Age of AI:

For over two decades, analytics and machine learning (ML) have been integral to the financial services industry, transforming key domains such as marketing, sales, credit underwriting, fraud detection, customer retention, process automation and pricing. These technologies have consistently delivered value, enabling efficiency, precision, and growth. However, a new era of transformation has reached financial services with the advent of Generative AI.

Late in 2022, Generative AI captured global attention with the release of ChatGPT. While large language models (LLMs) had been in development for years, their capabilities became mainstream with this mass adoption.

The wave of interest has driven extensive experimentation and large-scale implementations within financial services firms, unlocking possibilities beyond traditional AI’s reach.

By Sirius AI’s estimates, Generative AI will impact 70% of financial services use cases within the next two years.

For 20% of use cases Generative AI now tackles previously unaddressed workflows, solving challenges previously constrained by ROI or technical limitations from Traditional(Core) AI.

Study: McKinsey - The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

The adoption of AI in banking and credit unions started with Traditional (Core) AI used to transforming functions such as risk management, customer acquisition, cross-selling, and operations. Generative AI follows a more focused path, that addresses narrow, well-defined workflows rather than complete functional overhauls as of now.

This measured approach recommended by Sirius AI reflects the deliberate strategies of financial institutions, driven by two factors:

1. Experimentation : Controlled pilots to evaluate ROI and reduce risk

2. Agent-Based Deployment : Tailored insights using fine-tuned models and proprietary data

While initial applications have been domain-specific, early successes pave the way for broader adoption. For example, a New York-based bank used Generative AI to analyze transaction data and generate actionable insights for senior executives, cutting analysis time from days to minutes.

Key Components for Success

1. Proprietary Data : Leveraging internal, high-value data is essential for creating differentiated AI capabilities that drive bottom-line value.

2. Advanced Models : Access to sophisticated AI tools from providers like OpenAI and Meta enables seamless integration into workflows and allows the models to evolve with the underlying technology.

3. Structured Data Enablement : Access to well-organized and actionable data, whether through internal systems or fintech partnerships, accelerates the adoption of AI initiatives, helping financial institutions implement use cases more efficiently.

Evaluate how you want to leverage pre-packaged solutions vs developing custom systems or integrating third-party tools, the focus remains on balancing impact, speed, and cost.

Generative AI transforms financial services through highly domain-specific workflows that change how tasks are executed and insights consumed.

For instance, front office employees benefit from dynamically generated insights tailored to specific problems they face, delivered in visual, conversational formats. Similarly, data and technology teams experience greater productivity as Generative AI reduces the burden of custom data and insight requests.

Infographic : Generative AI is being deployed across a wide range of functions within financial institutions, from risk management and customer service to operations and marketing.

From Tasks to Agent-Based Workflows

The success of Generative AI deployments in financial services depends on three critical components:

Success with AI begins with identifying and prioritizing use cases based on two key factors: Business Impact and Speed to Value.

In our experience, some of the most impactful use cases end up being Accelerators or Quick wins but the priority is very specific for every Bank or Credit Union.

Evaluation Framework recommended by Sirius AI:

Learnings from AI success stories seen by Sirius AI

1. Leverage Expertise: : Learnings from AI success stories seen by Sirius AI

2. Leverage Expertise: : Consider accelerators like external partnerships or fintech solutions to build the foundation and train internal teams. Starting internally without expertise can slow progress and dilute results.

3. Adopt a Full-Stack Approach : Generative AI alone isn’t the solution. High performing financial institutions use a mix of data science, machine learning, automation, and Gen AI to address diverse challenges effectively.

SiriusAI provides financial institutions, payments companies, and Fintechs with AI Consulting and development of measurable outcomes with AI Solutions. SiriusAI is proud to serve multiple banks, fintechs, and other institutions across Georgia. Learn more at www.siriusai.com