AI for Fraud, Risk & Trust
Fraud moves at the speed of instant payments, and in the Gulf that speed is now the norm. Eurisko builds the fraud, risk and trust use cases that keep up: transaction anomaly detection, card-testing and enumeration attack detection, scam and social-engineering risk, merchant collusion graph analytics, AML alert prioritization with grounded narratives, dispute triage and evidence packs, and KYC document authenticity, for banks, payment providers, wallets and marketplaces across the GCC, the wider Middle East and Lebanon.
Every alert explains itself with reason codes and evidence, every model is measured at a fixed false-positive budget, and every intervention keeps an analyst in the loop, because a blocked legitimate customer is a loss too.
Fewer false alarms, faster investigations, explainable decisions.
Rule-based fraud engines in the region generate alert volumes their teams cannot work: too many false positives, too little context, and investigations that start from a blank screen. Meanwhile social-engineering scams over calls and messaging apps bypass transaction rules entirely, because the customer authorises the payment.
Eurisko’s approach pairs behavioural baselines with known typologies: anomaly and supervised models score each event, graph analytics expose the networks behind merchant fraud and mule accounts, and language models read the conversation and the case file to surface coercion cues and draft the narrative an analyst would otherwise write by hand.
The output is an operations layer, not a black box: a prioritized queue, the strongest contributing signals, an investigation timeline and a recommended intervention, delivered inside the case-management and monitoring tools your fraud and compliance teams already use.
Eight fraud and trust use cases, from the first transaction to the last dispute.
Each one is designed as decision support for an analyst, measured at the false-positive budget your operation can absorb, and proven on masked or synthetic data before it sees a live transaction.
Transaction anomaly detection
Combines each customer’s behavioural baseline with known fraud patterns to score unusual transactions, returning the risk score, the strongest contributing signals and an investigation-ready event timeline.
Card-testing & enumeration attack detection
Links rapid, low-value authorization attempts across cards, devices, merchants and IP addresses to expose organized testing campaigns, the attack cluster and the compromised credentials.
Scam & social-engineering risk
Reads transfer sequences, new-payee behaviour, session context and, where consented, call or chat text to detect coercion cues, and recommends an explainable intervention rather than an automatic block.
Merchant fraud & collusion graph analytics
Builds a graph of merchants, accounts, devices and disputes, finds suspicious communities and central entities, and presents evidence paths an investigator can follow and a court can read.
AML alert prioritization & narrative
Consolidates customer, transaction and alert signals, ranks cases by likely risk and drafts a case narrative tied to evidence and typologies, with every cited fact inspectable by the analyst.
Dispute & chargeback triage
Reads the dispute description and transaction context, predicts the reason category and the likely complexity or outcome, and routes simple, complex and urgent cases to the right queue and service level.
Dispute evidence pack generator
Retrieves transaction, authentication, delivery and communication evidence, maps it to the dispute reason and assembles a review-ready case pack, flagging what is missing.
KYC document authenticity & identity consistency
Combines OCR, visual tamper detection and cross-document matching to validate identity fields and detect alterations, returning a review score with evidence rather than an automated onboarding decision.
Behaviour, typology and network, scored together.
A single event rarely proves fraud; the pattern around it does. Each transaction, login or document is enriched with the customer’s own baseline, the device and session context and the counterparties involved. Anomaly and supervised models score the event, graph features add what the network knows about the payee, the merchant or the device, and typology rules and language models add the conversational and documentary cues.
Thresholds are tuned to an explicit false-positive budget agreed with your operations lead, and every alert carries its reason codes and evidence so the analyst starts from a case, not a score. Interventions are graded: a step-up authentication, a warning in the app, a hold for review, a block, with the human decision logged.
Model performance is reported the way a fraud team thinks: recall at the fixed false-positive rate, precision in the top risk band, lead time on attack campaigns and analyst minutes saved per case.
Built for instant payments, FATF expectations and Arabic conversations.
Real-time payment rails in Saudi Arabia and the UAE have shortened the fraud window to seconds, and the region’s supervisors have responded with detailed anti-fraud, anti-money-laundering and consumer-protection expectations aligned to FATF standards. In Lebanon, the Special Investigation Commission at Banque du Liban sets the reporting bar. Our models and narratives are built to satisfy those regimes: evidence-linked, typology-aware and auditable.
Scam detection here also has to read the region’s languages and channels: Arabic in several dialects, English, and the mixed messages customers paste from chat apps. Conversation cue models are trained and evaluated on regional language data, and every customer-facing warning is delivered in the customer’s language. Data stays inside your perimeter throughout, with masked or synthetic samples used until production.
A process built on evidence.
Discovery & success metrics
A workshop with the business owner, the data owner and the technology lead fixes the decision to improve and the number that proves it.
Data readiness
We profile the sample, masked or synthetic, and say plainly what it can and cannot support before anything is built.
Prototype
Model, prompt, retrieval or optimization logic plus a working demo your team can click through, not a slide.
Evaluate & play back
Accuracy, groundedness, fairness and cost are measured against an agreed test set and reported with their limitations.
Productionize
Integration, security review, MLOps and monitoring inside your perimeter, scoped only once the evidence supports it.
Frequently asked questions.
Will this replace our existing fraud rules engine?
No, it sits beside it. Rules stay for hard policy, and the models add behavioural, network and conversational scoring the rules cannot express. Most clients start by scoring the alerts the rules already produce, cutting false positives before changing anything customer-facing.
How do you measure fraud models honestly?
At a fixed false-positive rate agreed with your operations team, on a held-out period, with recall, precision in the top band, lead time and analyst time reported together. We also report what the model missed and why, because the gaps matter more than the headline.
Can the compliance team trust an AML narrative written by a model?
The narrative is generated only from evidence the system retrieved and cites: transactions, counterparties, KYC facts and the typology it matched. Analysts see every citation and edit the draft; nothing is filed without a person approving it.
Do you need labelled fraud cases to start?
Labels help, but we can start without them. Anomaly models learn from behaviour alone, synthetic attack scenarios cover rare patterns, and confirmed cases improve the supervised models as they accumulate.
Does our data have to leave the country or our data centre?
No. For banks, telecoms and government entities in Saudi Arabia, the UAE, Kuwait, Oman and Lebanon we deploy inside your private cloud or on-premises, use open-weight models when data cannot leave the perimeter, and start with masked or synthetic samples so no customer record is needed to prove the idea.
Where do we start if we want more than one use case?
Pick a small portfolio rather than isolated ideas: one quick win, one strategic differentiator and one operational efficiency case, ideally sharing one data foundation and one evaluation framework. We run a short selection workshop with your business, data and technology leads to shortlist by value and data readiness.
Explore more from Eurisko.
AI for Credit, Underwriting & Collections
Credit scoring, limits, early warning, collections and document intelligence.
AI for Merchant & Payments Intelligence
Merchant 360, churn, decline recovery, smart retries, onboarding and risk monitoring.
AI for Enterprise Governance & Responsible AI
Policy assistants, evaluation workbenches, PII redaction, plain language and governed agents.
Artificial Intelligence & Data Analytics
Generative AI, chatbots, computer vision, NLP and predictive systems.
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