Banks are already starting to see rewards from generative AI (GenAI) as it raises efficiency, enables sharper actionable insights, and increases profitability.
The wider use of GenAI is not a question of “if”, but “when” and “how”. GenAI could boost the banking sector’s annual revenue by $200-$340bn, or 9-15 per cent of operating profits, according to a McKinsey study, presenting a huge opportunity.
The industry already uses artificial intelligence (AI) and machine learning (ML) in areas such as marketing, fraud detection, and credit scoring. For many banks, GenAI will be an evolution rather than a revolution.
Wide range of benefits
Banking with its complexities and heavy reliance on data is especially suited to GenAI, allowing us to re-imagine the way we view data and resource-intensive processes.
Potential benefits include higher client satisfaction and more revenue through personalised customer engagements, reduced errors, more efficient use of resources, identification of new opportunities, and better data-driven decision-making.
Ultimately this will improve the way banks identify and manage risks, reduce losses and improve margins. Those that are quick to embrace and experiment with the technology could gain an advantage over slower rivals.
GenAI is set to transform banks’ interactions with clients. Generative models based on voice and speech can detect emotions to match clients with the right agent or even propose targeted solutions for agents.
AI models can also use behavioural data and other information to identify suitable products to recommend, helping to deepen relationships with existing clients or attract new ones.
The growing applications of GenAI
Common use cases for GenAI include the efficiency gains that can be achieved in research, loan origination, portfolio monitoring, regulatory compliance, know-your-customer (KYC) workflows, early warning and more.
GenAI is an excellent tool to improve research for faster processing of more data from a wider range of sources to support decision-making.
Banks can gain a more holistic view of a company, a group of companies, and/or a sector by considering not only the credit quality but also different types of risks that may become key drivers of financial performance and credit quality in the future, including climate risk, reputational standing and even cyber risk and vulnerability.
Analysts can intuitively interact with chat-like user interfaces such as Moody’s Research Assistant, to query internally or externally available content, combine insights across market segments, and put together comprehensive reports or concise summaries to present the research output.
More specific applications cover macroeconomic research for risk, strategy, or investment purposes, equity and fixed-income investment analysis and credit risk assessment for both investment and loan origination/monitoring.
One of the most common uses of GenAI is to streamline processes such as loan origination. It can enhance and speed assessment steps without losing accuracy or compromising customer data protection.
We see increasing demand for GenAI to enhance such processes and move away from manual processes.
Moody’s Smart Assistants will improve credit workflow by interacting with credit professionals through simple instructions for processes from credit pre-screening and KYC checks, to data collection, all the way to the credit assessment. Essentially preparing client memos in a fraction of the time it took previously.
Banks’ rating and scoring models have typically relied on financial statement information or bureau scores and affordability metrics.
GenAI can augment those with rich information and analytics based on a much broader dataset, ranging from behavioural, compliance, and KYC-related information to news-based signals, outlooks, sustainability/climate assessments and even cyber risk and supply chain indicators.
The output will be more informative than just a single rating or score and will also include a narrative and insights about the party in question.
Identifying & helping at-risk borrowers
Compliance and KYC checks are other key tasks for banks. AI and GenAI can work across a company’s entire ownership structure to identify compliance issues and generate full risk profiles for individuals and corporations.
Furthermore, GenAI can completely transform the KYC and investigation process workflow by providing compliance officers with a chat-based interface to comb through the data.
If an at-risk borrower is identified, AI models can suggest measures to prevent further deterioration such as adjusting credit limits, debt restructuring or consolidation, or connecting customers with financial advisors.
The models could also generate customised communications to borrowers via email, text, or phone with timely recommendations or warnings based on their situation.
Many banks are already benefiting from the application of AI-based processing based on complex combinations of deep learning (DL), optical character recognition (OCR), and natural language processing (NLP), such as Moody’s QUIQspread solution.
Models that identify outliers and abnormal patterns in submitted financial statements can spot possible indications of misstatements.
Customer information can also be sourced from accounting software, tax returns, credit bureau, or third-party master and financial entity databases, such as Moody’s Orbis Global Entity data. When properly integrated, GenAI can orchestrate the process and synthesise the information.
Time to experiment
The industry is quickly recognising the advantages of GenAI and its applications. Leading banks have already started integrating GenAI into their organisations’ DNA in the applications mentioned – and there is much more to come.
The potential is limitless. Still, for a highly regulated industry such as banking, there must be a balance between speed of deployment and the need for proper testing and governance to ensure robust, accurate, and unbiased input and results. Hence regulation is expected to drive the way many organisations leverage GenAI and ensure transparency.
Now is the time for banks to invest in understanding the enormous potential of GenAI, and to experiment and develop systems within secure environments with an eye to subsequent wider deployment. The delay could potentially be costly if faster and more nimble competitors establish a lead as this revolutionary technology reshapes industries.
Dimitrios Papanastasiou is a senior director at Moody’s Analytics, Yasman Moghaddam is a director at Moody’s Analytics