AI Transforms Risk from Blocker to Growth Engine
AI is reframing risk management from a cost center to a strategic growth enabler, according to industry experts. In a recent talk, Jill Sheckman argued, "AI is not just about reducing default rates—it’s about identifying new business segments... that traditional risk models would have excluded." This shift allows firms to use AI for dynamic risk scoring and rapid experimentation with new credit products.
The application of AI in credit underwriting is moving beyond simple automation to incorporate alternative data sources like utility payments and rental history, creating more comprehensive credit profiles. This is particularly beneficial for assessing applicants with limited credit histories, a segment often excluded by traditional models. The result is a reduction in first-payment default rates and a significant increase in the accuracy of risk prediction. Generative AI is poised to revolutionize risk management within the next three to five years by automating the synthesis of risk reports and extracting insights from unstructured data. Financial institutions are developing "virtual experts" that can analyze market news, transaction data, and even climate risk assessments to inform decisions in real-time. This shift allows risk professionals to focus more on strategic advising and proactive risk prevention. On the payments infrastructure front, the FedNow® Service is showing significant growth, processing an average of $540 million per day in the first quarter of 2025, with over 1,300 financial institutions now participating. While transaction volume is increasing, it still trails The Clearing House's RTP® network, which averages over 1 million payments daily and saw a 94% jump in payment value in 2024 to $246 billion. Digital identity verification is becoming a core component of financial infrastructure, moving beyond simple onboarding to continuous, real-time risk analysis. Innovations in biometrics, liveness detection, and behavioral risk analysis are being integrated to combat sophisticated fraud like synthetic identities and deepfakes. This multi-layered approach is improving onboarding completion rates while reducing fraud. For product leaders, influencing without direct authority requires building a strong foundation of data-driven insights and fostering a culture of shared understanding around strategic goals. This involves clearly articulating the "why" behind product decisions and aligning cross-functional teams around a common vision, a crucial skill when navigating the complex stakeholder environments of large enterprises like global payment networks. Institutional adoption of stablecoins for cross-border payments is gaining traction as a method to reduce costs and settlement times. Research suggests that blockchain-based cross-border payments can significantly cut transaction fees compared to traditional methods. Major payment players are increasingly using stablecoins to streamline international transactions, with projections showing stablecoins could capture a multi-trillion-dollar segment of the payments market by 2030. Regulatory bodies are intensifying their focus on AI in financial services, emphasizing the need for transparency and robust model governance to mitigate risks such as algorithmic bias and market correlations. This has led to a greater need for "explainable AI," where the rationale behind automated decisions can be clearly audited and understood, ensuring compliance and maintaining customer trust.
