Artificial intelligence (AI) is increasingly integrated into embedded finance, driven by advancements in generative AI (genAI), large language models (LLMs), and deep learning.

A report by international management consulting firm Arthur D. Little explores this trend, highlighting the shift from AI merely facilitating and automating processes to actively creating, innovating and shaping finance in real time.

The report outlines several existing use cases of AI across embedded finance. In risk management, algorithms are used to bolster assessments by detecting inconsistencies and identifying potential challenges. Along the customer journey, AI enhances value proposition and speeds up processes including underwriting and credit assessments, improving customer experience.

In personalization, AI dissects customer behaviors to offer services tailored to individual needs. Finally, in fraud prevention, AI analyzes vast data sets to uncover anomalies and prevent fraudulent activities in real time.

The report shares examples of fintech companies actively leveraging AI in embedded finance. In payments, for example, PayPal, Stripe, and Square uses AI to streamline payment processing and for fraud detection. Square also uses machine learning (ML) to underwrite and extend loans to small businesses overlooked by conventional banks.

Similarly, in embedded lending, companies like Affirm, Klarna, and Upstart, are using AI for risk evaluation.

In embedded wealth management, companies like Betterment, Wealthfront and Sigfig offer AI-powered robo-advisors that dispense financial advice and which manage portfolios.

In e-commerce as well as platforms like Shopify and Amazon are embracing AI. Shopify, for example, embeds AI technology from Stripe to offer individualized solutions, amplifying the sales potential for its merchants. Amazon, meanwhile, relies on AI to curate personalized loan offers anchored in sales history.

Predictions

Looking ahead, Arthur D. Little identifies several trends expected to shape the sector, forecasting that technologies including AI, LLMs, blockchain, and edge computing will eventually converge to deliver more secure, swift, and scalable embedded finance solutions.

One key development is advanced biometric authentication. Powered by AI, facial, voice, fingerprint, and iris recognition promise stronger level of security for all financial transactions in an evolving risk environment.

Another trend outlined in the report is the rise of AI-driven sentiment analysis. Using natural language processing (NLP), these systems extract opinions and emotions from social media and news to offer a predictive edge for market trends and investment strategies. Coupled with LLMs capable of formulating summaries and reports, these systems are poised to revolutionize embedded wealth and the overall wealthtech sector, Arthur D. Little predicts.

The report also anticipates growing use of AI in regulatory compliance. These systems are capable of analyzing complex regulations, pinpointing requirements, ensuring adherence, and highlighting discrepancies.

In embedded insurance, which often involves numerous policy details, LLMs can be paired with knowledge graphs to guide users through the intricate clauses, helping them better comprehend coverage details. In digital assets, AI can be used to detect patterns and anomalies on the blockchain, helping combat fraud, and ensure compliance.

LLMs can also be used to automate customer service. LLM-powered chatbots, for example, can provide more insightful, contextually aware customer support. These systems are capable of decoding complex user queries with high accuracy, and can offer real-time assistance, anticipate user needs, and suggest solutions.

Another trend outlined in the report is the rise of AI in risk management and behavioral analysis. By processing vast amounts of market data, AI can evaluate and mitigate risk exposures instantly, and issue timely alerts for emerging threats. In tandem, behavioral analysis capabilities can enhance security and user experience by identifying any deviations in typical user patterns.

Strategic collaborations

Increased integration of AI in embedded finance is expected to fuel strategic collaborations between financial giants and fintech or AI innovators, combining traditional wisdom with modern agility to elevate embedded finance solutions.

Arthur D. Little also anticipates increased acquisitions as AI adoption accelerates. Growing regulatory scrutiny will reinforce this trend as government step up efforts to establish frameworks for data integrity, algorithmic fairness, and consumer protection, potentially triggering further industry consolidation.

Embedded finance and AI have emerged as two of the hottest trends in the fintech sector. In North America, more than half of relevant independent software vendors now offer embedded payments, suggesting that adding payments to software-as-a-service (SaaS) products has become standard practice, data from Boston Consulting Group (BCG) show.

At the same time, small and medium-sized enterprises (SMEs) are increasingly expecting payment capabilities to be built into their SaaS tools. In the US, SME adoption of vertical software reached 59% in 2024, compared with 50% just two years earlier.

BCG estimates that embedded finance in North America and Europe is currently a US$32 billion market. However, the total addressable market across these two locations is about US$185 billion, highlighting significant room for growth.

AI, meanwhile, continues to attract significant investment. In Q3 2025, AI captured nearly one-quarter of fintech funding, according to market intelligence platform CB Insights. Additionally, five of the top ten deals in the space during the quarter went to AI-powered finance platforms, emphasizing investors’ bullishness in the prospect of AI in the fintech sector.

AI is also taking center stage in shaping the future of the industry. This year’s CB Insights’ Fintech 100, which spotlights the fintech firms defining the next generation of financial services, includes 11 companies specializing in AI agents, and 17 companies using AI to transform accounting, payroll, and treasury workflows, underscoring the growing dominance of AI across fintech.

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