세계 대출용 생성형 AI 시장 규모는 여러 요인으로 인해 2024년 25억 8,000만 달러에서 2032년 82억 7,000만 달러에 달할 것으로 예상되며, 2025-2032년 예측 기간 동안 15.67%의 CAGR로 성장할 것으로 예상됩니다. 주요 촉진요인 중 하나는 현금 흐름 패턴, 공공 요금 결제, 행동 지표와 같은 대체 데이터 소스를 스캔하는 정교한 위험 평가 모델을 통해 신용 의사결정을 개선하고 기존 점수화 방법보다 더 나은 예측을 달성할 수 있는 기술의 능력입니다. 이러한 혁신은 금융 포용의 맥락에서 특별한 가치를 지니며, 금융기관은 포트폴리오의 질을 떨어뜨리지 않으면서도 기존에 서비스를 받지 못했던 대출자에게도 서비스를 제공할 수 있게 됩니다. 두 번째 주요 요인은 대출 프로세스의 자동화로, AI는 신청서 처리, 심사 및 부정행위 탐지를 최적화하여 승인 시간을 며칠에서 몇 분으로 단축하고 영업 비용을 절감할 수 있습니다.
새로운 가이드라인은 설명 가능한 AI와 알고리즘의 투명성을 요구하고 있으며, 금융기관은 규제에 부합하는 선진화된 시스템에 투자해야 합니다. 맞춤형 금융 상품에 대한 소비자의 선호도 또한 AI를 통해 대출의 동적 가격 책정 및 상환 조건의 맞춤화를 가능하게 하는 맞춤형 금융상품의 도입을 촉진하고 있습니다. 그러나 데이터 프라이버시 문제, 구현 비용, 숙련된 AI 개발자 부족 등의 위협이 일시적으로 도입 속도를 늦출 것으로 보입니다. 기존 은행, 핀테크 스타트업, 주요 하이테크 기업들이 앞다퉈 이러한 솔루션을 출시하면서 경쟁 환경은 과열되고 있으며, 기술이 성숙하고 ROI가 명확해짐에 따라 더욱 폭발적인 성장이 예상됩니다.
예를 들어, 2025년 4월 LendingClub Corporation은 AI 기반 지출 인텔리전스 플랫폼인 Cushion의 지적 재산과 엄선된 인재를 인수한다고 발표했습니다. 쿠션의 AI를 활용한 기술은 사용자의 은행 거래 및 구매 정보를 수집해 인보이스 추적, 정산 기한 준수, 구독 관리, 신용 구축, BNPL(Buy Now, Pay Later) 대출 모니터링 등을 지원합니다.
세계의 대출용 생성형 AI 시장에 대해 조사 분석했으며, 시장 규모와 예측, 시장 역학, 주요 기업 개요 등의 정보를 전해드립니다.
Global generative AI in lending market is projected to witness a CAGR of 15.67% during the forecast period 2025-2032, growing from USD 2.58 billion in 2024 to USD 8.27 billion in 2032F, owing to undergoing revolutionary expansion, driven by some key factors. One of the prime movers is the technology's capacity to improve credit decision-making via sophisticated risk evaluation models that scan alternative data sources such as cash flow patterns, utility payments, and even behavioral metrics, resulting in better forecasts compared to conventional scoring techniques. This innovation is of special value in the context of financial inclusion, enabling lenders to reach previously unserved borrowers without compromising portfolio quality. A second major contributor is lending process automation, where AI optimizes application processing, underwriting, and fraud detection, shortening approval times from days to minutes and lowering operating expenses.
The market is also driven by regulatory change, as emerging guidelines require explainable AI and algorithmic transparency, compelling lenders to invest in more advanced and compliant systems. Consumer preference for bespoke financial products is also driving adoption, with generative AI allowing dynamic pricing of loans and customized repayment terms. Yet threats such as data privacy issues, costs of implementation, and a lack of skilled AI developers to develop them will slow down the rate of adoption temporarily. The competitive environment is heating up as the old banks, fintech startups, and major tech companies all scramble to deploy these solutions, portending further explosive growth as the tech matures and shows more defined ROI.
For instance, in April 2025, LendingClub Corporation announced the acquisition of intellectual property and select talent behind Cushion, an AI-powered spending intelligence platform, providing a natural complement to LendingClub's suite of mobile financial products and experiences. Cushion's AI-powered technology ingests users' bank transactions and purchase information to help them track their bills, make on-time payments, manage subscriptions, build credit, and monitor buy now, pay later (BNPL) loans.
AI-Powered Risk Assessment and Fraud Detection
Generative AI is transforming lending by improving credit risk models above and beyond the conventional FICO scores (Fair Isaac Corporation Score). In contrast to rule-based systems, AI scans different data (such as cash flow patterns, rental payment history, and even social media indicators) to forecast borrower reliability more accurately. This change is beneficial for thin-file borrowers (those with sparse credit histories), allowing for financial inclusion while lowering default risks.
Further, fraud detection has been enhanced with AI capability to mimic synthetic patterns of fraud to enable lenders to detect suspicious applications before approval. For instance, generative models can generate adversarial attack simulations to subject loan systems to test against advancing fraud strategies.
For instance, in February 2025, ZestFinance Inc. (Zest AI), a provider of AI-based lending technology, made its AI-automated underwriting and fraud detection natively integrated into the Temenos Loan Origination solution. This integrated solution equips traditional lending institutions in the U.S. with advanced capabilities to enhance loan approvals while preserving high-quality risk management in a highly competitive environment.
The partnership brings with it two important benefits, Zest AI technology can view thousands of data points much more than classic credit models allowing for more efficient and accurate lending decisions. Also, the Zest Protect system detects fake applications in real time without interrupting customer experience, so institutions can tailor security levels to suit their risk appetite.
Regulatory Push for Ethical AI in Finance
As AI adoption grows, regulators are tightening oversight to prevent algorithmic bias and ensure fairness in lending decisions. The EU AI Act (2024) classifies AI-driven credit scoring as "high-risk," requiring lenders to provide transparent decision-making processes. Similarly, the U.S. Consumer Financial Protection Bureau (CFPB) has issued guidelines mandating explainable AI (XAI) in loan approvals. This regulatory pressure is accelerating demand for AI audit tools that ensure compliance with fair lending laws (e.g., the Equal Credit Opportunity Act). Companies are now investing in bias-detection algorithms and synthetic data generation to train models without historical discrimination risks.
According to a report by PYMNTS.com LLC, 72% of finance leaders report actively using AI in their operations, with its applications ranging from fraud detection (64%) to customer onboarding automation (42%).
Hyper-Personalized Loan Pricing and Dynamic Offerings
Generative AI facilitates real-time loan product customization based on understanding borrower behavior, macroeconomic forces, and even geopolitical factors that could affect repayment capability. In contrast to fixed pricing models, AI-based systems dynamically change interest rates, tenures of loans, and repayment terms. For example, a cash-flow variable income freelancer can be offered a cash-flow cycle-based flexible repayment schedule, while a salaried individual borrower may be offered a reduced APR in view of stable employment statistics. AI also assists lenders in forecasting prepayment risk and maximizing profitability.
For instance, in April 2025, Lake Trust Credit Union, a leading credit union serving 200,000 members and businesses throughout Michigan with over USD 2.5 billion in assets, announced its partnership with Upstart, the leading artificial intelligence (AI) lending marketplace, to offer personal loans to more consumers.
North America Leads Global Generative AI in Lending Market
North America, particularly the United States, has emerged as the global leader in the adoption and innovation of generative AI in lending. This dominance is fueled by a combination of strong venture capital investments, progressive regulatory frameworks, and advanced digital banking infrastructure. The region's fintech ecosystem has seen over USD 12 billion invested in AI-driven lending startups in 2023 alone, with major players like Upstart, LendingClub, and Zest AI securing significant funding to scale their AI underwriting models. Additionally, U.S. regulators have taken a proactive stance by introducing sandbox environments that allow fintech firms to test AI solutions in a controlled setting, accelerating innovation while ensuring compliance with fair lending laws.
For instance, in February 2025, ZestFinance Inc. (Zest AI) announced the launch of LuLu Pulse, the first module of Zest AI's Lending Intelligence Platform powered by generative AI that is now available for all credit unions. By integrating industry public data and institution-specific data for customization, LuLu Pulse serves as a centralized intelligence hub that consolidates multiple data sources into a single, authoritative platform. Credit unions can access intelligence to enhance their lending practices and credit risk management to make better lending decisions.
Impact of the U.S. Tariffs on Global Generative AI in the Lending Market
Most AI lending models are trained on NVIDIA GPUs. With tariffs increasing chip prices, fintech companies might find higher operational costs, and AI model development would take a slower pace. Large lenders leveraging AWS, Azure, or Google Cloud AI solutions might experience increased prices if cloud vendors pass the cost of tariffs to users.
Large banks (e.g., JPMorgan, Goldman Sachs) have the capacity to absorb tariff expenses, but small fintechs and startups can put AI implementation on hold based on budget. If tariffs increase AI infrastructure costs, companies will reduce experimental AI lending models, slowing developments such as real-time credit scoring.
Most AI chips are produced in China. Tariffs might break supply chains, slowing new deployment of AI lending technologies. Switching to alternative non-Chinese suppliers (e.g., Taiwan Semiconductor Manufacturing Co.) will take time, leading to stopgap shortages.
Key Players Landscape and Outlook
The global generative AI lending market is dominated by a mix of established fintech disruptors, traditional credit bureaus, and tech giants, each competing on distinct capabilities. Top players differentiate themselves through algorithmic superiority, regulatory compliance tools, and data network effects. The competitive landscape is intensifying as cloud providers offer AI lending APIs, lowering entry barriers for challenger fintechs. Market conditions favor vertically integrated players combining proprietary data with AI, like LendingClub's small business lending automation, while pure-play AI vendors face margin pressures due to rising GPU costs and talent wars. The outlook remains bullish as three strategic battlegrounds emerge, which include hyper-personalization (real-time loan customization), fraud prevention (generative AI simulating synthetic identity attacks), and embedded finance (API-driven lending in e-commerce/platforms). However, fragmented regulation and resource scarcity could consolidate dominance among well-capitalized incumbents, potentially stifling innovation. Winners will likely be those mastering hybrid AI-human underwriting models that balance automation with regulatory explainability demands.
For instance, in May 2025, Finastra, a global provider of financial services applications, and IBM unveiled their collaboration on an enhanced cloud-based lending managed services offering. Finastra's Lending Cloud Service (LCS) offers comprehensive and cost-effective services for its Corporate Lending solutions, Loan IQ, Trade Innovation and Corporate Channels, and is supported by IBM for Finastra clients in North America and Europe.
All segments will be provided for all regions and countries covered
Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.