Financial services institutions are recognizing the potential of generative AI (GenAI) in optimizing operations, enhancing customer experiences and driving innovation. This article explores the impact of GenAI on the financial services sector, examining adoption trends, key use cases, challenges and the benefits associated with leveraging AI in this industry.
Key takeaways
- GenAI is currently an optimization-first play in finance: Most leaders expect moderate near-term impact, favoring measured, step-by-step adoption over disruption
- Early value shows up in productivity, efficiency, cost reduction and improved customer experience, especially in summarization, developer tooling and workflow automation
- High ROI initial use cases include fraud/AML and compliance assistance, code generation/conversion and contact center copilots, which are areas with clear controls and observable metrics
- Success requires disciplined pilot-to-production motion: Strong ROI hypotheses, data privacy by design, model risk management and compliance guardrails
- Partnerships and stack modernization matter: Banks that align product, data, platform and governance layers, and work with proven vendors, scale faster and safer
What is Generative AI in financial services?
GenAI refers to models, typically large language models (LLMs), that can generate and transform content, such as text, code and structured outputs, from prompts and context.
In a bank’s stack, GenAI typically sits in the “intelligence” layer between channels or apps and the data/platform layer, which is accessed through secure APIs and orchestration frameworks with controls for privacy, security and model risk.
The difference from predictive ML: Predictive ML estimates a value or class from features, while GenAI composes new content and reasoning traces conditioned on prompts and retrieved context.
When not to use: Avoid GenAI for fully automated, high-stakes, deterministic decisions without human oversight, or where data quality and grounding is weak, latency is ultra-tight or explanations must be rule-exact.
Why GenAI adoption is optimization-first in financial services
According to a Gartner Financial Services Research Panel survey, the financial services industry views GenAI as an optimization play, with a majority (49%) of senior business executives anticipating a moderate impact of this technology. Only a small percentage (2%) consider GenAI to have a disruptive impact in the short-term, highlighting the need for a step-by-step approach.
Financial services institutions understand that they must “walk before they can run” and cautiously navigate the adoption process. This measured approach helps mitigate risks and ensures a smooth integration of GenAI into their operations.
In 2023, Gartner’s AI Survey: CIOs and Technology Leaders View reported that 75% of financial services firms saw productivity and efficiency gains, 60% improved customer experience and 54% achieved cost reduction from AI adoption.
By harnessing the power of AI, financial services institutions can automate time-consuming manual tasks, streamline processes and free up resources to focus on more complex and value-added activities, such as more facetime with the customer.
Additionally, AI-driven insights enable personalized customer experiences, providing tailored recommendations and delivering relevant information in real-time. This enhanced customer experience strengthens customer loyalty and ultimately drives business growth.
Commenting on the significant opportunity ahead, Srinivasan Seshadri, Chief Growth Officer, Financial Services at HCLTech, said: “AI has the potential to transform financial services. For that to happen, financial institutions must transform across all layers of their capability stack. Organizations that recognize the value of AI and technology are moving toward a product-aligned operating model that combines talent, culture and ways of working to synchronize all layers of the stack. These institutions prioritize customer journey-led product development and bring people together to deliver solutions that customers value for sustainable growth.”
Generative AI use cases in financial services
Fraud prevention, AML and compliance
Financial services institutions are increasingly exploring various use cases for GenAI. One significant area of adoption is fraud prevention, where 13% of institutions are already implementing AI tools.
According to Gartner’s 2023 Customer Experience and Trust Survey, ‘better security’ was the top reason why retail banking customers switched primary provider, before reasons such as ‘better interest rates’ which ranked in second place.
Firms can leverage GenAI to proactively detect and prevent fraudulent activities. It's important to note that anti-money laundering (AML) and regulatory compliance are vital considerations in this domain, requiring tailored solutions and nuanced approaches.
Code generation and code conversion
Another prevalent use case for GenAI in financial services is code generation and conversion. By automating coding processes, institutions can save time, reduce errors and improve the efficiency of their software development operations. This enables faster deployment of applications and enhances the overall development lifecycle.
Contact center copilots and virtual assistants
Contact center assistance is another area being explored. By leveraging AI-powered chatbots and virtual assistants, institutions aim to provide efficient and personalized support to customers, reducing wait times and improving overall satisfaction, an important part of advancing total experience in finance. However, financial services face unique challenges in this area, as compliance with regulations and maintaining customer security is crucial.
Real-world GenAI examples in banking and payments
Several financial services institutions have successfully implemented GenAI in their operations, providing valuable insights into its potential.
For instance, J.P. Morgan utilizes GenAI to analyze patterns in emails for fraud detection, enabling them to identify and prevent fraudulent activities effectively. Stripe, a leading payment platform, leverages GenAI to better understand customer usage patterns and provide customized support, while simultaneously combating fraudulent transactions.
Ally Bank takes advantage of GenAI to transcribe and summarize customer service calls, increasing efficiency and enabling quick issue resolution. Klarna, a popular e-commerce platform, has integrated ChatGPT plugin into their system to enhance the customer experience by offering personalized shopping advice and product recommendations.
In addition, Erste Bank takes a distinctive approach by using GenAI to build a personalized financial services companion, focusing on enabling customers to improve their financial health. This use case goes beyond internal operations and aims to provide customers with personalized learning resources to make informed financial decisions. The GenAI companion offers various formats, such as video and audio, to cater to individual preferences and learning styles.
How to move GenAI from pilot to production (ROI, risk, compliance)
Transitioning from pilot projects to full-scale production with GenAI use cases can present challenges for financial services institutions. One significant challenge is providing a view on the return on investment (ROI) of AI implementation. Pilot projects may not require strong outputs, but when it comes to broader integration, institutions need to demonstrate tangible benefits, such as increased revenue, improved efficiency and effective risk mitigation. Legal and compliance risks also play a role in decision-making, as institutions must ensure that AI technologies adhere to regulatory frameworks, highlighting the growing focus on GenAI risk in finance.
Determining what success looks like when implementing GenAI is a crucial component. Research is underway to provide comprehensive metrics and frameworks that can guide institutions in measuring the impact of GenAI in their operations. By leveraging these metrics, financial services institutions can better understand the value generated by GenAI and make informed decisions for future implementations.
5-step pilot-to-production playbook
Size and select the opportunity
- Owner(s): Business Lead, Product Manager
- Exit criteria: Target use case prioritized with quantified value, clear user stories and success metrics defined
Ready the data, access and controls
- Owner(s): Data Engineering, Security, Privacy/Risk
- Exit criteria: Approved data access design, retrieval/grounding plan, model risk assessment initiated and sandbox with audit logging enabled
Build the pilot and validate offline
- Owner(s): ML Engineering, App Engineering, UX
- Exit criteria: Working prototype with prompt/guardrail design, safety filters and offline evaluation meeting minimum bars
Run controlled user trial
- Owner(s): Product, Operations, Compliance/Legal
- Exit criteria: Limited rollout with human-in-the-loop, monitored against KPIs; bias/consent/compliance checks passed; customer/agent feedback captured; go/no-go decision with playbook for scale
Productionize, govern and operate
- Owner(s): Platform/SRE, Model Risk Management, FinOps
- Exit criteria: Production SLOs and autoscaling in place, cost budgets/alerts set, incident and rollback playbooks ready, periodic revalidation schedule agreed and governance artifacts (model cards, DPIA, RRPs) approved
Outlook: Will GenAI disrupt future financial services?
Looking ahead, GenAI is predicted to become more prevalent in the financial services industry, with many firms already navigating the AI frontier in financial services to explore new possibilities. Vendors in this sector expect increased adoption, with more institutions leveraging AI to enhance customer and employee experiences and optimize operations. However, whether this technology is going to disrupt the industry and drive financial services firms toward true business model transformation is yet to be determined.
Navigating GenAI implementation requires the expertise and support of GenAI-specific vendors. These vendors, such as HCLTech, play a crucial role in ensuring that financial services institutions stay updated on the latest advancements in GenAI and can explore new use cases effectively. Collaborating with such vendors helps institutions streamline their AI adoption process and stay ahead in the rapidly evolving landscape of GenAI.
GenAI holds immense potential for transforming the financial services industry. By embracing GenAI, financial services firms can not only optimize their operations and enhance customer experience but also expand beyond traditional value propositions and create new revenue streams, accelerating AI innovation in finance.
A measured approach to adopting GenAI allows institutions to navigate challenges, mitigate risks and unlock the full potential of AI in financial services. As the industry continues its exploratory phase, collaboration with GenAI-specific vendors will play a vital role in staying ahead of the competition and delivering exceptional customer experiences.
FAQs
Is GenAI disruption or just optimization in finance?
Mostly optimization in the near term, but disruption may follow as controls, data and operating models mature.
What are the top GenAI use cases in finance?
Fraud/AML assistance, code generation/conversion, customer service copilots, document/process automation and knowledge retrieval for frontline/staff.
How can banks measure ROI from GenAI projects?
Define a baseline, pick 2–3 primary KPIs per use case and track net value after model/infra costs.
What role does data privacy play in GenAI?
A central role. Organizations should design for least privilege and data minimization, keep sensitive data on-prem/virtual private, log access and apply consent/retention policies.
How does compliance impact GenAI adoption?
It sets the gates: model risk management, explainability where required, records retention, fair treatment and marketing/communications rules shape scope and rollout.
What are the key GenAI applications in finance?
Customer and employee copilots, intelligent document processing, personalized insights, developer productivity and risk/compliance workflow augmentation.
What are common AI agent scenarios in finance?
Task-oriented agents for KYC refresh, dispute triage, ops runbooks, marketing content assembly and developer support, with human approval steps built in.
How is AI customer service used in banking?
As agent assist and self-serve chat, summarizing interactions, suggesting next best actions and drafting compliant responses, while handing off seamlessly to humans.
What are uses of AI marketing in financial services?
Audience segmentation, compliant content generation, journey personalization and testing/optimization across channels with strong approvals and brand guardrails.
