The Convergence of Open Finance and AI -Part I: Transforming Banking in the Agentic Era
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The Convergence of Open Finance and AI -Part I: Transforming Banking in the Agentic Era

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1.    Introduction

The financial industry stands at a historic inflection point where two powerful forces are converging: the data-sharing ecosystem of Open Finance and the intelligent capabilities of AI technologies. This convergence is creating unprecedented opportunities for financial innovation, personalization, and operational efficiency.


As Open Finance frameworks mature globally through regulations and standard setting bodies like the Financial Data Exchange who became the open banking API standard in 2025 and has today over 100 million connected consumer accounts in the US, AI has emerged as the critical catalyst that transforms raw financial data into actionable intelligence and automated processes.


The most exciting development in this space is the emergence of agentic AI, autonomous systems capable of making decisions and taking actions on behalf of consumers. Global payment leaders Visa, Mastercard, and PayPal have recognized this shift, recently unveiling initiatives to integrate payment capabilities directly into AI agents. This marks the dawn of "agentic commerce," a paradigm shift poised to follow the evolutionary waves of eCommerce and mobile commerce.


“As Open Finance and Generative AI converge, the future of finance is both intelligent and autonomous. We are entering an era where financial services are driven by real-time insights, hyper-personalization, and intelligent agents that will serve customers and financial institutions alike. This means better decisions, faster services, and more personalized experiences. The added layer of agentic AI, autonomous systems capable of perceiving their environment, making decisions, and taking actions without human intervention, is an exciting development in finance, but one that must be approached with responsible innovation in mind. ” – Ellis Odynn, Technology Strategy & Transformation Consulting | AI & Open Banking Strategist, EY

For financial institutions and fintechs navigating this rapidly evolving landscape, understanding the interplay between Open Finance data access, AI-powered analysis, and autonomous agent capabilities is no longer optional; it's essential for competitive survival. This article explores how these technologies complement each other, examines real-world implementations, and offers strategic guidance for organizations seeking to harness their combined potential.


2.  The Foundation: Open Finance Meets AI

Open Finance represents the evolution of Open Banking, extending beyond traditional banking to incorporate insurance, pensions, and investments into an interconnected financial ecosystem. At its core, Open Finance grants consumers the right to consent to sharing their financial data with third-party providers, creating a foundation for innovation and competition.


The financial landscape is undergoing a profound transformation, driven by Open Finance enabling seamless data sharing across institutions, while AI leverages this data to deliver intelligent automation, personalized insights, and enhanced security.


However, data alone isn't enough. This is where AI becomes the essential bridge, making sense of vast financial datasets, automating processes, and delivering real-time insights that enhance decision-making. As Mastercard's Executive Vice President of AI, Rohit Chauhan, aptly noted:

"The use of AI is about future-proofing Mastercard. If it's the new electricity, we want electricity to be flowing through every division within Mastercard.[1]"

The synergy is powerful: Open Finance provides standardized access to rich, cross-institutional financial data, while AI provides the intelligence to analyze this data at scale, identify patterns, automate decisions, and create personalized experiences.


3.  Key Application Areas Where AI Enhances Open Finance

3.1 Personalized Financial Experiences

Traditional banking services often fail to deliver truly tailored financial advice. The combination of AI and Open Finance data enables truly personalized financial experiences based on comprehensive analysis of consumer behavior across multiple accounts and institutions.


Research from Experian shows the growing consumer demand for these AI-powered experiences. According to their findings[2],


  • 63% of consumers are familiar with generative AI, with 84% of Gen Zers and 79% of millennials reporting familiarity.

  • Nearly half (47%) of consumers are using or considering AI-powered tools to manage their personal finances, with this trend particularly strong among younger consumers (67% of Gen Zers and 62% of millennials).

  • When asked about the most helpful areas for AI in their financial lives, consumers pointed to saving and budgeting (60%), investment planning (48%), and credit score improvement (48%).


Examples:

  • Cleo (UK fintech) – AI budgeting chatbot: Cleo is a popular personal finance app that connects to users’ bank accounts via open banking APIs and uses AI (including an NLP-powered chatbot) to deliver personalized budgeting advice and insights. Its machine learning algorithms analyze a user’s transactions and spending habits to provide real-time financial tips, nudges, and alerts[3]. Cleo’s conversational interface (e.g. on Facebook Messenger) turns finance into a friendly chat, helping mostly younger users manage money. The app’s AI-driven approach has attracted over 7 million users as of 2023[4], indicating strong engagement from hyper-personalized advice.


  • Royal Bank of Canada – NOMI (Canada) – Personalized savings and forecasts: RBC’s NOMI is an AI-powered digital banking assistant that leverages predictive analytics on customer account data to personalize the banking experience. NOMI analyzes monthly cash flows and transaction patterns to auto-categorize spending and even automatically set aside small amounts for savings (“NOMI Find & Save”). This AI-driven tool opened 250,000+ new savings accounts for RBC customers, with users saving an average of $225 per month without manual effort[5]. By proactively forecasting bills and recommending budgets, NOMI boosts customer engagement and has become a differentiator in RBC’s mobile banking app.


  • Personetics AI Platform (Global) – Hyper-personalization for banks: Personetics is a fintech whose AI-driven personalization engine is used by over 80 banks globally (e.g. U.S. Bank, Metro Bank, Santander). It analyzes bank and open finance data in real time to generate personalized insights, financial advice, and automated savings programs within banks’ digital channels[6]. For example, Central Bank (USA) implemented Personetics to offer a unified money management dashboard with insights and “smart saving” programs that aggregate all internal and external accounts. Personetics’ machine learning models anticipate customer needs and deliver guidance that rivals big-bank capabilities. Banks using Personetics have seen up to 35% increases in digital engagement and 20% growth in account balances due to these AI-personalized services[7].


  • Tink by Visa (Europe) – Predictive insights via open banking: Tink is a leading European open banking platform (6,000+ bank connections) that embeds AI and predictive analytics into its data aggregation services[8]It uses machine learning to analyze aggregated account data and deliver personalized insights – for instance, predicting users’ cash flow for budgeting or providing credit score recommendations based on spending behavior. Banks and fintechs using Tink’s AI-powered APIs have reduced the time required for credit risk assessments by 40% (thanks to instant analysis of banking data) and improved personal finance recommendations to customers.


3.2 Smarter Lending & Credit Decisions

Traditional credit scoring relies on limited historical data, often excluding individuals with thin credit files. AI, combined with Open Finance data, enables more inclusive and accurate lending decisions by analyzing alternative financial data.


Examples:

  • Credit Kudos (UK) – Open banking–based credit scoring: Credit Kudos (recently acquired by Apple) is a fintech that uses open banking data and machine learning to predict loan repayment risk[9]. Its flagship product “Signal” analyzes a borrower’s bank transaction data (with consent) and an AI model trained on 6+ years of outcomes to produce an alternative credit score. This AI-driven score enables lenders to approve applicants who might be declined by traditional credit bureaus. In trials, lenders using Signal were able to approve 30% more applicants who had thin or no credit files without increasing default rates, and even reduce overall default rates from 11.7% to 9.7% by better assessing risk. The model is also explainable, highlighting key features in a customer’s financial behavior to satisfy regulators.


  • NestEgg and Credit Unions (UK) – Faster lending decisions with open data: Central Liverpool Credit Union partnered with fintech NestEgg.ai and open banking API provider TrueLayer to streamline its loan underwriting. Instead of relying only on credit bureau scores, they now pull real-time transaction data from applicants’ bank accounts (with consent) and feed it into NestEgg’s AI-driven affordability model[10]. This has enabled the credit union to make instant, more accurate lending decisions – often approving the full loan amount requested, rather than half as before. In fact, Central Liverpool CU was able to safely deploy an additional £700,000 in credit to members that meet its risk criteria, thanks to the richer data and AI analysis of borrowers’ cashflows. Similarly, Police Credit Union in the UK uses Credit Kudos’s open banking insights to better evaluate loan applicants with thin credit files[11].

 

  • Kabbage (USA) – Real-time SMB lending: Kabbage (fintech lender, now part of American Express) pioneered AI-based small business lending using live business data. It uses open finance data integrations (e.g. connecting to business bank accounts via APIs) and machine learning models to instantly underwrite lines of credit. Rather than rely solely on credit reports, Kabbage’s platform analyzes real-time bank account flows, sales, and other alternative data to determine credit risk within minutes[12]. The use of AI on open banking data not only speeds up lending (often approving loans in under 10 minutes) but also expands credit access to businesses that might be overlooked by traditional lenders.


  • Accelerated loan processing: Many mainstream banks are also leveraging open banking data with AI to modernize lending. By automatically retrieving applicants’ account and income data via APIs, AI-driven underwriting systems can reduce manual paperwork and loan approval times significantly. For instance, Tink reports that its partner banks have cut credit assessment time by 40% using open banking data fed into AI credit models[13]. The result is faster credit decisions and an ability to serve consumers who previously lacked sufficient credit history, improving financial inclusion.


3.3 Enhanced Fraud Detection & Security

As Open Finance expands access to financial data, security concerns become paramount. AI plays a crucial role in detecting and preventing fraudulent transactions in real-time.


Examples:

  • A pioneering pilot by Synectics Solutions and Pay.UK demonstrated how combining syndicated data intelligence and machine learning can dramatically reduce authorized push payment (APP) fraud in the UK. The AI-driven solution analyzed data from 30 participating financial institutions to detect cross-bank fraud patterns that are often invisible to individual banks. The pilot revealed that if implemented across the UK banking sector, the system could help recover or prevent over £100 million in fraud losses annually.[14] This marks a significant step forward in collaborative, AI-powered fraud prevention using open finance data-sharing frameworks.


  • Danske Bank (Denmark) – Deep learning for payment fraud: Danske Bank deployed a deep learning–based fraud detection system (with partner Teradata) to monitor transactions for fraud in the PSD2 open banking era. Its previous rules-based system caught only ~40% of fraud attempts and inundated staff with 1,200 false-positive alerts per day[15]. After implementing the AI model, Danske achieved a 50% boost in fraud detection and cut false positives by 60%, dramatically reducing wasted investigator time. The AI system uses neural networks to learn normal vs. suspicious transaction patterns (including cross-channel behavior) and flags fraud in real time, with a “champion/challenger” model approach to continuously improve accuracy.

 

4. Conclusion

“I think ten years from now, you could get a completely AI-powered bank—a platform where each individual customer actually creates their own AI version of the bank. So, the sky is the limit in terms of where you could go with this.”[16] Piyush Gupta, CEO of DBS

Open Finance and AI are a perfect combination—one provides access to financial data, while the other transforms that data into intelligent, real-time financial solutions. From personalized banking to fraud prevention and smarter lending, AI is unlocking the full potential of Open Finance.


As financial institutions continue to embrace this transformation, those who integrate AI-powered Open Finance solutions early will gain a competitive advantage. The future of finance isn’t just open—it’s intelligent.


Part II will explore the The Rise of Agentic AI and commerce: Beyond Assistance to Autonomy.


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