Adoption has been slow. The benefits of Gen AI technologies for retail banking are understood but adoption has been slowdespite rapid growth in investment and development.
Retail banking leaders are unaware of relevant use cases for the banking industry.
Banking leaders lack insights for developing a business-aligned Gen AI strategy as part of a transformation effort, nor do they have an understanding of how AI and ML can impact the business and how it can provide significant value.
Retail banks should approach a Gen AI adoption strategically and methodically within a larger digital strategy context, with a clear understanding of the specific use cases and benefits.
Impact and Result
Info-Tech’s use case library provides
practical guidance to help retail banks accelerate value-driven Gen AI use case
Generative AI Use Case Library for the Retail Banking Industry Research & Tools
1. Generative AI Use Case Library for the Retail Banking Industry Deck – Use this research to identify value-driven generative AI use cases to transform your organization.
Leverage a robust set of Gen AI use cases to identify potential initiatives that can accelerate value creation. Use cases cover a range of technologies and capabilities and include insight into sources of value.
Generative AI Use Case Library for the Retail Banking Industry
Identify value-driven generative AI use cases to transform your organization.
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 getting a lot of hype. It seems almost too good to be true and everyone is talking about it, especially your senior leaders and board members.
Other banks are launching new AI powered products and services and your bank is looking to do the same, but where do you begin?
You must educate yourself on key issues to ensure you are implementing responsible AI solutions that are compliant with your regulators and generate internal stakeholder value, while your customers get an exceptional experience.
You don't know where in your bank to implement AI. AI is a broad and diverse technology that it is difficult to know where it fits best within your bank.
AI governance is challenging and you must make sure it is implemented and governed effectively but you are unclear on the key issues.
The market for AI services is evolving quickly and you don't know where to begin. What model do you use? How do you choose an AI solution?
This research addresses the key issues that arise when a bank is considering implementing AI in the following areas:
This material includes the use of:
Retail banking reference architecture
Internal/external AI use cases
Six real-world case studies
The use of AI in the banking industry is evolving quickly. While it has been in use for over a decade, the recent wave of Gen AI in the media as well as the rapid growth in the AI ecosystem has gained a lot of attention especially from senior management and board members. IT teams are now expected to act quickly.
Gen AI is an innovation in ML
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.
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).
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.
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)
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
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)
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
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.
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