Generative AI (GenAI) offers transformative opportunities for banks to drive regulatory compliance, deepen customer engagement, and optimise operational costs. Early adopters of GenAI are experiencing significant improvements in productivity, operational efficiency, and customer satisfaction. Data indicates that banks utilising Generative AI can achieve the following benefits:
40% increase in employee productivity through AI-assisted task automation.
30-50% faster completion of customer-facing processes, enhancing client experience.
Up to 25% reductions in operational costs through targeted process optimisation.
These metrics highlight that generative AI is more than an incremental innovation; it is a strategic asset that offers significant competitive advantages.
In this blog, we explain GenAI's key components, how it can be applied in the banking landscape, and the business case for change.
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Disclosure: This blog was created based on a conversation (or series of prompts) with Revvy, a ChatGPT developed by Revvence and trained on finance transformation and systems change-related content.
Generative AI is a revolutionary subset of artificial intelligence that surpasses traditional rule-based systems and analytical models. It utilises large language models (LLMs) and advanced architectures to create new content, including text, code, detailed reports, and customised responses. Unlike conventional AI, which primarily focuses on identifying patterns or automating routine tasks, generative AI can simulate human-like reasoning and produce original, context-aware outputs.
Think of generative AI as a highly-skilled banking assistant with access to an extensive digital library filled with every financial report, regulatory guideline, and market analysis ever produced. When asked to create a comprehensive report or respond to a client inquiry, this assistant doesn't just pull up existing information; it synthesises insights from multiple sources to generate a new, relevant response tailored to the specific need.
Unlike a traditional assistant, which may require significant oversight and direction, this AI assistant can adapt, learn, and continuously improve its outputs over time.
In a practical sense, generative AI for banking can:
Create Detailed Reports: Automate the drafting of regulatory and financial reports by pulling data from various sources and structuring it according to compliance standards.
Enhance Customer Interactions: Generate personalised client responses, providing real-time support from historical data and current context.
Support Strategic Decisions: Offer simulations and scenario analyses that help financial planners anticipate future conditions and devise proactive strategies.
Generative AI marks a significant shift from reactive to proactive operations for banking executives involved in transformation efforts. For example, unlike traditional AI, which merely identifies potential fraud based on predefined thresholds, generative AI can simulate complex fraud scenarios using historical data and current activity. This enables risk teams to anticipate and address threats before they occur.
As another example of the power of generative AI, imagine planning a strategic transformation project where generative AI acts as a consultant embedded within the process. Instead of requiring human analysts to compile data manually, interpret it, and present options, the generative AI system analyses volumes of historical and real-time data, proposing tailored strategies and forecasting potential outcomes—all within moments.
Would you like to see a working example of this? Revvence has developed a ChatGPT application focused exclusively on Finance Transformation in banking. You can try it here.
For transformation leaders, generative AI isn’t just another technological tool; it’s a strategic partner that can scale insights and operational efficiency across multiple bank processes. By integrating generative AI, banks can reimagine processes, reduce manual workloads, and increase the speed and quality of output across all critical functions, from compliance to customer service.
In an industry driven by data, compliance, and customer expectations, generative AI will be a game-changer for banks that embrace and deploy it at scale.
Generative AI (GenAI) is transforming how banks and financial institutions conduct their operations and strategic initiatives. Unlike traditional AI, which is adept at logical tasks such as calculations and pattern recognition, GenAI adds value to creative endeavours. It excels in areas that require complex problem-solving and nuanced decision-making.
This distinctive capability makes GenAI a potent tool for areas that require human-like judgment and innovation, such as customer interaction, product development, and strategic planning.
Banks can benefit from viewing GenAI as a creative enabler rather than just an operational tool. Unlike traditional AI, which excels at processing numerical data for credit risk analysis, GenAI can synthesise diverse data into comprehensive insights to simulate market responses, create personalised communications, and assist in strategic brainstorming.
Key Focus Areas for GenAI:
Creative Problem-Solving: GenAI enhances a bank’s ability to respond to complex challenges that require more than just data analysis. It can generate scenario plans, suggest innovative solutions, and simulate the outcomes of different strategic approaches.
Enhanced Customer Engagement: GenAI improves customer interactions by producing tailored, context-aware responses. This leads to more meaningful engagement that resonates on a human level, which is essential for building trust and long-term relationships in retail and business banking.
Product and Service Innovation: GenAI can analyse market trends, customer feedback, and competitor activities to recommend new product ideas or modifications to existing services, ensuring that offerings stay relevant and aligned with customer expectations.
Integrating GenAI into Banking Strategy:
For banking leaders, adopting GenAI should be more than a technological upgrade; it should transform how creative and analytical processes intersect within the organisation. Financial institutions leveraging GenAI as a right-brain complement to traditional left-brain analytical tools are positioned to harness strategic insight and operational efficiency.
This dual focus allows banks to:
Automate routine and repetitive tasks while introducing capabilities to conceptualise, generate, and adapt content and ideas.
Shift from solely reactive and data-centric models to proactive, creative problem-solving strategies.
Use GenAI in collaborative settings where it can augment the work of teams, offering new perspectives and enhancing decision-making.
Generative AI has the potential to transform many facets of banking by automating tasks, improving decision-making, and generating new insights. Below, we explore GenAI’s potential and impact across key banking functions.
Generative AI will enhance customer interactions through intelligent chatbots and virtual assistants capable of managing complex inquiries and providing real-time, personalised service. These AI-driven systems can reduce customer wait times by 30-50% and improve customer retention by delivering faster, high-quality responses.
Customer Service: A European bank deployed a generative AI chatbot to handle routine customer queries. This resulted in a 40% reduction in calls to human agents, enabling staff to focus on more complex issues.
Customer Service: A major U.S. bank implemented an AI-driven virtual assistant to assist customers with account management and transaction inquiries, leading to a 25% increase in customer satisfaction scores and a 20% reduction in operational costs.
Customer Service: Commonwealth Bank of Australia uses GenAI to help its staff answer complex customer questions by interrogating 4,500 bank policy documents in real-time.
Content Discovery: Morgan Stanley uses GPT4 to deliver insights to its financial advisors. The application has been trained on the firm’s content and source documents.
Content Creation: Ally Bank uses GenAI to transcribe and summarise customer service calls, a previously done manually.
Content Creation: ABN AMRO is using GenAI to generate summaries of customer calls and help employees gather data on customers to assist with answering queries and repetitive questions.
Explore Further: Transforming FP&A in Retail Banking with Oracle EPM and Generative AI: A Strategic Playbook.
Generative AI streamlines the loan origination process by automating document analysis, extracting critical data from financial statements, and performing preliminary credit assessments using bank guidelines and scoring. This reduces loan officers' workload and accelerates decision-making.
Loan Origination: A significant business bank integrated generative AI into its pre-qualification stage, allowing the bank to process more applications with a 20% increase in processing speed and a 15% reduction in underwriting errors, enhancing operational efficiency and customer satisfaction.
Loan Origination: A leading financial institution employed AI-driven document processing to automate the extraction and analysis of client financial data, resulting in a 30% reduction in loan processing times and a 10% increase in loan approval rates.
Generative AI automates document preparation for business banking clients, such as loan agreements and financial summaries. This reduces the workload on relationship managers and speeds up service delivery. Generative AI’s ability to analyse client data to craft personalised engagement strategies can increase the efficiency of client-facing teams.
In fact, banks that have integrated AI-driven document automation tools report a 25% decrease in document processing times and improved client onboarding experiences.
Use Case Example: A major US bank employed generative AI for automated financial report generation. This enabled relationship managers to deliver tailored financial summaries to clients with 35% faster turnaround times, improving client satisfaction and streamlining workflows.
Generative AI aids business banking product development by analysing market trends, customer feedback, and competitor offerings to identify gaps and accelerate the creation of new and relevant products. AI-driven prototyping and simulation tools enable banks to assess a product's potential before its launch, resulting in faster development cycles and more targeted products.
Product Development: A business bank utilised generative AI to analyse market data and customer feedback, leading to the development of a new financing product tailored to small businesses, resulting in a 15% increase in market share within six months.
Product Development: An international bank employed AI-driven simulations to test new product features, reducing time-to-market by 25% and increasing customer adoption rates by 20%.
Workflow Optimisation: GenAI enhances content discovery and automates workflows to increase efficiency across business banking teams. By leveraging internal data, GenAI enables users to synthesise various documents and condense large amounts of data, facilitating quicker decision-making.
GenAI improves the detection of financial crimes, including money laundering and fraud, by analysing transaction data, customer behaviour, and external data sources in real time.
Combining generative models with anomaly detection algorithms identifies deviations from typical transaction patterns that may indicate fraudulent activities. These models can generate detailed Suspicious Activity Reports (SARs) highlighting anomalies and assisting compliance officers in risk assessments.
For example, GenAI can monitor millions of transactions and flag those that match complex fraud indicators, even if the transactions are spread across different accounts and involve multiple parties. It compiles comprehensive, structured SARs that detail the rationale behind each flagged transaction. This capability reduces the time compliance teams spend on manual analysis and improves the overall accuracy of fraud detection.
Fraud Detection: A European bank implemented GenAI to support its AML operations. The AI analysed customer transaction histories and external data sources, reducing false positives by 30% and improving the accuracy of detected fraudulent activities by 20%, streamlining the workload for compliance teams.
Fraud Detection: A global bank implemented AI-driven fraud detection tools, leading to a 30% increase in fraud identification accuracy and a 20% reduction in financial losses from fraud.
Fraud Detection: A regional US bank utilised generative AI for compliance monitoring, automating the analysis of transactions and customer behaviour. This resulted in a 40% reduction in compliance-related incidents and a 15% decrease in associated fines.
AML Use Case: HSBC has been experimenting with GenAI to create simulations that test various AML strategies, enhancing their financial crime prevention capabilities.
AML Use Case: Swedbank uses NVIDIA GPUs to train GANs (Generative Adversarial Networks) for anti-money laundering (AML) and fraud prevention, significantly improving identifying high-risk activities before they result in losses.
Compliance Reporting: An international bank used generative AI for AML compliance, cutting report preparation time by 50% while ensuring high precision.
Compliance: J.P. Morgan employs GenAI to examine internal emails for security purposes, ensuring communications align with compliance standards and are protected against potential threats.
Regulatory Reporting: A European financial institution implemented AI-driven regulatory reporting, reducing manual effort by 60% and decreasing reporting errors by 25%.
GenAI automates gathering and analysing data related to KYC attributes, customer transactions, and external market indicators in customer risk profiling. The models continuously learn from new information to adapt real-time risk profiles. Banks can keep customer profiles current and flag individuals or entities with changing risk levels.
By utilising GenAI, banks can establish automated processes that gather data from customer interactions, account activities, and public records. The AI analyses these data points to identify changes in risk profiles and alerts compliance officers to any potential increases in risk. This proactive approach allows for faster interventions and enhances the effectiveness of risk management strategies.
Use Case Example: A global bank integrated GenAI into its customer profiling system, allowing for real-time updates to customer risk scores. This implementation increased the efficiency of monitoring high-risk clients by 25% and reduced the time needed for manual profile reviews by 40%.
GenAI helps banks ensure regulatory compliance by analysing extensive regulatory texts and continuously monitoring updates from regulatory bodies.
This technology utilises large language models (LLMs) to understand complex legal language and identify changes that could affect current banking policies. It automates cross-referencing these regulations with internal bank policies, highlighting discrepancies and suggesting necessary amendments.
Banks can use GenAI to create dynamic compliance checklists that update automatically based on new regulations. This capability minimises manual compliance checks, reduces the likelihood of non-compliance, and accelerates the adoption of new regulatory requirements, ensuring the bank remains in good standing with regulators. Automating policy analysis can cut compliance review times by up to 50%, significantly reducing manual workload.
One of the biggest challenges in ESG reporting is the sheer volume of data that needs to be collected. Banks must gather detailed information on various metrics, including carbon emissions and employee diversity, sourcing this data from multiple internal systems. In this context, GenAI can automate the collection and organisation of these complex data sets.
AI can search internal databases, extract relevant ESG metrics, and categorise them into standardised groups while maintaining data integrity and accuracy. For example, AI models can continuously monitor environmental performance data, offering insights or identifying potential risks.
After collecting the data, the next challenge is translating complex findings into a clear and engaging format for regulators and stakeholders. Generative AI (GenAI) can enhance ESG reporting by automatically generating structured, coherent reports tailored to specific audiences.
Use Case Example: A multinational bank applied GenAI to evaluate the environmental risks within its loan portfolio. By analysing various climate models, the bank identified potential high-risk investments and adjusted its portfolio strategy, leading to a 15% reduction in climate risk exposure and improved sustainability reporting.
Generative AI (GenAI) offers several promising applications in finance, treasury, and Risk-Weighted Asset (RWA) optimisation, potentially improving financial reporting, capital allocation, and regulatory compliance processes.
GenAI transforms financial reporting by automating data aggregation and creating preliminary financial statements, including the narrative.
This advanced automation reduces the time that finance teams typically spend on collecting and organising data, allowing them to shift their focus toward strategic activities. Additionally, GenAI's sophisticated capability to integrate and synthesise information from diverse sources enhances reporting processes' efficiency, enabling banks to generate accurate and timely financial insights.
This not only improves the overall quality of financial reporting but also empowers finance teams to engage in more proactive and strategic roles within the bank.
In treasury management, Generative AI (GenAI) can analyse various risks associated with currency fluctuations, shifts in interest rates, and credit exposures.
By integrating and synthesising large sets of internal and external market data, GenAI equips treasury teams with real-time insights, which were not previously possible. These insights mean treasury teams can better manage their risk exposures with greater precision and agility.
Generative AI can streamline data collection and analysis to enhance stress testing and capital adequacy planning for financial institutions.
Generative AI can significantly reduce the time and effort required for these assessments by automating the gathering of relevant data. Furthermore, it can seamlessly integrate the latest regulatory guidelines into its analyses, ensuring that banks remain compliant with evolving standards.
The ability to run various scenario analyses faster and more precisely allows banks to anticipate potential market shifts and adapt their strategies accordingly.
GenAI’s natural language processing (NLP) capabilities can automate the generation of Basel IV disclosures. It can structure stress test results, risk-weighted asset (RWA) calculations, and other key data into formats required by regulators, ensuring that all necessary components are included in the disclosures.
Using GenAI, banks can produce detailed disclosures explaining stress test outcomes, capital adequacy, and RWA allocations in regulator-friendly language. GenAI can also cross-reference these disclosures against Basel IV requirements, flagging missing components.
Also, to meet Basel requirements, banks must maintain clear documentation of their stress testing methods, data sources, and decision-making processes. GenAI can help create and organise these documentation trails by automatically capturing metadata, summarising processes, and generating audit-friendly descriptions of the models and data.
GenAI offers a promising approach to optimising Risk-Weighted Assets (RWA) by continuously evaluating asset risk profiles.
GenAI can systematically update RWA calculations in response to the dynamic nature of risk factors. GenAI can pinpoint assets associated with lower risk in particular circumstances by analysing transaction data alongside relevant economic indicators. This capability allows banks to make more informed decisions about capital allocation, ensuring that resources are utilised efficiently and effectively to mitigate potential risks.
GenAI can analyse historical performance data for specific asset classes, including credit risk data, default rates, and loss-given default (LGD) metrics. By evaluating these trends, GenAI can identify discrepancies between actual performance and the current assumptions used in RWA calculations. If an asset class has consistently outperformed or underperformed relative to the assumptions, GenAI can flag this as a candidate for reassessment.
GenAI can compare a bank’s internal assumptions for asset classes against external market benchmarks or industry standards. This allows the AI to detect where a bank’s assumptions may be more conservative or aggressive compared to the broader market.
For example, if the bank’s RWA assumptions for mortgage-backed securities are significantly more conservative than market averages, GenAI could highlight this difference and provide a rationale based on recent market data or credit rating agency assessments. By drawing on external benchmark data, GenAI offers evidence to support a potential recalibration of assumptions, enabling the bank to adopt assumptions that are in line with current market norms.
Generative AI has the potential to significantly enhance the efficiency of regulatory reporting and audit support processes.
GenAI minimises the manual effort typically required to prepare audit documentation by automatically producing structured and compliant reports. This automation saves time and improves data consistency across various regulatory submissions.
Investing in Generative AI (GenAI) offers transformative potential for banks. As banks consider the investment, understanding the associated costs and the measurable business benefits is essential.
Below, we outline the main cost components, discuss GenAI's tangible benefits, and examine how platforms like Oracle’s GenAI service can optimise costs. We focus on Oracle because this is our area of expertise.
Building and running a GenAI solution involves a range of cost elements, each with unique implications for banking institutions:
Infrastructure and Cloud Services:
GenAI models require high-performance computing resources, especially for tasks like training large language models and running real-time inference. Banks frequently turn to cloud platforms for scalable infrastructure. For example, Oracle’s GenAI pricing ranges from $0.004 to $0.0267 per 10,000 transactions, depending on model type and size, with dedicated AI cluster usage costing $6.50 to $24.00 per AI unit hour. These costs scale with the volume of data processed, making cloud pricing flexibility a critical factor for managing GenAI expenses.
Data Acquisition and Management:
Data quality is central to GenAI’s performance. Banks need to invest in data acquisition, cleaning, and integration to build and maintain high-quality datasets. In our experience, this is already an investment banks are making and, therefore, not a ‘new’ GenAI-related expense.
Model Development and Customisation:
Developing or customising GenAI models for specific banking needs requires skilled data scientists, machine learning engineers, and development tools. The choice between fully custom models and pre-trained models tailored to banking applications affects the cost and speed of implementation. This is why turning to pre-trained models that can be augmented and refined with a bank's data is a more cost-effective approach.
Compliance and Security:
Banks must prioritise regulatory compliance and cybersecurity in their Generative AI (GenAI) applications as financial institutions. The ongoing costs of meeting regulatory standards—especially regarding data privacy and model transparency—are significant. Similar to those associated with data management, these costs reflect existing investments that banks make and are not directly related to their investments in GenAI.
Operational and Maintenance Costs:
After deployment, banks must maintain and monitor GenAI models, including model tuning and periodic updates, to ensure they remain accurate and relevant. These ongoing operational expenses cover infrastructure updates, model retraining, and technical support to maintain uptime and security.
While the costs of implementing GenAI can be substantial, the measurable benefits often outweigh these expenses, particularly when aligned with strategic business objectives. Here are key benefits that banks can expect from a well-integrated GenAI investment:
Enhanced Operational Efficiency and Cost Savings:
GenAI can automate complex and labour-intensive tasks, including regulatory reporting, customer support, and financial analysis. This leads to improved productivity and cost savings. By integrating GenAI into their operations, banks can reduce manual workloads and reallocate resources to higher-value activities. Automation can potentially lower operational costs by 20-30%, accelerating return on investment as routine processes are streamlined.
Improved Risk Management and Compliance:
By improving real-time data analysis and offering insights into credit risk models and transaction monitoring, GenAI enables more accurate risk assessments and timely compliance reporting. It can automatically update reports as regulatory standards change, helping banks avoid fines for non-compliance and reducing reporting cycle times by up to 50% in some cases.
Accelerated Product Development and Time-to-Market:
GenAI provides banks with quick insights into customer preferences, market trends, and competitor offerings, enabling faster development of new financial products. Banks that utilise GenAI in product development, report innovation cycles that are 25-30% faster. This acceleration improves customer acquisition and retention by delivering targeted and timely products.
Enhanced Customer Engagement and Satisfaction:
GenAI can create customised experiences that strengthen customer relationships by using personalised client interactions and predictive recommendations. For instance, AI-driven chatbots offer immediate responses and tailored financial advice, enhancing customer satisfaction while lowering support costs. Banks implementing GenAI in their customer service have seen satisfaction scores improve by 20-25%, and the cost to support clients drops dramatically.
Optimised Capital Allocation and RWA Management:
In treasury and capital planning, GenAI enhances decision-making by refining risk-weighted asset (RWA) calculations and forecasting capital needs. This enables banks to optimise capital allocations, improving return on equity (ROE) while maintaining regulatory compliance. GenAI’s role in dynamic scenario modelling and capital planning supports banks in adjusting RWA assumptions and optimising resource allocation based on richer data insights.
Oracle’s GenAI platform can help banks manage costs effectively while deploying scalable AI solutions:
The business case for GenAI in banking rests on the balance between its implementation costs and the extensive benefits it delivers. With upfront investments in infrastructure, data management, and compliance, banks can expect significant returns through improved operational efficiency, enhanced customer engagement, and potentially optimised capital allocation.
Platforms like Oracle’s GenAI service further support this investment by offering scalable, compliant solutions that help manage costs and maximise GenAI’s value.
Ready to see how GenAI can transform your bank’s operations?
Contact us to explore GenAI solutions tailored to banking compliance, efficiency, and innovation. Banks must act fast because their competitors will also explore GenAI opportunities.
At Revvence, GenAI will play a large part in the technology and finance transformation work we deliver for our clients. Revvence has developed a unique GenAI application called Revvy. Based on OpenAI's ChatGPT 4o model, it is trained in finance transformation processes, best practices, and technology architectures.
Revvy is designed to understand complex topics related to finance transformation and Oracle-based technology delivery. It complements our human expertise by enabling us and our clients to review large volumes of information, including relevant documentation such as policies or regulations. It also assesses existing application codes to optimise performance and recommends improved workflows to enhance efficiency.
Revvy can be deployed to projects to accelerate every aspect of the project, including requirements gathering, application design, documentation, testing, and application code development.
Revvence can help in several valuable ways:
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