.g. PEP status, sanctions exposure or suspicious ownership, before accounts are activated. & improve efficiency by reducing duplication, accelerating onboarding & ensuring cleaner data.
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
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
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.
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
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.
Heard the tale of the blind men & the elephant? One touches the tail & thinks it’s a rope, the other, the skin & claims it’s a crocodile, the trunk a snake & so on. The point is if you see your data in isolation, or from a fixed perspective, it doesn’t paint a true picture.
A rules-based approach to AML is built on a rigid set of if-then statements, flagging transactions based on predefined thresholds or behaviours. Major limitations are: high false positives, limited adaptability & an inability to detect novel or evolving laundering techniques.