Edward Maslaveckas, CEO and co-founder of Bud, recently joined key figures from U.S. Bank, DataStax and Google for an exclusive webinar hosted by CBA on October 16 2023.
Along with fellow leading experts within the field, we explored the transformative impact of AI on banking and financial services. Here are some key takeaways and actionable insights you might’ve missed – or simply wanted to recap.
Introduction: The paradigm shift towards AI in financial services
Kicking off the webinar, the panelists provided an overview of the paradigm shift happening in the industry due to artificial intelligence. Specifically, how generative language models allow analysis of massive datasets, even unstructured data previously deemed worthless – empowering a more personalized and thoughtful approach to financial services.
The biggest disruptions will likely start within enterprise workflows. By automating tedious tasks like simple data analysis, customer segmentation, and outreach, AI systems can accelerate operations, minimize human error and let employees focus on value-generating or human-facing work.
Over the next two to three years, many organizations will have to decide whether they’ll leverage AI to drive automation. Though smaller businesses may focus first on chatbots, larger organizations may eventually transition core systems to AI-driven models.
Real-time data: the crucial foundation for AI success
A crucial theme throughout the discussion was the importance of quality, real-time data and how essential it is for generative AI to deliver value. In essence, the models are only as good as the data they are built on.
Therefore, one key challenge is preventing damaging ‘hallucinations’ where AI makes false predictions due to limited or skewed training data. To mitigate this, financial institutions must focus on building scalable, high-throughput data pipelines capable of ingesting accurate, relevant data streams in real time.
By combining reliable data and generative models, financial organizations may unlock powerful and responsible insights designed to empower precise, data-driven decision making.
The four key components revealed as integral to the success of generative AI are:
- Scale: Massive datasets allow models to learn effectively and provide nuanced insights.
- Relevancy: Real-time, accurate data that correlates with desired outcomes trains better models.
- Throughput: High-speed ingestion of quality data enables real-time analytics.
- Speed: Low-latency data pipelines keep insights fresh and models adaptive.
For financial institutions, getting these data foundations right is critical before layering on generative AI. Data truly powers responsible and effective AI.
Generative AI enables real-time data intelligence for lending
One use case discussed was leveraging generative AI for data-driven insights to supercharge lending. Traditionally, lending decisions have depended on historical customer data that quickly becomes stale.
But with AI-powered systems, institutions can analyze thousands of real-time data points per application to effectively reduce risk while simultaneously increasing access to credit.
Generative models can also generate persuasive and explainable credit narratives to inform underwriting decisions. This increases trust and transparency for applicants and for lenders. AI aims to provide 24/7 underwriting without bias – all backed by accurate and relevant real-time data analytics.
So from startups to large banks, expect real-time data and generative AI to rapidly transform lending– unlocking faster, fairer, lower-risk decisions.
Leveraging AI-powered customer engagement to increase revenue, improve marketing and reduce overhead costs
Another key theme was how real-time data and AI enable hyper-personalized customer experiences. With comprehensive profiles from integrated datasets, financial institutions can tailor products, messaging and service offerings to each individual’s unique needs.
For instance, Bud aggregates transactional data (first party or via open banking) to generate holistic customer insights for banks and fintechs. By leveraging powerful segmentation, institutions can better identify unique interests and needs in real time – empowering hyper-personalization at scale for better banking experiences.
With this 360-degree view of their finances, banks can provide tailored experiences capable of matching individual preferences at the right time, every time. Not only does AI-powered personalization enable moving from batch analytics to continually optimized, 1:1 customer engagement, but it also builds trust and loyalty, increasing share of wallet.
Unlocking strategic insights from AI in financial services
Adopting AI is not as simple as purchasing off-the-shelf software; it requires thoughtful evaluation of business challenges and data pipelines first. The key takeaway was that financial institutions must have a strategic plan to benefit from AI.
With the right foundations and approach, generative AI unlocks immense opportunities to improve customer experiences, reduce risk, and drive revenue and deposit growth.
The future of finance is GenAI – how will you prepare?
So with the inevitable shift towards AI in finance, how will you prepare? From workflow automation to personalized services, generative AI promises to reshape financial services. But realizing this potential begins with strategic data and technology investments. Why not start by discovering how Bud is leveraging GenAI to supercharge our AI-powered solutions?
Discover Drive – Bud’s newest GenAI-powered solution
Delve into Bud’s newest GenAI-powered product offering, Drive, and uncover how you can push the boundaries of traditional finance to break down organizational silos, save countless hours of data analytics, quickly grow deposits and wallet share.
