- Hello, everyone, happy to be here
at the Spark+AI Summit.
My name is Anurag Sehgal an head on data analytics
and machine learning and AI at Credit Suisse
within the global markets division.
I'm here to talk about
how data and digital are transforming all aspects
of how we do business as a bank.
We've gone from a traditional approach
a human-centric customer approach
to a digitally connected client journey,
where digital solutions augment human expertise.
There's an exponential demand for insights
and data-driven answers from our clients.
Our business models have gone from a predominantly bespoke
and expert judgment driven approach to additional platforms
that allow us to drive into new markets
or new routes to customers.
The value proposition for our sales trading
and research teams continues to evolve as well.
Our trading teams are looking for real-time intelligence
on how to price, how to manage inventory,
how to drive RF cues.
Our sales teams are looking for insights
and in what clients are interested in
what products they're interested in
what stocks they interested in.
And our research teams are looking to differentiate
our content and our advice on to clients
with data data and distinct and differentiated analytics.
Data Analytics is at the core of everything we're doing.
We've gone from leveraging traditional
financial market data, price data,
leveraging alternative forms, or nascent forms of data
in driving unique insights, intelligence,
and recommendations for our clients.
Data and AI machine learning are a core driver
of how we evaluate where we go next.
In order to achieve this transformation.
Our goal is to foster the generation of ideas new ideas
in enhanced and growing our business with data analytics,
allowing for experimentation, rapid prototyping,
and having a VC mindset
in leveraging and driving mature commercially viable ideas
into products, and business ventures.
In being able to do that, we're setting up ideation events,
forums where ideas come to life, be a chalk tanks,
hackathons or design thinking workshops
in partnering with digital champions
and digital advocates across all our businesses
in rethinking, re-imagining our business
and business process and,
and how we deliver most value to our clients.
We're looking at how we go from ideas to pilots prototypes
to minimum viable products
and having a clear digital product strategy
and how we go to market.
This wouldn't be possible if we weren't prioritizing
our idea funnel with a very clear, you know,
mindset of looking at ideas that are most viable
and most critical for us to take our business forward.
None of this would be possible
without having a core foundational platform
that allows us to drive rapid prototyping
rapid ideation experimentation
and take the ideas that are most commercially viable
to that scale-out mode.
So what does our core foundational platform look like?
Well, our goal is to look at large amounts
of both internal and external data
in ingesting large amounts of data onto our platform,
in looking at not only structured data
but unstructured data and looking at all kinds
of real-time streaming data.
And so the forms and types of data
that we're ingesting into our platform continues to evolve.
We are we've set up a Metadata driven
data ingestion framework that allows us
to go from define Metadata to ingesting data
to enriching those Metadata sets, to being able to look
at quality monitoring of our of our data.
And so data quality plays a key role in in, in the,
in the consumption of this data
in consumers gaining confidence on the platform
and on the data they're using.
We're using anomaly detection
to be able to look at data quality in very different ways.
Analytics is a core part of the foundation.
Machine learning and AI is a core part
of how we achieve success.
With the growth and in the use of machine learning,
our goal is to democratize the use
of machine learning and AI.
So the way we're achieving
that is through machine learning Sandboxes
in Sandbox environments that allow our data scientists
to be able to develop, deploy and test models rapidly.
in being able to look at derive trading signals and factors
that can be derived from the underlying raw
and enriched data in recommendation engines and NLP engines
that allow us to very quickly consume unstructured forms
of data and create insight from it.
Obviously, part of this ecosystem includes the ability to,
you know, have that Lifecycle Management in models
and in algorithms that we develop on the platform.
The insights from these algorithms and the data insights
that we're driving
is delivered to our clients to all our internal personas
through various different channels.
One of the key channels is through the visualization
and reporting and business intelligence layer.
But we deliver insight to our end users
where they needed at the moment and point, they need at.
So for our clients is through client portals through API's
for our sales and trading teams,
it is through our sales trading desktops,
at the point where they're making decisions on RF cues
or on iOS on any axis.
And API's play a key role in the sort of flexibility
that the platform provides us.
As we embark on the platform,
we needed to set some core design principles.
Some core architecture principles in
what would drive the success
for us in the short, mid, and long term.
Being on the public cloud was a fact most important to us
having platform as a service capabilities
that provide, you know, multi-cloud enablement that provide
for cloud-agnostic capabilities was critical to us.
The ability to scale up and scale down is something
that we did not have on-premise,
and the ability to automatically provision infrastructure
on the cloud was it was a key feature we needed.
Next-Gen technologies for machine learning AI
and a consistent user experience for our data scientists
our data engineers our business analysts
was a key part of the principles we set ourselves up with.
Data breaks and MLflow are key tenants
of how we achieve these these capabilities on the cloud.
Data Security being you know, being common comfortable
with putting confidential data
on the cloud was a journey for us.
Providing for efficiency in how we deliver
and what we deliver, you know, with CICD with continuous,
you know, continuous improvement with integration
with sprint-based deliveries with agile capabilities was,
was a big part of our core principles.
As I mentioned, machine learning
and data mining was a key focus for us in this journey.
And so the types
of things we're doing around machine learning and AI,
include, you know, with a natural language processing
include tonality and sentiment analysis, topic detection,
event detection, and text summarization.
Graph analytics and pattern mining is extremely important
to us from a you know,
from evaluating strongly connected entities
to looking at sequence of events
to look at community, community detection.
Predictive analytics was important
in areas around anomaly detection of market data
to recommendation engines for sales,
to pricing and fundamental analysis for trading teams
and our core teams.
To achieve this, we very early set out to, you know,
create a Sandbox environment for data scientists
to be able to very quickly, you know,
enable a Sandbox with all kinds of open source libraries,
open source machine learning libraries, you know,
that, that allowed them
to develop and deploy models fairly quickly.
So we created an NLP image and a Quant/Predictive image
that allowed for us to fairly quickly
take all kinds of new machine learning libraries
and put them into the hands of our data scientists
on this cloud environment.
Obviously, you know, these images and these, these,
these Sandbox environments are key for for data scientists,
but you know, the development environment
for models deployment more, you know,
in Lifecycle Management of models,
was as critical a feature in this in this process.
And so, you know, for a data science to develop models,
their IDE environment was was the Databricks notebook
for a data scientist to deploy and test these models,
you know, we allow for compute capabilities
both on scale-out and scale-up modes.
So scale-out using spark and scale up using,
you know, dynamic GPU cluster management.
To manage the Model lifecycle, you know,
we use MLflow and we'll talk a little bit more
about model Lifecycle Management
in our approach model Lifecycle Management.
But also to be able to visualize the results
and the outcomes from these models.
Our data scientists use all kinds of capabilities, you know,
from Databricks to Tableau to, you know, to other features
and other visualization libraries.
So, as I mentioned, model, machine learning
and AI model Lifecycle Management is critical to us.
And, you know, our approach
to machine learning model Lifecycle Management
is very similar to all other models we going for you know,
in rescoring in our trading trading systems.
And so model registry and versioning for all our models
but you know essentially includes aspects of all aspects
of data all aspects of model code, logging, and versioning
of parameters and hyper parameters
versioning of taxonomy used in a model
corpuses used in a model
validation and monitoring metrics used by a model.
All of that is is controlled is registered
as version controlled through this life cycle.
Here is three case studies and examples
of how the platform is starting to create value
for our business.
We're seeing recommendation engines
deliver personalized recommendations
to our sales teams around
products our clients are interested in
based on a variety of signals and data sets.
We're using alternative data insights in unique ways
to measure company performance
to look at sector performance,
to look at macro-economics views
as an example to look at economic recovery from COVID-19,
look at trends in the ESG space and enhance our products
with differentiated alternative data insights.
We're starting to look at digital platforms
and the use of digital platforms
that is allowing us to expand product coverage
and distribution capabilities
to offer unique new products and services
and reduce operational friction
and create efficiency in our business process.
So as we look to the future,
our goal is to continue to enable both offense
and defense value cases
in how we empower our business
with state of the art digital capabilities
and data-driven intelligence
to both enhance our core products
but also offer new products
and look at how we can drive
continued to drive efficiency and growth.
You know, leveraging leverage data and digital.
Platform-based solutions,
you know, as we look to embark on network effects,
and you know gain the value of platform,
we want these network effects to continue to play out.
We want to leverage digital platforms to create new markets
and new routes to customers
and deploy more and more product via digital platforms.
And lastly, we want to continue
to turbocharge data analytics, embedding machine learning
and AI in all aspects of how we do business,
in creating value for our clients,
as well as for our internal sales,
trading and research teams.
I'd like to thank you all for joining me
at the Spark+AI Summit today.
I hope you found my presentation useful and informative.
Thank you.