Researchers have outlined four innovative business models designed for the age of agentic artificial intelligence:

1. Existing+: This model enhances traditional business operations with AI. For instance, a financial services firm could utilize AI to analyze customer data, thereby providing personalized financial advice.

2. Customer Proxy: Companies use AI to accomplish customer goals through preset processes. For example, a financial institution might automate investment management by setting specific parameters for AI to follow.

3. Modular Creator: This approach allows firms to harness AI in assembling customizable service bundles from reusable modules. A financial services company can apply this by integrating investment, insurance, and credit services tailored to an individual's aspirations.

4. Orchestrator: In this model, firms employ AI to build a cohesive ecosystem of complementary services and products. One application would be a financial institution offering a comprehensive wealth management solution that dynamically optimizes investment portfolios.

The benefits created by these models include increased efficiency, enhanced personalization, and the ability to adapt rapidly to customer needs. One New Zealand Group exemplifies these advancements. Currently, they leverage AI agents for tasks such as responding to customer inquiries and managing service upgrades (Existing+) and utilizing data to forecast demand during service disruptions (Modular Creator). Looking forward, they plan to implement AI for marketing, enabling automated, responsive campaigns to meet customer preferences (Orchestrator).

Empowering enterprises to pivot effectively relies on understanding how AI can help streamline operations and represent customer objectives through autonomous actions. Leaders must identify existing AI-driven business models that can be expanded and the associated capabilities their organizations need to thrive in this evolving landscape.

Sentiment analysis has become essential for modern businesses, offering valuable insights into customer sentiments, satisfaction, and frustrations. Given that most interactions occur through text—like social media or e-commerce reviews—or voice, organizations are leveraging AI to interpret these signals effectively. By accurately gauging customer emotions, companies can enhance user experiences, boosting satisfaction and loyalty.

However, implementing sentiment analysis is fraught with challenges, including language ambiguity, cultural nuances, and the complexities of high-volume real-time data. AWS has stepped up to these hurdles with a comprehensive suite of tools, including Amazon Transcribe for audio capture, Amazon Comprehend for text sentiment analysis, and real-time data streaming via Amazon Kinesis. These services empower businesses to analyze customer sentiment across various platforms seamlessly.

Through innovative partnerships, such as with the Instituto de Ciência e Tecnologia Itaú, AWS has showcased various machine learning models, emphasizing sentiment classification for both text and audio. Their experiments reveal that while traditional text processing offers some insights, incorporating direct audio analysis can capture emotional nuances that text alone may miss. For instance, analyzing audio input directly using models like HuBERT or Wav2Vec has demonstrated higher performance in identifying sentiment than relying on transcribed text.

By utilizing AWS services, companies can effectively build and scale sentiment analysis pipelines. This workflow may involve Kinesis for stream processing, Amazon Comprehend for sentiment classification, and SageMaker for managing model deployment. The potential future developments, such as incorporating multimodal inputs and advanced prompt engineering, position organizations to glean even deeper insights from customer interactions, ultimately leading to more responsive and empathetic engagement strategies.

The financial services sector faces the pressing need to transition from AI pilot programs to full-scale deployments that genuinely enhance customer value. The key challenge lies in seamlessly integrating AI into operational workflows, particularly where high-volume decisions intersect with customer interactions. The effectiveness of AI in this context relies heavily on robust execution architecture.

This session will explore the essential infrastructure, organizational collaboration, and workflow integration necessary for scaling AI from testing phases to production. Utilizing real-world banking examples, attendees will see how leading institutions effectively embed AI across various functions such as product development, marketing, and customer engagement, transforming AI into connected intelligence rather than mere isolated experiments.

Experts from Nomis will provide a practical workflow demonstration, showcasing how AI can be effectively integrated into existing processes with appropriate feedback mechanisms.

This webinar will equip banking executives with valuable insights, including:

• A diagnostic framework to pinpoint areas where AI can yield the highest ROI within their institutions.
• The vital link between analytics teams and market execution that facilitates sustainable value creation.
• A concrete workflow architecture illustrating AI’s integration across customer insights, product strategy, and execution.
• Infrastructure prerequisites that support ongoing improvement beyond initial deployment.
• Actionable steps for advancing AI initiatives from pilot stages to full production.

Presenters will include Greg Demas (CEO of Nomis), Dallas Wells (CPO of Nomis), and Wes West (Chief Analytics Officer of Nomis).

The Financial Brand serves as a leading source for in-depth insights in the financial services sector, offering articles, webinars, reports, and research to keep banking executives informed about transformative trends and growth strategies in the industry.

The Year in FinTech: Key Developments from November 2025

November 2025 saw significant advancements in the FinTech sector, highlighted by Lloyds Banking Group's deployment of a groundbreaking AI framework and its acquisition of Curve, among other notable updates.

Lloyds is set to launch an agentic AI financial assistant for its digital banking platform in early 2026, catering to over 21 million customer accounts. This represents a significant leap in automating financial guidance on spending, savings, and investments. Using a proprietary Generative AI and Agentic framework, the system will facilitate autonomous interactions by processing natural language queries and executing tasks like transaction analysis and financial planning without needing structured input. This technology enhances user experience by providing personalized financial advice efficiently.

In another strategic move, Lloyds has acquired Curve, a digital wallet aimed at becoming a comprehensive financial operating system. This acquisition, valued at approximately £120 million, is expected to fast-track Curve's digital innovations, benefiting millions of Lloyds customers by integrating advanced financial management features.

Revolut has introduced a 1:1 conversion rate for stablecoin transfers, allowing users to seamlessly convert $1 into 1 USDC or USDT directly within the app. This initiative alleviates concerns about transitioning funds from cryptocurrency to fiat, promoting a smoother user experience in the evolving digital finance landscape.

Additionally, Chase Bank UK has partnered with Transport for London (TfL) as the 'Official Payment Partner,' enabling Chase customers to earn up to 1% cashback on their transport payments through contactless readers. This collaboration underlines the increasing integration of banking and everyday transactions.

Finally, the UK government has reduced the annual tax-free limit for cash Individual Savings Accounts (ISAs), a move aiming to direct household savings toward equity markets, thereby stimulating investment activity. Enhanced demand for stocks is anticipated, likely positively impacting investment platforms as well.