😱 European Central Bank adopts GenAI, oh my…
😱 European Central Bank adopts GenAI, oh my…
Starting in 5 hours! Free live training on how-to build predictive analytics for loan approvals
The use of generative AI in banking and financial services is set to take off at lightning speed, and with it - the demand for data professionals who can build generative AI applications to support these requirements. Here’s what you need to know…
The financial services market for generative AI reached USD 847 million in 2022 and is expected to exceed USD 9.48 billion by 2032. That’s more than 10x growth over the next 10 or so years!
Source: MarketResearch.biz
This is not some speculative far-off future
In fact, just last week European Central Bank (ECB) went on record, publicly stating that they are already using large language models (LLMs) for the following requirements:
Drafting code: Using LLMs to generate preliminary code drafts to assist experts in analysis.
Testing software applications: Using LLMs for quicker and more comprehensive software testing.
Analyzing documents: Using LLMs to evaluate, summarize, and compare documents from supervised banks, in order to support the decision-making of ECB’s supervisory teams
Producing summaries & briefings: Deploying LLMs to increase the speed at which summaries and draft briefings are written, thereby hastening bank-wide policy development and decision-making.
Refining public messaging: Utilizing LLMs to revise and refine texts written by ECB staff, in order to ensure ECB's messages are better received by the public.
Investors and board members are keen to adopt, and for good reason… The potential returns from generative AI could yield exponential increases in ROI.
Rapid adoption of generative AI across finance offers unprecedented ROI
Here are just a few of the readily available benefits that financial institutions can expect from rapidly adopting generative AI technologies.
High interest & investment: Conversations about AI and generative AI are taking place at the boardroom level, indicating the industry's keen interest. With 10x projected growth, it’s reasonable to expect easier funding and adoption of upcoming generative AI projects and products.
Decreased financial analysis workloads: The use of generative AI can significantly decrease the workloads of finance professionals by automating the production of personalized financial analyses such as credit scores, credit risks, budgeting, and tailored investment recommendations.
Fraud detection: One thing about generative AI applications is that they’re stellar at identifying anomalous patterns in real-time data. This capability stands to drastically improve fraud detection.
Regulatory compliance support: Automation using generative AI can help produce compliance reports, document verification, and customer identity validation, streamlining practices like AML and KYC, thus decreasing the administrative workloads associated with account management.
While there is much to gain with rapid adoption of generative AI across the banking sector, financial institutions will also need to take strong proactive measures to mitigate any risks related to data privacy, security, and AI ethics.
While there is much to gain with rapid adoption of generative AI across the banking sector, financial institutions will also need to take strong proactive measures to mitigate any risks related to data privacy, security, and AI ethics.
Nonetheless, a projected 10x increase in market size over 10 years is nothing to shake a stick at. That’s one reason I’m happy to let you know about a free live training - to be held TODAY - that will get you started in building generative AI applications to serve banking and finance use cases!
But first, a quick poll…
📆 FREE LIVE TRAINING - How-to build predictive analytics for loan approvals
Don’t miss your chance to get upskilled in LLMs in banking with today’s free live training on how to leverage vector DBs, OpenAI, Langchain, and machine learning models to quickly deploy generative AI applications for use in loan approvals.
Topic: LLMs in Banking: Building Predictive Analytics for Loan Approvals
Time: Monday, October 2 at 10 am PDT
Duration: 1 hour
Intended audience: Data practitioners & software engineers
What you’ll get:
A primer on technology in banking: Get acquainted with emerging paradigms in banking technology and see the whys and hows behind predictive analytics as a beacon for streamlined and intelligent loan decisions.
A high-level conceptual overview of LLMs: Learn the core concepts of Large Language Models and their pivotal role in shaping predictive analytics for loan approvals.
Hands-on code sharing and live demonstration of how to quickly build generative AI applications for rapid deployment in banking. Learn practical tools to translate these technologies into tangible outcomes.
A technical deep dive: See the ins and outs of integrating Vector DBs, OpenAI, and Langchain to bolster the predictive prowess of your applications.
Replays are available if you sign up now.
Yours Truly,
Lillian Pierson
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