In product management, major shifts are emerging as generative AI reshapes the risk management landscape. This transformation is impacting everything from financial services to e-commerce and autonomous vehicles. A siloed, reactive approach with a compliance-first mentality to risk management is no longer sufficient; multiple past failures can be traced back to exactly this way of operating.
In the 2016 Wells Fargo “Fake Accounts” scandal, the company’s risk management approach was designed to meet compliance and regulatory checklist. This led to a toxic sales culture that encouraged opening millions of unauthorized bank accounts. The approach of satisfying compliance without focus on ethical practices resulted in billions of dollars in fines and reputation damage.
In the eCommerce world, a reactive approach to fraud such as refund abuse, return abuse (when a customer requests and receives a refund for a purchase they claim was incomplete or unsatisfactory) or chargebacks (where a customer disputes a legitimate transaction, often after receiving the product) creates loopholes for fraudsters. Chargebacks are estimated to cost eCommerce ~$34B in 2025.
Risk management can become a strategic business driver and not a blocker, by applying AI product principles that prioritize user-centricity and metrics-driven analytics. A good example is the transformation of a sensitive customer touchpoint like First Notice of Loss (FNOL) in an insurance claims workflow. FNOL typically involves collecting policyholder information, accident details, coverage data, and photos or videos.
Traditionally, this required complex app flows with multiple data-entry steps or lengthy calls with human agents. Now, agentic AI is increasingly handling this critical touchpoint. By streamlining the customer journey with AI, insurance carriers are making the FNOL experience faster, more intuitive, and significantly more efficient.
The Limitations of Traditional Risk Management
There are three recurring problems often seen in traditional risk management in sectors such as eCommerce and financial services.
Reactive “after the fact” approach
Traditional models tend to respond to problems only after they’ve occurred. Delays in detecting fraudulent activity can significantly damage a company’s bottom line and erode customer trust.
Consider an insurance company that uses historical data to set homeowner policy premiums. When an unprecedented disaster occurs such as large-scale wildfires the volume and severity of claims can fall far outside the model’s predictions. A reactive approach to modeling new, evolving risks like climate change leads to major financial losses for the company and sharp premium increases for customers.
Operational Silos
Often risk management teams are isolated from the product, engineering, and operations teams. This disconnect leads to product launches that go forward without a comprehensive risk assessment, creating unexpected vulnerabilities. A siloed operating environment in a company will lead to significant safety problems, reputational damage and eroded customer trust.
For example, in self-driving vehicles a product manager designs an in-car UI experience for emergency maneuvering with design, engineering and hardware teams. The UI experience lacks accessibility features that were missed since safety and compliance teams operated separately. A real world incident occurs and the new UI experience for emergency maneuver fails due to missed accessibility features. This failure highlights safety breakdown leading to potential injury/loss of life and financial loss.
Compliance-First Mentality
When the only focus is on checking compliance and regulatory rules, risk management can miss the bigger strategic picture. There are situations where this narrow focus allowed critical risks to evolve into serious product vulnerabilities or new fraud behavior. In a claims workflow, meeting the bare minimum for customer communication and resolution protocols can lead to customer dissatisfaction.
In eCommerce, performing basic KYC (Know your customer) or KYB (Know your business) verifications for onboarding customers and sellers to the marketplace makes the platform vulnerable to fraud activities. Meeting only the compliance requirement while not really knowing the onboarded user leads to platform revenue loss and negative user experience.
AI-driven Product Management principles
Two core AI-driven principles that are reshaping risk management: user-centric risk assessment and metrics-driven decision-making. These principles are emerging from real-world experience building risk management products across multiple industries.
User-Centric Risk Assessment
Effective risk management starts with a deep understanding of the user journey. Mapping detailed user journeys to identify risks whether the user is an external customer or an internal stakeholder is highly effective.
Comprehensive user profiles for behavior prediction and anomaly detection can be developed by using AI models (such as unsupervised learning) to analyze interaction data, device data, network data, and more. For example, in e-commerce, applying this kind of holistic user profiling has been used to proactively identify fraudulent activities (such as pirated content and transaction abuse), while also deepening understanding of how risk manifests and impacts the business.
Metrics-Driven Decision-Making
Teams can move toward a mindset of preemptive control by implementing ML models that proactively address fraud and strategic risks. This involves defining forward-looking metrics such as real-time risk scoring and risk likelihood. AI models that leverage graph analytics can evaluate hundreds of risk signals to produce a dynamic risk score for every user interaction. These metrics enable risk teams to anticipate, and prioritize issues far more effectively.
A metrics-driven approach is especially powerful since accurate predictions directly contribute to improved operational efficiency and a stronger competitive edge. For example, integrating telematics data into behavior-based insurance models has led to more accurate risk assessments and personalized pricing for customers. The shift from reactive to proactive risk management is proving to be one of the most impactful changes in the industry.
Conclusion
By integrating AI product management principles into risk management organizations to become more agile, innovative, and strategically competitive. Organizations should swiftly move away from a reactive, compliance-driven and siloed risk function to a proactive, user-centric one. Adoption of these best practices can significantly improve the organization’s resilience and growth in today’s AI-powered world.
