Generative AI Use Case Library for the Retail Banking Industry

Identify value-driven generative AI use cases to transform your organization.

Analyst Perspective

Artificial intelligence is disrupting every industry, but you can get ahead of the shift.

The use of advanced modeling, including machine learning (ML) and artificial intelligence (AI), is not new in the retail banking industry. However, the rapid acceleration of generative AI (Gen AI) is causing banks to reevaluate their policies and use of these technologies. The explosion of new tools and capabilities is reshaping the landscape almost on a daily basis.

The banking industry has several regulatory, privacy, and fiduciary restrictions in place that make the use of Gen AI more complicated. The strict requirements of having clear and audit-compliant processes in place sets a high bar that limits broader-based adoption of Gen AI in banking.

While there are challenges, there are also significant opportunities for the use of Gen AI in retail banking. Product- and service-specific applications and use cases are evolving every day. Gen AI holds promise in the area of customer experience while optimizing internal processes to drive greater speed, efficiency, and cost savings.

David Tomljenovic, MBA LL.M CIM Principal Research Director, FSI Industry Info-Tech Research Group

Gen AI is an innovation in ML

Gen AI

A form of ML whereby, in response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby use case applications.

• Audio – Converts text to sound.
• Visual – Enables text to image, video, or web design conversions.
• Code – Creates code in various programming languages based on human language prompts.
• Text – Creates text-based outputs such as articles, blog posts, emails, and information summaries.

ML

An approach to implementing AI, whereby the AI system is instructed to search for patterns in a dataset and then make predictions based on that set. In this way, the system "learns" to provide accurate content over time (think of Google's search recommendations).

AI

A field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems operate on pre-set rules, such as customer service chatbots.

Info-Tech Insight

Many vendors have jumped on "Gen AI" as the latest marketing buzzword. When vendors proclaim to offer Gen AI functionality, pin down what exactly is "generative" about it. The solution must be able to induce new outputs from inputted data via self-supervision – not trained to produce certain outputs based on certain inputs.

Banks are adopting AI

Failure to adopt an AI and Gen AI strategy is likely to result in your bank falling behind the competition in terms of client-facing products and services. You also risk suffering financially as early adopters may lower their non-interest expense ratio through efficiencies gained by Gen AI. The savings could provide your competitors more to reinvest in future differentiating capabilities.

77% of financial services organizations anticipate AI to possess high or very high importance to their business within 2 years ("A New Banking Era," Globant, 2023)

60% of banks said they had implemented at least one AI capability ("AI banking of the future," McKinsey & Company, 2020)

36% — Robotic process automation was the most popular implementation of AI in banks ("AI banking of the future," McKinsey & Company, 2020)

Info-Tech Insight

AI has been used in banking for over a decade. However, the last few years have seen a massive acceleration in its widespread adoption. AI is rapidly becoming an essential competitive requirement in banking.

Info-Tech's approach and team can help irrespective of where you are in your digital journey

Applicable framework element(s) for this document

	Starting				Benefiting
Where are you in the journey?	Establish your digital North Star	Quantify the value of digital use cases	Create the digital roadmap	Deliver digital use cases and realize impact	Create the infrastructure to drive and sustain change
	Set aspiration: Vision setting with key business unit (BU) stakeholders to discuss and align on digital aspiration (e.g. Big-T vs. Mini-T, self-funded and slow burn vs. investments).	Assess opportunity: Comprehensive E2E understanding of the digital opportunity across BU/functions (e.g. data analysis, process walks and interviews).	Design and plan: Bottom-up initiative design and planning (e.g. opportunity to initiatives, financials, phasing, design principles).	Execute: Detailed initiative builds and implementation; execution with rigor and transparency (e.g. process optimization then automation, test, measure, scale).	Enable: Set up the transformation infrastructure, operating model, and culture to drive value capture and sustain change.
Examples of how Info-Tech can help	Digital North Star placemat (e.g. industry trends, top-down opportunity, high-level planning)	Digital maturity assessments (e.g. current state digital adoption and transformation readiness)	Initiative bottom-up design (e.g. initiative ideation and business case creation, workplan, investments)	Initiative build (e.g. zero-based process redesign with technology)	Transformation infrastructure (e.g. transformation program design, transformation office)
		Opportunity assessments (e.g. BUs/function value creation diagnostics, opportunity levers)	Holistic initiative planning (e.g. phasing, interdependencies, investments)	Initiative implementation (e.g. testing and pilot, scale-up roadmap, performance tracking)	IT modernization (e.g. technology infrastructure required to execute digital levers)
				Value assurance assessment (e.g. course correcting and accelerating initiatives underway)	Change management (e.g. org-wide change program and stories, comms, governance)
	The following content will provide an overview of AI/ML use cases in retail banking. This will support opportunity assessments across the organization's value chain. Note: This does not provide the value/ROI specific to your organization. To do that, detailed current state assessments and opportunity assessments need to be executed.				Performance management (e.g. KPIs – leading and lagging, people mgmt. for continuous imp.)
					Workforce management (e.g. upskilling, right people, right place, right time)

Measure the value of this document

Document objective

Highlight best-in-class use cases to spur the initiative planning and ideation process.

Measuring your success against that objective

There are multiple qualitative and quantitative, direct and indirect metrics for which you can measure the progress of your initiative pipeline's development. Some examples of this are:

• Increased initiative pipeline value.
• Number of capabilities impacted by initiative pipeline.
• Enhanced understanding of initiatives' impact aligned to organization's capability map.
• Better understanding of which sources of value are being addressed or under-addressed in the organization's initiative pipeline.

See Establish Your Transformation Infrastructure from the Digital Transformation Center for more details

Gen AI Use Case Library Methodology

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