5 Ways to Strengthen Fraud Detection and Risk Analytics

If You Only Change Five Things About Fraud and Risk in the Next 24 Months…

• Shift from rules-based alerts to AI-driven behavioral analytics – in phases, not all at once.
• Treat zero trust as a talent and operating model challenge, not just a security project.
• Unify KYC, AML, and fraud into a single risk view to cut duplication and blind spots.
• Measure ROI of fraud platforms and people together, not in isolation.
• Build a sustainable fraud operations workforce – core FTE, plus contingent and project-based talent – through structured technology staffing services.

Fraud is no longer a back-office compliance problem. According to McKinsey’s 2025 payments resilience report, online payments fraud losses are projected to exceed $362 billion between 2023 and 2028. And 77% of customers say they would leave a bank that failed to protect them.

For CIOs, CHROs, COOs, and CFOs, this is a strategy, talent, and investment question rolled into one. AI fraud detection in banking is accelerating, but the platforms only perform when the operating model and the people behind them are right.

This guide breaks down five practical ways to strengthen your fraud detection and risk analytics strategy – each grounded in analyst data and built around what your teams can realistically execute. By the end, you will have a clear view of where to start, what talent you need, and how to measure progress.

Way 1: Redesign Your Fraud Analytics Operating Model, Not Just the Tech Stack

US banks filed 2.6 million Suspicious Activity Reports in FY2024 – roughly 7,100 per day, according to Deloitte’s 2026 banking outlook. That volume makes reactive, siloed fraud operations unsustainable.

A fraud analytics operating model defines how teams, data, and tooling connect across KYC, AML, fraud, and payments – not just which tools you buy. Think of it as the connective tissue between your risk strategy and your technology investments.

A pragmatic 12-24 month roadmap:

1. Unify data and alerts across KYC, AML, and fraud into a shared risk view.
2. Introduce AI models for your highest-volume, highest-value use cases first.
3. Centralize model governance and reporting so risk leaders have a single source of truth.

This is also a talent shift. Platform engineering talent strategy in BFSI increasingly requires data engineers and fraud analysts working together – not in separate silos.

Way 2: Align Zero-Trust Security With Your Fraud and Risk Analytics Talent Strategy

According to Gartner’s Predicts 2025: Scaling Zero-Trust Technology and Resilience, 30% of organizations will abandon their zero-trust initiatives by 2028 due to complexity, lack of integration, and limited vendor value.

Zero trust increases your dependence on IAM engineers, cloud security architects, fraud analysts, and data engineers who work as a coordinated team. If your workforce plan does not reflect that, your program stalls.

A minimal “zero-trust + fraud” pod includes:

• Fraud analytics lead – owns detection models and escalation workflows.
• IAM engineer – enforces identity-first access controls.
• Cloud security architect – hardens the infrastructure layer.
• Data engineer – ensures clean, accessible data pipelines for analytics.

Not all of these need to be full-time hires. Many BFSI teams source this profile through a mix of internal talent and IT staffing companies in the USA, using contingent and project-based arrangements to stay flexible.

Way 3: Move From Rules-Based Alerts to Explainable AI Fraud Models, in Phases

Best-in-class US banks run false-positive rates in the 60s. The industry average sits in the high 90s. That gap, highlighted in McKinsey’s 2025 payments resilience report, translates directly into customer friction, operational cost, and missed fraud.

Closing that gap requires a phased AI approach – not a single platform purchase:

• Phase 1: Apply AI-assisted prioritization on top of existing rules to reduce alert noise immediately.
• Phase 2: Deploy behavioral models for specific channels – card-not-present fraud is a strong starting point.
• Phase 3: Build cross-product behavioral models with full monitoring and explainability for regulators and internal audit.

Explainability is non-negotiable. Regulators expect model documentation. Internal audit expects traceability. The data engineers and fraud analysts supporting fraud analytics in BFSI must understand both the technical and compliance dimensions.

Way 4: Measure ROI on Fraud Platforms and Teams Together

Vendor ROI case studies typically show loss avoidance and chargeback reduction. Those numbers matter – but they are incomplete if you do not include people and process costs.

A stronger exec-level KPI set:

• Loss prevented (the baseline vendor metric).
• False-positive rate (directly linked to customer churn).
• Dispute cycle time (operational efficiency indicator).
• Time-to-deploy new controls (your real measure of team agility).

The last one is often the most revealing. If your team takes three months to deploy a new fraud rule due to staffing gaps or data access issues, the platform’s projected ROI will not materialize. Candidate quality in contingent workforce programs is a direct input to this KPI – the right fraud analyst or data engineer can significantly reduce deployment time.

Way 5: Build a Sustainable 24/7 Fraud Operations and Analytics Workforce

KPMG’s 2025 Cybersecurity Survey of 310 C-suite and senior security leaders at US organizations with $1B+ revenue finds that 83% report rising cyberattacks, 99% plan to increase cybersecurity budgets, and 53% still cannot find qualified candidates. IAM is the #1 budget priority for 42% of respondents.

Budget intent is high. Talent availability is the bottleneck.

Frontline fraud operations compound this. Overworked, undertrained call-center fraud teams create coverage gaps, inconsistent decisions, and high turnover – all of which increase risk exposure.

A sustainable workforce model blends:

• Core FTE leadership: fraud program leads, model risk owners, compliance architects.
• Specialized contingent talent: fraud analysts, IAM engineers, cloud security staff for defined phases.
• Project-based teams: deployed for major platform rollouts or regulatory response.

Explore a contingent workforce strategy for IT and software teams or review Artech’s guide to future-proofing your contingent workforce to see how BFSI leaders are structuring this today.

Ready to Build the Fraud and Risk Analytics Team Your Program Needs?

Modernizing fraud detection is not a platform decision alone – it is a workforce and operating model decision. If you want to explore what this could look like for your environment, talk to our team about your current talent gaps, program timeline, and staffing model, and we will help you identify exactly where the right mix of full-time, contingent, and project-based expertise makes the biggest difference.

FAQ: What Executives Ask About Fraud Detection and Risk Analytics

How do CIOs sequence the shift from rules-based fraud detection to real-time, AI-driven analytics?
Start with AI-assisted triage on existing rules to reduce alert volume without replacing your systems overnight. Move to behavioral models for your highest-risk channels in Phase 2, then build cross-product coverage with governance in Phase 3. Each phase needs specific data engineering and fraud analytics talent to execute.

What roles and skills are essential for a modern fraud, risk analytics, and cloud security team under zero trust?
The core roles are fraud analytics lead, IAM engineer, cloud security architect, and data engineer – with a product owner connecting them. Not all need to be permanent hires. Many BFSI teams use a staffing company or IT staffing specialists to source contingent talent for specialized roles.

Which fraud detection tasks should be automated first, and which still need human review?
Automate high-volume, lower-risk alert triage and pattern matching. Keep human review for high-value transaction disputes, model tuning decisions, and regulatory escalations. The judgment layer – context, exceptions, edge cases – still requires experienced fraud analysts.

Beyond vendor case studies, what KPIs show whether a fraud detection platform is delivering value?
Track false-positive rate, time-to-deploy new controls, dispute cycle time, and customer churn alongside loss-prevented figures. These together give CFOs and CIOs a realistic picture of whether the platform and the team around it are performing.