Clarity Connect

Artificial Intelligence (AI) has long been an integral part of digital transformation in the financial sector—primarily through fraud detection, automated customer service, and process automation. However, the evolution of generative AI (GenAI) is significantly broadening the range and depth of possible applications. Recent research suggests that 32–39% of tasks across banking, capital markets, and insurance have high potential for full automation. AI is now embedded in both back-office operations and customer-facing roles, enabling institutions to serve clients faster, more efficiently, and with greater personalization. Common use cases include:

• Process Automation and Optimization: End-to-end workflow redesigns powered by AI capabilities in text, image, and voice processing—for example, automated onboarding, claims processing, and credit assessments.
• Productivity Enhancements: AI agents and virtual assistants are supporting bankers, sales teams, and customer service agents with contextual information and task automation.
• Investment Management: AI tools are being used to build portfolios, analyze market trends, and deliver personalized, real-time investment advice.
• Risk and Fraud Management: Proactive fraud detection systems analyze transactions and behavioral patterns in real-time to flag anomalies before they result in losses.

Banco Bilbao Vizcaya Argentaria (BBVA) showcases how financial institutions are using AI to drive digital adoption and customer acquisition. Using facial recognition and text analytics, BBVA enables mobile onboarding and same-day funding. Their AI-enabled marketing automation (search engine optimization, marketing automation, analytics, and content production) and hyper-personalized digital strategy led to a 150% growth in new clients, with 65% of the 11.1 million customers onboarded in 2023 via digital channels. Seven out of ten BBVA sales now occur digitally, contributing to a reduced cost-to-income ratio.

Expanding AI Applications Across Financial Sub-Sectors

While mainstream applications of AI in financial institutions are well documented, less attention is paid to how AI is transforming adjacent sectors and actors in the financial ecosystem – MSME finance, agriculture finance, supply chain finance and also broader market supervision. These emerging use cases are covered in more detail below.

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AI in Agricultural Finance

• Tailored Credit Products: AI can integrate diverse data sources—market access, weather patterns, disease incidence, and farming practices—to structure loans aligned with production cycles and climate risk.
• AgTech and MFI Collaborations: In Morocco, SOWIT provides farm-level insights while alAmana, backed by IFC, offers credit risk guarantees to farmers. Participating farmers have seen a 27% increase in yields and nearly doubled incomes.
• Decision Support Tools: AI-powered tools help lenders and credit officers translate agronomic and climate data into actionable credit decisions, requiring upskilling of frontline staff in data interpretation.

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AI in Supply Chain Finance

• Instant Credit Scoring for SMEs: Traditional credit assessments often include lengthy processes for SMEs which struggle to provide audited financials, collateral, business plans, and other documents needed to assess risks by traditional methods. AI-based scoring models using transaction data, shipment timestamps, and partner credit profiles help quicken assessment and pre-approve limits.
• Automated AR/AP Management: Suppliers use AI to track receivables and payables, optimize inventory, flag overdue clients, and negotiate payment terms—enhancing working capital management and cash flow stability.         

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AI in Private Credit and Long-Term Lending

As private credit markets grow, AI is reshaping credit evaluation and portfolio management:

• Early Warning Systems: AI tools monitor corporate portfolios, flag emerging risks, and aggregate data from multiple sources to inform timely decisions.
• Automated Credit Memos: By integrating proprietary and external datasets, AI builds dynamic borrower profiles—streamlining due diligence, risk scoring, and ESG evaluations.

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AI in Policy and Supervision

Central banks and regulators are leveraging AI not just for internal efficiency, but to enhance oversight:

• Netherlands – ChatDNB: An AI chatbot interprets regulations and offers guidance on supervisory expectations, improving compliance and regulatory clarity.
• Namibia – Risk Monitoring: Automated supervision tools support real-time analysis across asset classes, improving cost-effective oversight. The automation enables analyzing risks at a bank level or across a specific asset class, and also comparing the financial health of a group of banks.
• New Zealand – Climate Stress Testing: The Reserve Bank of New Zealand (RBNZ) is using AI to simulate the impact of floods and droughts on banks’ portfolios, incorporating physical and legal climate risks into national stress-testing frameworks. With the agricultural sector representing 11% of all bank lending in New Zealand and increasing flood related risk for mortgage lending, banks have also incorporated climate-related risks into the stress test.

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Recommendations for AI enabled Inclusion and Innovation

AI is not just a back-office enabler or chatbot technology, it is emerging as an engine of innovation. To unlock its full potential, greater investment is needed in cross-sector partnerships, data infrastructure, and capacity-building—especially in emerging markets. Financial institutions, regulators, and ecosystem enablers can take targeted action to enable:

Inclusion focused design: Prioritize AI use cases that expand access to underserved segments, such as MSMEs, smallholder farmers, and informal workers. This requires investment in alternative data infrastructure and embedding AI in last-mile delivery channels.

Capacity building: Strengthen human-AI interfaces by training frontline staff including credit officers, sales staff and supervisors, to interpret and act on AI-generated insights.

Cross-sector Partnerships: Build integrated data ecosystems and encourage open data collaborations across public and private sectors to power more accurate, real-time decision-making in credit, insurance, and policy. Regulatory sandboxes and innovation hubs that enable experimentation, especially in areas like ESG lending, climate risk modelling, to design tailored solutions that balance performance, transparency, and ethics.