Generative AI vs. Conventional AI: Introduction For Operational Risk Professionals
Summary
TLDRIn this informative video, Manos Kulal, co-founder and chief risk officer at Risk Spotlight, introduces generative AI and contrasts it with conventional narrow AI technologies. He explains how generative AI, capable of producing various outputs like text, videos, and images, can enhance risk management in the financial services industry. Kulal highlights the accessibility and affordability of generative AI models, such as OpenAI's chat GPT, which has revolutionized the field by offering advanced AI to businesses of all sizes. The video also emphasizes the importance of prompt engineering to effectively harness AI's expertise and concludes with an offer of a specialized training course on applying generative AI in operational risk management.
Takeaways
- 🧠 Generative AI vs. Narrow AI: The video introduces generative AI as a technology that can produce various formats of output, contrasting it with narrow AI, which is designed for specific use cases.
- 🛡️ Narrow AI Applications: Narrow AI is widely used in financial services for tasks such as credit card fraud detection, cyber risk management, and money laundering detection.
- 🚀 Generative AI Emergence: Generative AI gained prominence with the launch of OpenAI's chat GPT in November 2022, which quickly reached 100 million users.
- 💡 Generative AI Capabilities: This technology can generate outputs in text, videos, images, and audio, offering a wide range of applications in operational risk management and beyond.
- 💼 Business Benefits: Generative AI can enhance productivity and quality of risk management activities in the financial services industry.
- 📈 Adoption and Accessibility: OpenAI's mission to democratize AI has made advanced generative AI models available for free or at a low cost, allowing even small and medium-sized firms to adopt these technologies.
- 📚 Expertise Access: Generative AI models, trained on large internet data, can provide expertise on a wide range of topics, from fitness and nutrition to business strategy and risk management.
- 🔍 Use Cases: The video highlights over 100 operational risk use cases where generative AI models can significantly improve productivity and risk management quality.
- 🛠️ Prompt Engineering: Operational risk professionals are encouraged to learn prompt engineering to effectively utilize AI models and maximize benefits.
- 🎓 Training Opportunities: Risk Spotlight offers a 15-hour training course focused on prompt engineering for operational risk management, available in online or classroom formats.
- 📧 Contact and Resources: Interested parties can reach out to Risk Spotlight for more information on prompt engineering courses and AI services via email or their website.
Q & A
What is the main focus of the video by Manos Kulal?
-The video focuses on introducing generative AI, contrasting it with conventional AI technologies, and discussing how generative AI can enhance risk management activities in the financial services industry.
What is the term 'narrow AI' referring to in the context of the video?
-Narrow AI refers to AI technologies built for specific use cases and is what is commonly referred to as AI in business discussions, as these technologies have been widely implemented across many financial services firms for around 10 to 15 years.
Can you provide examples of narrow AI implementation in operational risk management?
-Examples include credit card fraud detection, detecting malicious transactions on IT networks to manage cyber risks, and detecting money laundering transactions.
What are some first-line use cases of narrow AI technologies mentioned in the video?
-First-line use cases include marketing teams predicting customer churn, AI-based algorithmic trading, and credit scoring models based on unstructured data such as social media content.
Why are generative AI technologies labeled as 'generative'?
-Generative AI technologies are labeled as such because they can generate outputs in various formats such as text, videos, images, and audio.
What significant event in November 2022 brought the term 'generative AI' into widespread recognition?
-The term 'generative AI' became widely known when OpenAI launched ChatGBT, which signed up 100 million users in the first two months, making it the fastest adopted technology of all time.
How does generative AI differ from narrow AI in terms of accessibility and cost?
-Generative AI models, such as those provided by OpenAI, are available for free or at a low cost of $20 to $30 per user per month, making them accessible to even small and medium-sized firms, unlike narrow AI technologies which are resource-intensive.
What is the key benefit of generative AI technologies mentioned in the video?
-A key benefit of generative AI technologies is that they can provide access to expertise on a wide range of topics due to their training on large amounts of data available on the internet.
Can you provide examples of how generative AI can be utilized in operational risk management?
-Examples include identifying risks related to a business process, identifying controls to mitigate operational risks, and building detailed cyber risk scenarios.
What is 'prompt engineering' and why is it important for operational risk professionals?
-Prompt engineering is the skill of writing effective prompts to maximize the benefits gained from AI models. It is important for operational risk professionals to learn this skill to utilize AI models for real operational risk management cases effectively.
What training does Risk Spotlight offer to help professionals learn prompt engineering?
-Risk Spotlight offers a 15-hour training course focused on generative AI and operational risk management, which includes learning how to apply generative AI in the context of operational risk management and writing effective prompts.
Outlines
🧠 Introduction to Generative AI and Its Impact on Risk Management
Manos Kulal, co-founder and chief risk officer at Risk Spotlight, introduces the concept of generative AI and distinguishes it from conventional AI technologies. He explains that generative AI can produce outputs in various formats like text, videos, images, and audio, and has been rapidly adopted since the launch of OpenAI's chatbot in November 2022. Kulal contrasts this with narrow AI, which is limited to specific use cases and requires significant resources, making it accessible mainly to larger firms. Generative AI, on the other hand, has the potential to enhance productivity and quality in risk management across the financial services industry, including operational risk management and first-line use cases such as marketing and trading.
📚 Harnessing Generative AI for Expertise and Risk Management
This paragraph delves into the wide range of expertise generative AI can provide, from fitness and nutrition to business strategy and operational risk management. Kulal highlights that Risk Spotlight's AI practice has analyzed over 100 operational risk use cases where generative AI can significantly improve productivity and quality. He also emphasizes the importance of 'prompt engineering' – the skill of writing effective prompts to maximize the benefits from AI models. Kulal demonstrates the difference between a simple prompt and an effective one, showing how the latter can yield more specific and useful results for operational risk identification in a retail banking context.
🎓 Training in Prompt Engineering for Operational Risk Professionals
The final paragraph focuses on the need for operational risk professionals to learn prompt engineering to effectively utilize generative AI models. Kulal introduces a 15-hour training course developed by Risk Spotlight, which is the world's first course on generative AI for operational risk management. The course aims to teach key concepts of generative AI and its application in operational risk management, with exercises that can be customized to the specific needs of an organization. Kulal invites interested parties to explore the course and other AI services offered by Risk Spotlight by reaching out via email or visiting their website.
Mindmap
Keywords
💡Generative AI
💡Narrow AI
💡Operational Risk Management
💡Financial Services
💡OpenAI
💡Prompt Engineering
💡Risk Spotlight
💡ChatGBT
💡AI Adoption
💡Expertise
Highlights
Introduction to generative AI and its differentiation from conventional AI technologies.
Generative AI enhances productivity and quality of risk management activities in the financial services industry.
Narrow AI is built for specific use cases and has been widely implemented in financial services firms for over a decade.
Examples of narrow AI implementation in operational risk management include credit card fraud detection and cyber risk management.
Narrow AI requires extensive data, significant infrastructure, and a team of AI professionals, making it resource-intensive.
Generative AI can generate outputs in various formats such as text, videos, images, and audio.
Generative AI became widely known with the launch of OpenAI's chat GPT, which gained 100 million users in its first two months.
OpenAI's mission is to provide advanced AI technologies to counter the influence of large technology firms.
Generative AI models are available for free or a low monthly cost, making them accessible to small and medium-sized firms.
Generative AI provides access to expertise on a wide range of topics due to training on large amounts of internet data.
Risk Spotlight's AI practice has analyzed over 100 operational risk use cases where generative AI can provide significant benefits.
Generative AI can be utilized in first-line use cases such as marketing and wealth management advice.
The use of generative AI models can be extended beyond operational risk to other risk management and compliance topics.
Operational risk professionals need to learn prompt engineering to write effective prompts for AI models.
Demonstration of the difference between a simple prompt and an effective prompt for identifying operational risks.
Risk Spotlight offers a 15-hour training course on prompt engineering for operational risk management.
The course covers key topics on generative AI and its application in operational risk management.
Risk Spotlight can deliver the course in online or classroom format and customize it for specific organizational needs.
Transcripts
in this video I will cover a brief
introduction of generative AI
Technologies and how these are different
to Conventional AI
Technologies clearly differentiating
between these two technologies can
facilitate operational risk
professionals to have more thoughtful
and insightful conversations with the
business
stakeholders I'm Manos kulal and I'm
co-founder and chief risk officer at
risk Spotlight at risk Spotlight are AI
practice specializes in generative AI
Technologies and how these can enhance
the productivity and quality of risk
management activities in financial
services
industry for this video I'm going to
contrast generative AI with narrow AI
first let me cover narrow AI
Technologies narrow AI Technologies are
built for specific use cases and hence
I'm referring to them with the label
narrow
in our common business discussions when
someone refers to AI this is typically
what they are referring to this is
mainly because these AI technologies
have been with us for the last 10 to 15
years and have been widely implemented
across many Financial Services
firms example of implementation of
narrow AI Technologies in operational
risk management include credit card
fraud detection detecting malicious
transactions on it Network to manage
cyber risks and detecting money
laundering
transactions all of these are examples
of narrow AI Technologies being used as
part of controls to manage operational
risks examples of implementation of
narrow AI Technologies in firstline use
cases include marketing team predicting
customer Chun AI based algorithmic
trading and credit scoring models based
on unru structured data such as social
media content of a
customer developing and implementing
these Technologies is a major
undertaking they need extensive data for
training significant it infrastructure
for execution and a team of seasoned Ai
and data professionals this makes them
resource intensive often limiting their
use to larger firms small and
medium-sized firms typically do not have
the resources required to take benefits
of these
Technologies now let me cover the key
aspects of the generative AI
Technologies these Technologies are
based on AI models that can generate
output in various formats and hence the
label
generative they can generate outputs in
various formats such as text videos
images and
audio the term generative AI became
widely known in November 2022 when open
AI launched chat gbt and it signed up
100 million users in the first two
months this made it the fastest adopted
technology of all time ever created by
humans the adoption was faster than
mobile phones social media and even the
internet examples of implementation of
gen Technologies in operational risk
management include identifying risks
related to a business process
identifying controls to mitigate an
operational risk and build detailed
cyber risk
scenarios examples of implementation of
gen AI Technologies in firstline use
cases include marketing team
brainstorming ideas for developing
advertising content chatbot to provide
wealth management advice to clients and
it team utilizing gen AI to write
code the purpose of founding openi was
to develop and provide Advanced AI
Technologies to everyone to counter the
influence of large technology firms
holding the benefits of AI
Technologies due to this unique Mission
openai has made its Advanced generative
AI models available for free and the
premium version of the model is
available only for $ 20 to $30 per user
per
month when was the last time you saw a
transformative technology made available
to everyone in the world for free or for
$ 20 to $30 per month due to this now
even small and medium-sized firms can
adopt these AI Technologies and drive
tremendous productivity
benefits a key benefit of generative AI
Technologies is that it can provide
access to expertise on wide range of
topics the Gen models are trained on
large amounts of data available on the
inter
internet due to this they can provide
access to expertise on wide range of
topics such as Fitness Nutrition
business strategy they can be Math's
tutor for your kids and be your travel
guide when you go on
holiday for risk management these models
also provide access to expertise on
credit Market strategy reputational risk
and of course they also provide
expertise on operational risk management
our AI practice has analyzed over 100
operational risk use cases where gen AI
models can provide significant
productivity benefits and enhance the
quality of risk management a sample of
use cases where gen AI can be utilized
for the first line are highlighted
[Music]
here
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a sample of use cases where generative
AI models can be utilized by the second
line are highlighted
[Music]
here
[Music]
[Music]
since generative AI models are defined
to work with text information the use of
these models can also be extended Beyond
operational risk to many other risk
management and compliance related topics
highlighted
here while generative AI models provide
access to wide range of expertise you
can only get access to these benefits if
you utilize effective prompts a prompt
is a question you ask the AI model to
gain access to the model's expertise all
operational risk professional
now need to learn a new skill called
prompt engineering which involves
writing effective prompts to maximize
the benefits you can gain from the AI
models in the example here I have asked
a question to chat gbt to provide three
key business benefits of managing
operational risks in a financial
services firm you can see the responses
provided by chat G to this
question this is an example of an easy
prone however if you want to utilize the
AI models for real operational risk
management cases then you need to write
a more structured
prompt let me demonstrate an example of
identifying operational risks for a
business process using a simple prompt
versus an effective prompt this will
allow you to see the benefits of
learning prompt engineering I'm logged
into chat gbt now and let me show you a
simple prompt for risk identification
I will paste The Prompt text
here in this prompt I'm asking for list
of five operational risks for a business
process called account opening and
onboarding I'm providing details of the
process such as the process title the
process description and list of
activities performed as part of this
business
process let me submit this PR so I can
show you the output of this
prompt so here you can see chat gbt has
given list of five operational risks but
these are not very useful these risks
are very high level very generic like
data security and privacy compliance and
Regulatory so now let me demonstrate the
same example but this time with prompt
Engineering in this prompt I'm
requesting the risks to be identified
for the same business process however
I'm being very specific with my prompt
and providing detailed instructions to
the AI model to ensure that it provides
me with a high quality output I'm giving
the model A Persona of an expert for
identifying operational risks related to
business processes in the retail bank
banking industry I'm explicitly
specifying the risk categories for which
I want it to identify the operational
risks I'm requesting the output to be in
the table format so I can easily copy
and paste it in Excel and I'm requesting
four columns in the table the risk title
column the risk description column and
some banking examples in this case I'm
asking for two banking examples Les for
each risk and the risk category column
for the risk title column I'm specifying
that the title should be 8 to 12 words
long and it should not provide any
generic risks like geopolitical risk or
vendor dependency risks in its output
I'm also specifying that every risk
title should contain a verb which should
highlight the main event that would
occur as part of that particular
risk so so let me submit this prompt so
you can see the output based on this
prompt so here you can see the output is
now in a table format with the four
columns I
requested and you can see the title of
the risk is a lot more specific now than
what we were getting in our previous
prompt which was without prompt
engineering so I have a very specific
title which is then related to the
business process I have a description
and then I have two banking examples of
how this risk could occur in a retail
bank and because I've asked for risks
for three risk categories it's also then
specifying which risk category each risk
relates
to hopefully this demonstrates the
benefit of prompt engineering to you you
can see that the quality of output that
has been produced by prom prompt
engineering is significantly better than
the output we saw from a generic
prompt to help operational risk
professionals learn the new skill of
prompt engineering we have developed a
15-hour training course you can find the
details of this course on the training
page on our website risks spotlight.com
let me show you the details of this
course
this is world's first generative AI
course focusing on operational risk
management the course covers these key
topics you and your team need to learn
on key generative AI
conent the course will focus on these
operational riskmanagement use cases to
learn examples of how to apply
generative AI in context of operational
risk
management
we can deliver this course in online
format or classroom format and also
customize it to make the exercises
relevant for the operational risk
content of your
organization that's all I wanted to
cover in this video If you interested in
exploring more about our prompt
engineering course and other services we
can provide to facilitate the benefits
of generative AI in your organization
then please email us at AI at risks
spotlight.com you can also find more
details about our AI offerings at
www. risks spotlight.com thank you for
your time and attention
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