- Go to the KuppingerCole Booking tool webpage.
- Under Select a Service, select Leadership Compass Briefing 45 mins.
- Under Select Staff, scroll down and select John Tolbert
- Choose date and time between Sept 28, 2026 - Nov 6, 2026 that are convenient for your team.
- You will receive an MS Teams invite
- Please prepare a slide deck and provide it to the analyst
Recommended briefing outline
1. Company and Product Context (5 minutes)
- Brief company overview and positioning in the FRIP market
- Product scope and major components relevant to this Leadership Compass
- Primary customer profiles by vertical (banking, payments, ecommerce, insurance, healthcare, telecom, gaming, G2C), use case focus (ATO, NAF, payments fraud, scams and APP fraud, AML, bot and AI agent abuse), and typical deployment scenarios
2. Architecture and Deployment Model (10 minutes)
- Supported delivery models (SaaS, self-managed, hybrid, on-premises) and container platform support
- Real-time scoring methodology, data ingestion pipeline, and end-to-end decision latency profile
- Multi-tenancy, data residency, and localization capabilities
- Approach to handling the entity classes that FRIP must distinguish: human users, malicious bots, adversarial AI agents, authorized AI agents acting on behalf of legitimate users, and compromised authorized agents
- How risk signals and decisions are exposed and consumed: REST APIs, SDKs (web, iOS, Android), SSF/CAEP, webhooks, event streams, and synchronous versus asynchronous scoring patterns
- Vertical adaptations and modularity: how the platform supports finance-specific use cases (3DS2, PSD2, AML/KYC) versus general FRIP use cases across ecommerce, insurance, telecom, healthcare, and gaming
3. Core Capabilities (can be discussed during the demonstration)
Please structure this section explicitly around the evaluation categories below. We do not expect every solution to cover all capabilities, so please focus on those you do support.
Platform Foundations: API design and management, MFA and RBAC/ABAC for administrators, customizable connector toolkits, auditing and dashboarding, scalable high-availability architecture, use of machine learning for risk analysis, integration with customer SIEM and ITSM, ISO 27001 and SOC 2 Type II certification, and AML and KYC compliance features
Identity Verification: Built-in user attribute verification, integration with third-party IDV and authoritative attribute providers, IDV APIs and SDKs, deepfake detection for document liveness, face liveness, and injection attacks, audio deepfake and voice cloning detection for voice IDV and call-center authentication, and use of content provenance signals such as C2PA for media presented during IDV
Credential Intelligence: Use of credential intelligence drawn from the vendor's own network of customers, and inclusion of credential intelligence from external Identity Providers and third-party sources
Device Intelligence: IP reputation, geo-location and impossible travel detection, mobile network and Wi-Fi network analysis, device ID and fingerprinting, device reputation and health, IMEI/SIM analysis, SIM swap detection, known-user-on-unknown-device checking, and device jailbreak or rooting detection
User Behavioral Analysis: Login context analysis, transaction details analysis, data privacy compliance, and detection of behavioral patterns consistent with LLM-driven or agentic interaction
Behavioral Biometrics: Obfuscated JavaScript and secure SDK implementation, breadth of features and modalities examined across computer and mobile devices, and resistance to behavioral mimicry generated by AI agents and replay of harvested biometric telemetry
Bot and AI Agent Detection and Management: Detection of bot versus human users, bot intent classification (good, gray, malicious), unobtrusive or user-friendly challenges, policy-based bot handling, distinguishing AI agents from both bots and human users, detection of computer-use agents and LLM-driven headless browser frameworks, and recognition and authorization of legitimate user-deployed AI agents
Risk Engine: Customer-configurable risk policy authoring, risk score and rationale output, integration with third-party authentication services, customer IAM systems, and customer LOB applications, robustness against adversarial ML and model evasion attacks, and support for agent identity signals (token exchange context, agent attestations) as inputs to risk decisioning
Innovative Capabilities: Cross-customer credential and device intelligence sharing, biometrics and liveness for remote identity verification and authentication apps, unobtrusive methods for fraud detection, use of Generative AI in the fraud analyst interface (natural-language queries, case descriptions, executive reporting), OFAC/PEP and sanctions compliance features, 3DS2.x and EU PSD2 compliance, no-code/low-code policy authoring, call center integration, detection of sophisticated smishing, vishing, and quishing, recognition of authorized agent identities via OAuth 2.0 Token Exchange or CIBA, support for SSF/CAEP, and real-time multi-channel correlation for AI-coordinated attacks spanning web, mobile, and call center
4. Demonstration (Mandatory, 20-30 minutes)
- Risk policy authoring and rule configuration, orchestration of third-party sources, including how risk scoring is composed, weighted, and tuned
- Real-time risk scoring across at least one realistic transaction or login flow, with explainability and score rationale output
- Identity verification flow including document liveness, face liveness, injection attack detection, and where applicable voice IDV with deepfake and voice clone detection
- Bot and AI agent detection scenarios distinguishing humans, legacy bots, adversarial AI agents, and authorized user-deployed agents
- Behavioral biometrics and user behavior analysis in a realistic session or transaction
- Fraud analyst console: case management, investigation workflow, and any GenAI-assisted querying, summarization, or reporting
- Integration with authentication systems, IAM, customer line-of-business applications, and call center platforms
- Compliance-specific capabilities (3DS2, PSD2, AML/KYC, OFAC/PEP) where applicable to your platform
Focus on differentiating features and realistic workflows. Please use a demo environment with meaningful complexity, such as multiple signal sources, varied transaction types, and realistic policy configurations.
5. Differentiation and Roadmap (5–10 minutes)
- Clear articulation of what differentiates your platform from peers in the FRIP market specifically in terms of signal breadth and quality, model performance and explainability, vertical specialization, and readiness for agentic AI threats
- Near-term roadmap priorities relevant to FRIP, particularly around deepfake and voice clone detection, AI agent detection and authorization, adversarial ML defense, and real-time multi-channel attack correlation
- Known limitations or trade-offs, for example, which FRIP pillars are not natively covered, and how your platform is designed to complement rather than replace specialized solutions in those areas. Openness is appreciated and expected.
6. Wrap-Up and Q&A
- Reference customers, deployments, or representative use cases that illustrate the platform operating across multiple FRIP pillars in real-world fraud reduction scenarios
- Time for clarification questions and discussion
