Generative AI: Enabling a New Wave of Workflow Automation

We are seeing generative AI contribute to new opportunities within fintech, helping automate certain workflows that were previously manual and difficult to scale. In our view, its impact is already significant and is likely to grow, particularly when applied to processes that depend on interpreting and synthesizing unstructured information where language, narrative and context matter most.⁽¹⁾

We believe client engagement, as well as KYC and AML case management applications, offer a few examples of areas where generative AI is effectively applied. Within case management, teams are tasked with interpreting IDs, contracts and corporate filings, generating investigation summaries and working to resolve cases efficiently. Generative AI can help streamline these tasks by accelerating document interpretation and narrative construction. We are seeing similar gains emerging in research automation, earnings-call analysis and monitoring of regulatory and rulemaking developments, where large volumes of text need to be processed quickly and accurately.  

Within client engagement, we are seeing financial advisors apply AI to translate complex investment strategies into clearer, more digestible explanations. VC IV portfolio company Vestmark offers one example of this trend, incorporating conversational and agentic AI capabilities into its platform in an effort to help financial advisors retrieve information, explore scenarios or complete simple tasks without navigating multiple systems. We believe tools like these can meaningfully reduce friction in day-to-day workflows, while preserving the central role of the financial advisor.

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Machine Learning: Turbo-Charged by Expanding Data Volumes

Alongside classical mathematical models, we have seen quantitative machine learning emerge as a driver of innovation across fintech. Large language models focus on learning patterns in human language to generate and interpret text, while quantitative machine learning relies on explicit statistical and numerical models in an effort to make more precise predictions and decisions from structured data. As the volume of transactional and behavioral data has grown, we have seen these ML techniques become more sophisticated and more effective. Unlike rules-based systems, ML models are designed to learn from historical data to help identify patterns and anomalies using approaches such as clustering, classification and time-series analysis.

In many structured prediction tasks, including numerical modeling, regression, time-series forecasting or portfolio-level stress testing, we believe generative AI remains ill-suited. In our view, quantitative machine learning remains a more effective technology for these particular use cases and can offer higher accuracy and lower latency.⁽²⁾ We already see its impact across areas such as anti-money laundering where abnormal patterns can be detected in near real time through machine learning-driven transaction monitoring and anomaly detection systems that analyze large volumes of data and highlight suspicious behavior.⁽³⁾ We also see ML used in credit scoring and underwriting where machine learning models analyze diverse behavioral and transactional datasets to help enhance risk assessment and predict default risk, in some cases more precisely than traditional statistical methods.⁽⁴⁾

Machine learning is also helping to enable innovation in certain customer-facing financial platforms. For example, VC V portfolio company Fundraise Up’s platform is designed to apply machine learning to help personalize online donation flows by modeling donor behavior across variables such as time, location, device, language and traffic source. Trained on a large proprietary dataset of historical and real-time behavioral signals, these models are designed to optimize conversion, gift size and campaign efficiency while also supporting fraud detection and payment-risk mitigation.

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Mathematical Models: Here to Stay

Despite the attention surrounding generative AI, many aspects of fintech continue to utilize classical mathematical models. While many of these models were developed decades ago, we believe they remain foundational to modern investment theory and quantitative finance. Options pricing models such as Black–Scholes, performance frameworks like CAPM and factor-based approaches such as Fama–French are widely used in portfolio construction, risk analysis and regulatory reporting. These established mathematical models continue to play an important role in areas of fintech where accuracy and regulatory requirements are central considerations.⁽¹⁾

These mathematical models are typically both deterministic and explainable, characteristics that matter in financial services, in part because of demand for approaches that can be audited, tested and relied upon to produce consistent outcomes under defined assumptions. One area where we continue to see mathematical models effectively applied is market surveillance and risk monitoring. We believe Europe III portfolio company TradingHub illustrates this well, with a platform designed to apply quantitative market impact models to aid in deconstructing risk sensitivities, market correlations and the price impact of trading activity. TradingHub’s models help to better identify specific market-abuse scenarios and to reduce false positives. While generative tools may assist downstream, for example by aggregating signals across dashboards, translating outputs into more accessible formats or automating remediation workflows, we do not believe they are a replacement for the core analytical logic that underpins these systems.

We believe a similar opportunity for mathematical models exists within critical financial infrastructure. In our experience, effective core banking platforms, payment systems and trade execution rely on low latency, deterministic behavior and high uptime. We believe these requirements make them unlikely candidates for near-term disruption by generative AI.⁽¹⁾ 

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The AI Data Moat in Fintech

Across these three approaches – generative AI, machine learning and mathematical models – fintech companies can benefit from specialized datasets. Transaction data, behavioral signals and market information help create what we believe are deep competitive moats in an increasingly AI-driven world. This dynamic mirrors what we see across vertical SaaS more broadly where workflow depth and proprietary data can help create differentiation.

In fintech, data are not simply inputs to AI systems; they can be a key source of differentiation. The quality, structure and scale of financial datasets can often determine the effectiveness of models and ultimately the outcomes and information they produce.

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