Fraud in 2025 operates at machine speed. What used to be sporadic abuse is now continuous, automated, and scaled across channels. Losses reflect that shift. A 2025 report from the Global Anti-Scam Alliance estimates that consumers in more than 40 countries lost $442 billion to scams in a single year, spanning payments, e-commerce, investment fraud, and account takeover.
E-commerce is a clear example. Fraud losses in this channel are projected to exceed $107 billion by 2029, driven by faster checkout flows, instant refunds, and more adaptive attack techniques. The shared constraint across all of these channels is time. Transactions are authorized and settled in seconds, while many fraud controls still assume they can react after the fact.
India’s rising cyber fraud losses
India’s digital payments ecosystem illustrates this shift clearly. With UPI and real-time transfers operating at a national scale, fraud losses have spiked. In 2024, reported cyber fraud losses exceeded ₹22,845 crore, with millions of cases linked to payment fraud, social engineering, and mule accounts.
In real-time payment systems, once funds move, recovery is rare. The cost of fraud is determined less by how well it’s investigated later, and more by whether it’s stopped in time.
Why “detect later, fix later” is no longer viable
Traditional fraud programs were built for slower systems. They entailed batch settlement, delayed authorisation, and manual intervention. By the time fraud is detected post-transaction, funds have dispersed and the same attack has already been replicated elsewhere. Prevention must now happen during the transaction, not after it.
| Explore how real-time fraud detection works in practiceSee how identity, behavioural, and transaction signals come together to stop fraud before funds move. |
What is real-time fraud detection and why does it matter?
Real-time fraud detection is the ability to assess risk and make a decision within milliseconds, before a transaction or action is completed. This is possible by evaluating identity, behavioural, device, and contextual signals. Based on risk, the system can approve, challenge, or block an action before a value exchange.
In high-risk payment flows, this capability forms the foundation of a modern credit card fraud detection system, where decisions must be made before authorisation to prevent irreversible losses.
How it differs from batch and rules-only approaches
In traditional batch or rules-only systems, suspicious activity is flagged after the transaction is completed.
Batch systems analyse transactions after processing, often hours or days later. Rules-only systems rely on predefined thresholds. These conditions must be manually updated as fraud evolves.
By contrast, a real time fraud detection system design operates in-transaction. It combines rules with machine learning (ML) to adapt continuously as fraud patterns change.
Real-time vs near real-time
Real-time decisions occur within milliseconds and directly influence transaction outcomes. Near real-time decisions occur seconds or minutes later and are better suited for monitoring or follow-up actions. For instant payments, onboarding, and account access, only real-time detection can prevent loss.
Traditional vs real-time fraud detection: side-by-side comparison
The key difference between traditional and real-time fraud detection systems lies in when and how risk is assessed. Traditional systems and their rules react after fraud occurs. Real-time systems evaluate risk during the transaction, before damage is done.
This distinction is especially critical for any credit card fraud detection system, where post-authorisation detection often means the loss has already occurred.
Side-by-side comparison
| Dimension | Traditional fraud detection | Real-time fraud detection |
| Approach | Reactive, post-transaction | Proactive, in-transaction |
| Technology | Batch processing, static rules | Streaming systems, rules + ML |
| Data used | Limited transaction data | Identity, behaviour, device, context |
| Latency | Minutes to days | Milliseconds |
| Outcomes | Fraud flagged after loss | Fraud prevented before loss |
Where rules still help and where they break
Rules are useful for enforcing clear policies, regulatory thresholds, known fraud scenarios, and clear policy violations. They are transparent and easy to audit.
However, rules break down when fraud patterns change. Modern fraud is adaptive, coordinated, and identity-driven. On their own, rules either miss new attacks or become overly restrictive, increasing false declines and harming customer experience.
Real-time fraud detection architecture: from event to decision
The goal of real-time fraud detection is simple: stop fraud before value is exchanged, without slowing down genuine users. A robust real time fraud detection system design typically includes:
Data sources & events
Real-time fraud detection begins when an event occurs such as a transaction, onboarding attempt, or sensitive account action. At this point, systems capture transactional data, identity attributes, device information, behavioural patterns, and network signals like IP or location.
Risk emerges not from individual signals, but from how these signals correlate at the moment of action.
Streaming ingestion & processing
To act in time, data must be ingested and processed continuously. Streaming architectures ensure that every event is evaluated as it happens, keeping detection aligned with live transaction flows.
Real-time feature store & context
Raw data must first be transformed into features that provide context. Examples include transaction frequency, device reputation, identity confidence, and behavioural consistency over time. This allows the system to gain a full picture of the user and the activity, rather than evaluating the transaction in isolation.
ML inference & risk scoring
ML models score each event in real time. Multiple models may operate in parallel, each focused on different fraud vectors. This enables the detection of both known and emerging threats.
Decision orchestration & actions
Based on risk scores, the system automatically decides whether to approve the action, introduce step-up verification, or block it entirely. These decisions are automated and occur within the transaction flow..
Feedback loop & continuous learning
Outcomes such as chargebacks, disputes, and manual reviews feed back into the system. This feedback continuously improves the models and decision logic over time.
How AI and ML power real-time fraud detection
Real-time fraud detection systems use multiple machine learning techniques together. Each approach addresses a different type of risk. This includes known fraud, new anomalies, and coordinated attacks. Here, systems to adapt as fraud tactics evolve.
Supervised models for known fraud patterns
Supervised models learn from historical fraud data and score new events based on previously seen patterns. They are effective at catching repeat tactics such as common payment abuse or account takeover methods, but require regular retraining as fraud behaviour changes.
Unsupervised and anomaly detection for unknown threats
Unsupervised models identify deviations from normal behaviour without relying on labelled fraud data. This makes them useful for surfacing new or early-stage fraud patterns that supervised models may not yet recognise.
Graph and network-based fraud detection
Graph models analyse relationships between accounts, devices, identities, and transactions. They are particularly effective at detecting mule networks, synthetic identities, and coordinated fraud rings that appear low-risk individually.
Handling imbalanced data, latency, and explainability
Fraud data is highly imbalanced, decisions must be made within milliseconds, and outputs must be explainable. Effective systems balance performance with transparency to meet regulatory and operational requirements.
How GenAI both accelerates and fights fraud
Generative AI has changed the fraud landscape. It enables attackers to create realistic fake documents, synthetic identities, deepfake videos, and voice-based scams at scale. On the defence side, AI improves document verification, synthetic media detection, and fraud simulation. This turns prevention into an AI-versus-AI challenge.
Which metrics matter for real-time fraud detection?
Effective real-time fraud detection balances loss prevention with customer experience and revenue impact. The following metrics show whether prevention is working at speed and at scale:
- Detection rate: Measures how much actual fraud is stopped before loss occurs. High detection only matters when paired with low friction.
- False positive rate: Indicates how often genuine users are incorrectly blocked. Elevated false positives directly translate to revenue loss and churn.
- Detection latency: Time taken to assess risk and make a decision. In real-time systems, this must remain within milliseconds to influence transaction outcomes.
- Fraud loss (% of volume): Reflects the true financial impact of fraud over time, normalised for business growth.
- Chargeback rate: Critical for card networks and compliance thresholds, with direct implications for operational cost and penalties.
- Uplift vs legacy rules: Compares approval rates and fraud reduction against rules-only systems, showing whether ML delivers measurable business value.
Which fraud types need real-time detection?
Real-time fraud detection systems are most effective where speed, identity, and coordination matter. These fraud types are difficult to manage with batch or rules-only systems.
- Payment card fraud and instant payments: For card transactions, fraud must be stopped before authorisation. A modern credit card fraud detection system relies on real-time signals to prevent losses that cannot be recovered after settlement.
- E-commerce and refund fraud: Fast-moving abuse, such as refund manipulation or promotion exploitation. Real-time systems can assess context to prevent repeat abuse without blocking genuine buyers.
- Identity fraud and account takeover: Relies on compromised credentials, social engineering, or synthetic identities. Real-time detection helps identify unusual behaviour before accounts are accessed or drained.
- Mobile wallet, UPI, and RTP fraud: Especially relevant in India. These systems leave no window for post-transaction recovery. Real-time fraud detection is essential to stop mule accounts, social engineering scams, and unauthorised transfers as they occur.
- Insurance and healthcare fraud: Often involves repeated claims, identity misuse, or coordinated activity across accounts. Real-time systems help flag suspicious behaviour early, reducing leakage and investigation costs.
- Synthetic identities and multi-accounting: Fraudsters combine real and fake data, often supported by AI-generated documents, faces, or videos. Identity-centric detection is critical at onboarding and transaction stages.
For fraud types driven by weak or synthetic identities, transaction-only controls fall short. Find out how HyperVerge prevents fraud across onboarding and payments.
Industry-specific use cases & playbooks
While the core principles of real-time fraud detection remain the same, how they are applied varies by industry.
Banking & payments
Primary risk: irreversible transactions and account misuse.
Real-time detection focuses on pre-authorisation risk scoring and continuous account monitoring to stop fraud before settlement.
Digital lending & BNPL
Primary risk: identity fraud and early-stage abuse.
Real-time checks are used to validate identity, detect synthetic profiles, and assess risk before credit is issued.
E-commerce & marketplaces
Primary risk: refund abuse, promotion exploitation, and repeat offenders.
Real-time detection evaluates checkout behaviour and user history to block abuse without increasing false declines.
Gaming & digital goods
Primary risk: bonus abuse, multi-accounting, and instant value theft.
Real-time systems identify coordinated behaviour using device, behavioural, and network signals.
Telecom & subscription services
Primary risk: SIM swaps, free-trial abuse, and account churn fraud.
Real-time detection verifies identity at signup and flags risky account changes as they occur.
Government & public sector benefits
Primary risk: duplicate beneficiaries and identity misuse.
Real-time, identity-led detection helps prevent multiple enrollments and blocks fraudulent disbursements before funds are released.
How to implement real-time fraud detection in your organization
Implementing a real-time fraud detection system is not a one-time project. It is a gradual shift. The focus should be on identity strength, real-time data, controlled ML adoption, and governance, without harming customer experience.
Implement stricter safety standards
Real-time fraud detection starts with strong identity controls. Multi-factor authentication, robust KYC processes, and biometric verification provide the foundation for accurate risk assessment. These signals increase confidence in who the user is before transactions take place.
Build the data and streaming layer
Real-time decisions require real-time data. Organizations need systems that can capture events as they happen and process them without delay. This means moving away from batch pipelines and toward streaming data flows.
Introduce machine learning gradually
Machine learning should be introduced in phases. Early models can work alongside existing rules, providing risk scores rather than full automation. As confidence grows, ML can take on more responsibility. This approach reduces operational risk.
Integrate without breaking customer experience
Real-time systems must integrate seamlessly into existing transaction and onboarding flows. Low-risk users should pass through with minimal friction, while higher-risk activity triggers step-up checks only when needed. The goal is to apply controls intelligently, not uniformly.
Modern fraud programs don’t require ripping out existing systems.
Find out how HyperVerge integrates with existing payment, KYC, and fraud stacks without disrupting customer experience.
Governance, monitoring, and compliance
Models must be monitored for performance drift, bias, and unintended outcomes. Clear audit trails and explainable decisions are required to meet regulatory expectations. The most mature systems combine automation with clear accountability to ensure fairness, compliance, and long-term trust.
Maturity model: from batch rules to identity-anchored AI defense
Most organisations progress through stages as fraud grows in scale, speed, and sophistication. This maturity model outlines how fraud prevention typically evolves.
Level 1: Rules and manual review.
Fraud detection relies on static rules and post-transaction investigation. Detection is reactive, losses occur before action, and the model does not scale.
Level 2: Real-time rules with exception queues.
Rules are applied in real time, with high-risk cases routed to manual review. Speed improves, but false positives and rule maintenance remain high.
Level 3: Real-time ML with feedback loops.
Fraud detection using machine learning scores risk in real time using behavioural, transactional, and identity signals. Most decisions are automated, with feedback continuously improving performance.
Level 4: Identity-centric, AI-vs-AI, cross-channel orchestration
Identity becomes the core control layer. Multiple AI models operate across onboarding, transactions, and account activity to detect synthetic identities, deepfakes, and coordinated fraud in real time.
How to evaluate real-time fraud detection vendors
Beyond features, teams need to assess how well a solution fits their technical environment, regulatory needs, and fraud maturity level.
Critical capabilities checklist
When evaluating vendors, focus on capabilities that impact real-time decision quality, scalability, and regulatory confidence, not just feature breadth.
- Low-latency decisioning: Must assess risk and respond within milliseconds, especially for payments and instant transfers.
- End-to-end journey coverage: Should support onboarding, transactions, and ongoing account activity, not isolated touchpoints.
- Seamless integrations: Needs to work with existing payment systems, data pipelines, and identity workflows.
- Explainability and auditability: Should provide clear decision reasons, audit logs, and regulatory alignment for compliance in regulated markets.
Build vs buy vs hybrid
Building in-house offers control. But it also requires significant investment in data infrastructure, ML expertise, and ongoing maintenance. Buying a vendor solution accelerates deployment and brings domain expertise, but may limit flexibility. Most organisations adopt a hybrid approach, balancing control with speed and expertise.
Vendor landscape comparison
| Vendor | G2 ratings (out of 5) | Key features |
| HyperVerge | 4.7 | ID verification, KYC, AI-based fraud prevention, OCR, anti-money laundering (AML) screening, document verification, deep fake detection, KYB, and customer onboarding |
| Sumsub | 4.6 | ID verification, KYC, and AML |
| Jumio | 4.1 | ID verification and AML screening |
| Onfido | 4.4 | Document verification, biometric verification, and ID verification |
| Persona | 4.5 | Risk assessment and ID verification |
| Trulio | 4.4 | ID verification and business verification |
No single vendor fits every use case. The right choice depends on whether fraud risk is driven more by identity, transactions, geography, or regulatory complexity.
Questions to ask in a vendor demo
Use this checklist to evaluate whether a solution is truly built for real-time fraud prevention at scale:
☐ How fast are decisions under real-world load?
Ask for end-to-end latency numbers in production, not lab benchmarks.
☐ What data is required to operate effectively?
Clarify required inputs across identity, device, behaviour, and transactions.
☐ How are models trained and updated over time?
Understand retraining frequency, feedback loops, and human oversight.
☐ How are decisions explained and audited?
Look for clear reason codes, audit logs, and regulator-ready explanations.
☐ How does the system handle emerging threats?
Ask specifically about AI-generated documents, deepfakes, synthetic identities, and coordinated fraud attacks.
How HyperVerge implements real-time fraud detection
HyperVerge approaches real-time fraud detection from an identity-first perspective. Instead of treating fraud only as a transaction problem, the system anchors risk decisions in who the user is, how confidently that identity is verified, and how it behaves over time. This makes fraud detection stronger at both onboarding and transaction stages.
Identity-first approach
HyperVerge’s system is built around strong identity proofing, including video KYC, biometric liveness, document verification, and deduplication. These checks establish identity confidence early in the user journey.
Trained on diverse facial variations and ID formats, HyperVerge’s AI has verified over 750 million users across 195+ countries, enabling high accuracy even at scale.
Real-time checks during onboarding and transactions
Identity, behavioural, and contextual signals are evaluated as events occur, enabling in-transaction decisions. Low-risk activity is approved instantly. Meanwhile, higher-risk actions trigger step-up verification, and high-risk activity is blocked before loss occurs.
Example outcomes
In practice, identity-led real-time detection reduces fraudulent onboardings, lowers transaction fraud, and cuts manual review effort. It also results in fewer false positives and improved compliance readiness.
Where HyperVerge fits in your architecture
HyperVerge acts as an identity and real-time decisioning layer within the fraud stack. It integrates with existing payment systems, data pipelines, and fraud tools, allowing organisations to strengthen identity controls and real-time risk assessment without rebuilding their infrastructure.
From rules to AI vs AI: where fraud prevention is heading
In 2025, fraudsters are actively using GenAI to move faster than traditional controls and bypass static rules with ease. Defending against them requires AI-native strategies that combine strong identity proofing, real-time behavioral and transaction analysis, and continuous learning across channels.
For most organizations, the next 3–6 months should focus on practical steps: strengthening identity checks at onboarding, enabling real-time risk signals for high-impact flows, introducing ML-based scoring alongside existing rules, and putting governance in place to monitor performance and compliance.
The shift is no longer optional. As fraud evolves from manual abuse to machine-scale attacks, prevention must evolve from rules to AI versus AI, with identity as the foundation of trust.
