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WELLS FARGO CIO: AI AND MACHINE LEARNING WILL MOVE FINANCIAL SERVICES INDUSTRY
FORWARD

Taryn Plumb@taryn_plumb
June 29, 2022 9:50 AM
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It’s simple: In financial services, customer data offers the most relevant
services and advice. 

But, oftentimes, people use different financial institutions based on their
needs – their mortgage with one; their credit card with another; their
investments, savings and checking accounts with yet another. 

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Internal threats that create external attack opportunities and how to combat
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Internal threats that create external attack opportunities and how to combat
them


And in the financial industry more so than others, institutions are notoriously
siloed. Largely because the industry is so competitive and highly regulated,
there hasn’t been much incentive for institutions to share data, collaborate or
cooperate in an ecosystem. 

Customer data is deterministic (that is, relying on first-person sources), so
with customers “living across multiple parties,” financial institutions aren’t
able to form a precise picture of their needs, said Chintan Mehta, CIO and head
of digital technology and innovation at Wells Fargo. 


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“Fragmented data is actually detrimental,” he said. “How do we solve that as an
industry as a whole?”

While advocating for ways to help solve this customer data challenge, Mehta and
his team also consistently incorporate artificial intelligence (AI) and machine
learning (ML) initiatives to accelerate operations, streamline services, and
enhance customer experiences.

“It’s not rocket science here, but the hard part is getting a good picture of a
customer’s needs,” Mehta said. “How do we actually get a full customer profile?”


A RANGE OF AI INITIATIVES FOR FINANCIAL SERVICES

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As the 170-year-old multinational financial services giant competes in an
estimated $22.5 trillion industry representing roughly a quarter of the world
economy, Mehta’s team advances efforts around smart content management, robotics
and intelligent automation, distributed ledger technology, advanced AI, and
quantum computing. 

Mehta also leads Wells Fargo’s academia and industry research partnerships,
including with the Stanford Institute for Human-Centered Artificial Intelligence
(HAI), the Stanford Platform Lab, and the MIT-IBM Watson Artificial Intelligence
Lab. 

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In its work, Mehta’s team relies on a range of AI and ML tools: traditional
statistical models, deep learning networks, and logistic regression testing
(used for classification and predictive analytics). They apply a variety of
cloud native platforms including Google and Azure, as well as homegrown systems
(based on data locality). 

One technique they apply, Mehta said, is long short-term memory. This recurrent
neural network uses feedback connections that can process single data points and
entire sequences of data. His team applies long short-term memory in natural
language processing (NLP) and spoken language understanding to extract intent
from phrasing. One example is in complaints management, extracting “specific
targeted summaries” from complaints to determine the best courses of action and
move quickly on them, Mehta explained. NLP techniques are also applied to
website form requests that have more context than those in dropdown menu
suggestions. 

Traditional deep learning techniques like feedforward neural networks – where
information moves forward only  in one loop – are applied for basic image and
character recognition. Meanwhile, deep learning techniques such as convolutional
neural networks – specifically designed to process pixel data – are utilized to
analyze documents, Mehta said. 

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The latter helps prove certain aspects of submitted scanned documents and
analyze images in those documents to ensure that they’re complete and contain
expected attributes, contents and comments. (For example, in a specific type of
document such as a checking account statement, six attributes are expected based
on provided inputs, but only four are detected, flagging the document for
attention.) All told, this helps to streamline and accelerate various processes,
Mehta said. 

For upcoming initiatives, the team is also leveraging cloud-native and
serverless components, and applying transformer neural network models – which
are used to process sequential data including natural language text, genome
sequences, sound signals and time series data. Mehta also plans to increasingly
incorporate random forest ML pipelines, a supervised learning technique that
uses multiple decision trees for classification, regression, and other tasks. 

“This is an area that will forward most of the financial institutions,” Mehta
said. 


OPTIMIZING, ACCELERATING, AMIDST REGULATION

One significant challenge Mehta and his team face is accelerating the deployment
of AI and ML in a highly regulated industry. 

“If you’re in a nonregulated industry, the time it takes to have a data set of
features and then build a model on top of it, and deploy it into production is
pretty short, relatively speaking,” Mehta said. 

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Whereas in a regulated industry, every stage requires assessment of external
risks and internal validation.

“We lean more towards statistical models when we can,” Mehta said, “and when we
build out large neural network-based solutions, it goes through a significant
amount of scrutiny.”

He said that three independent groups review models and challenge them – a
frontline independent risk group, a model risk governance group, and an audit
group. These groups build separate models to create independent sources of data;
apply post hoc processes to analyze the results of experimental data; validate
that data sets and models are at “the right range”; and apply techniques to
challenge them. 

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On average, Mehta’s team deploys 50 to 60 models a year, always observing the
champion-challenger framework. This involves continuously monitoring and
comparing multiple competing strategies in a production environment and
evaluating their performance over time. The technique helps to determine which
model produces the best results (the “champion”) and the runner-up option (the
“challenger”).

The company always has something in production, Mehta said, but the goal is to
continuously reduce production time. His department has already made strides in
that respect, having reduced the AI modeling process – discovery to market –
from 50-plus weeks to 20 weeks.

It’s a question of “How can you optimize that whole end to end flow and automate
as much as possible?” Mehta said. “It’s not about a specific AI model. It’s
generally speaking, ‘How much muscle memory do we have to bring these things to
market and add value?’”

He added that “the value of ML specifically is going to be around use cases that
we haven’t even thought of yet.” 

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ENCOURAGING FINANCIAL SERVICES INDUSTRY DIALOGUE 

As a whole, the industry will also greatly benefit by bridging the digital
expanse among players big and small. Collaboration, Mehta said, can help foster
“intelligent insights” and bring the industry to its next level of interaction
with customers. 

This can be achieved, he said, through such capabilities as secure multiparty
computation and zero-knowledge proof platforms – which don’t exist today in the
industry, Mehta said. 

Secure multiparty computing is a cryptographic process that distributes
computations across multiple parties, but keeps inputs private and doesn’t allow
individual parties to see other parties’ data. Similarly, cryptographic zero
knowledge proofing is a method by which one party can prove to another that a
given statement is indeed true, but avoids revealing any additional (potentially
sensitive) information. 

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Building out such capabilities will enable institutions to collaborate and share
information safely without having privacy or data loss issues, while at the same
time competing in an ecosystem appropriately, Mehta said. 

Within five years or so, he predicted, the industry will have a firmer
hypothesis around collaboration and the use of such advanced tools.

Similarly, Wells Fargo maintains an ongoing dialogue with regulators. As a
positive sign, Mehta has recently received external requests from regulators
around AI/ML processes and techniques – something that rarely, if ever, occurred
in the past. This could be critical, as institutions are “pretty heterogenous”
in their use of tools for building models, and the process “could be more
industrialized,” Mehta pointed out.

“I think there’s a lot more incentive, interest and appetite on the part of
regulators to understand this a little better so that they can think through
this and engage with it more,” Mehta said. “This is evolving fast, and they need
to evolve along with it.”

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