SistaEyes turns transactions, identities, devices, and financial relationships into a continuously evolving intelligence graph—helping financial systems predict emerging threats, investigate coordinated networks, and act in real time.
Traditional rules evaluate transactions in isolation. A single transfer looks normal, a single device looks familiar, and a single account appears verified. But when relationships, behavioral shifts, and temporal links are mapped together, coordinated crime rings become unmistakable.
A ₹5,000 UPI transfer with valid PIN passes traditional threshold rules effortlessly. The rules engine sees only the isolated transfer amount.
Behind the transaction, the device IMEI is shared across 34 accounts, moving dispersed funds toward a shared dormant mule cluster.
SistaEyes links the entities, computes network pressure, forecasts the imminent cash-out, and intervenes before settlement.
Traditional fraud scoring asks: "Is this transaction fraudulent right now?" SistaEyes asks: "What is happening around this transaction, who is connected, what has changed, and where is this situation heading?"
Static rules engines process isolated data payloads after an event occurs, resulting in alert fatigue and high false positives.
Outcome: High friction for good users, zero network awareness, delayed loss discovery.
Living temporal graphs synthesize context, track behavioral shifts, forecast trajectory states, and generate explainable evidence.
Outcome: Minimum loss + minimum customer friction with continuous adversarial learning.
A transaction is only the beginning of the intelligence process. Experience how events evolve from raw ingestion into living defense.
Financial systems continuously stream events: payments, beneficiary additions, login attempts, device updates, and loan submissions. SistaEyes receives these raw signals in sub-milliseconds without jumping to premature conclusions.
Real-Time Ingestion • Temporal Knowledge Graph • Graph ML • Counterfactual Interventions
Fraud and financial crime are coordinated across entities. Click any node below to inspect real-time topology, entity connections, and risk metrics.
Traditional scores are static screenshots of the past. SistaEyes models financial risk as a continuous Markov state transition from baseline behavior toward coordinated fraud.
Predictions without rationale cannot be trusted by regulated financial institutions. SistaEyes generates structured evidence chains answering what, why, who, and what next.
SistaEyes is not just a scoring system; it is a counterfactual decision engine. Compare operational outcomes across intervention types before executing.
Adjust the contextual and network pressure variables below to experience how multi-dimensional risk scores and decision recommendations update dynamically.
High device novelty combined with network pressure indicates probable coordinated takeover or mule extraction.
From high-velocity UPI switches to digital lending platforms and institutional banks, SistaEyes powers predictive defense across ecosystems.
Detect account takeovers, unauthorized device registrations, and rapid mule draining before settlement on instant payment rails.
Uncover cross-lender coordinated applications, fabricated credit identities, and first-party fraud rings before capital is disbursed.
Extract multi-hop mule account rings, monitor behavioral drift in high-risk entities, and maintain transparent audit trails.
Bring predictive intelligence, temporal graph modeling, and counterfactual decisioning into your fraud, risk, and trust architecture.
