Banks are increasingly using Generative AI for assessing credit risk by enhancing traditional models with advanced data analysis and predictive capabilities. According to various reports market size for artificial intelligence (AI) in the banking sector is estimated at USD 34.58 billion in 2025, with projections to grow significantly. Forecasts indicate it could reach USD 75.36 billion by 2030 at a compound annual growth rate (CAGR) of 17.96% or up to USD 379.41 billion by 2034 at a CAGR of 30.63%. Specifically for generative AI, spending in banking is projected to hit USD 84.99 billion by 2030, reflecting a CAGR of 55.55%. AI has ability to play transformative role in enhancing efficiency, security and towards enhancing customer experience in banking. McKinsey estimates AI could add up to $1 trillion in value to banking by 2030.
Bank of America (BofA) has significantly advanced its use of generative AI to enhance credit risk assessment, aligning with its broader AI adoption strategy to improve productivity and client service across its global workforce of 213,000 employees. BofA employs AI to analyze traditional and alternative data sources, such as transaction histories and economic indicators, to build more accurate creditworthiness models. This improves the prediction of default risks and supports better lending decisions. Launched in 2018, Erica, BofA’s AI-driven virtual financial assistant, has facilitated over 2.5 billion client interactions, serving 20 million active users. In 2020, BofA introduced Erica for Employees, adopted by over 90% of its workforce. This tool handles tasks like password resets, device activation, and HR queries, reducing IT service desk calls. Plans for 2025 include expanding its capabilities with generative AI to cover broader topics, including product and service inquiries.
Its AI-powered conversation simulators enables employees to practice client interactions. Over one million simulations were completed last year, improving service consistency and employee proficiency. Tools like Erica for Employees and coding assistants streamline workflows, reducing operational bottlenecks. The 50% reduction in IT calls and 20% coding efficiency gains demonstrate significant time savings. With over 7,400 patents, including 1,200 in AI and machine learning, and a $13 billion annual tech budget ($4 billion for new initiatives in 2025), BofA maintains a competitive edge in financial innovation.
JPMorgan’s COiN technology reviews documents faster than humans and generative AI could save banks $200–340 billion annually by boosting productivity. Its AI systems identify malware and phishing threats while Barclays uses AI to flag suspicious activities. AI-powered chatbots and virtual assistants like Bank of America’s Erica are handling inquiries 24/7. It is reducing wait times and improving satisfaction. Erica has surpassed 1.5 billion interactions since 2018. AI is tailoring financial advice and product offerings by analysing customer data. Banks like USAA are leveraging AI for hyper-personalized service, such as customized insurance based on individual needs.
AI algorithms are analysing vast datasets in real-time to detect fraudulent transactions and assess credit risks. More precisely, Generative AI analyzes vast datasets including non-traditional sources like transaction histories, social media activity, and economic indicators for creating more accurate creditworthiness profiles. It identifies patterns that traditional models might miss. It enables better predictions of default risk and lenders can track changes in borrower behavior and adjust risk profiles promptly.
It generates realistic synthetic data to simulate financial scenarios and improves stress testing. Generative AI model conducts validation without relying solely on historical data. This helps assess risks under diverse conditions. It is automating tasks like drafting credit memos, summarizing customer data, and generating risk reports, speeding up decision-making and reducing manual effort.
Generative AI is assisting in coding risk models, such as those for Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), improving model accuracy and efficiency. It is streamlining operations from loan processing to regulatory compliance and helping banks in classifying green transactions by supporting sustainability goals. Deutsche Bank is using machine learning to pre-select eco-friendly deals for aligning its operations with EU regulations.
Though AI adoption is facing hurdles like data privacy, potential biases in algorithms, and regulatory compliance, but its role will continue to increase and transform the banking sector.
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