Machine Learning and Artificial Intelligence in Banking

Artit “Art” Wangperawong
Distinguished Engineer
US Bank


Every company’s AI journey is different. We’re all trying to figure out how we use AI for our business or our productivity and development. We’re all on different paths and at different parts of the journey. It also may lead to different places. 

Current status of AI/ML in business 
In just 15 years, 52 percent of the S&P 500 has vanished, according to CB Insights. A lot of this can be attributed to technology. That’s why many companies pay attention to any development in technology these days. Example: when blockchain was big hype in 2017. Many companies did not want to lose out on that. It’s the same thing now with machine learning.

Watch the full presentation here

Why do companies disappear from the S&P 500? Incumbents often lack ability to adapt and incorporate emerging technologies faster than startups gain distribution. 

Defining AI/ML for business 

AI is a part of big data analytics. AI has been pursued through machine learning. Machine learning can mean: 

  • supervised learning
  • reinforcement learning
  • unsupervised learning.

In order to have AI in a corporation, you need to have the ecosystem to support it: 

  • AI and machine learning
  • Robotic process automation and orchestration
  • Data and processing capacity
  • Secure cloud enablement
  • Strong customer authentication choices (privacy is a huge concern these days). 

Applying AI to business problems

Ways to describe artificial intelligence:

  • A set of algorithms,
  • That leverage data,
  • To provide capabilities, 
  • That are designed into user experiences and business processes, to create business value. 
  • Bottom line: a system of technologies operating together to achieve a goal. 

What makes AI strong

AI that can learn by itself, and that can achieve superhuman tasks. 

Note: we’re not talking about emulating the human mind or performing any tasks that humans can. We’re not trying to create artificial humans.

Commonly used machine learning methods: 

  • Supervised learning: learning with a clear target label. You can train your models to eventually predict inputs and targets. Example: home prices. 
  • Deep learning: learning from hierarchical relationships. Each level of a neural network learns levels of features that eventually define what you are trying to predict. 

Defining the AI landscape in banking. 

  • Access to data: Profitability: Cost reduction, revenue gains, and risk reduction.
  • Computational power — Competition: arms race with other companies. 
  • Advanced models: Regulation: reporting, best execution, AML. 

Deloitte study: where AI is applied with the greatest impact to banking: 

  • 65% customer service
  • 52% back office/operations
  • 42% financial advisors 
  • 31% fraud detection
  • 29% risk management

Offense and Defense for banking customers

Offensive superpowers: 

  • Natural language understanding 
  • Lead conversion and churn prevention (when customers are unhappy with their service) in sales and marketing.

Vision for the future: the next chapter of where AI might go

One way of looking at it is a digital transformation and a 4th industrial revolution. AI will be: 

  • Always on.
  • Always connected.
  • Using natural interfaces that provide contextual experiences. 
  • Proactive. 

Additionally, AI can help…

  • Update customer locations
  • Switch default payment card to business
  • Offer Packing advice before travel
  • Initiate automatic check-in
  • Provide room keys 
  • Push room number notice
  • Order room service
  • Order preferences: shades down, music soft. 

How AI helps in self-driving cars: 

  • Check investment performance
  • Order groceries 
  • Pay bills
  • Zelle money to…
  • Micropay to pass
  • eCommerce with delivery to trunk

The fine line among banking and tech and eCommerce is all getting blurred. We’re used to separating these two ideas, but they are no longer considered separate. 

How AI helps in customer service: 

  • Voice-based banking and chatbots
  • Personalized website navigation and “intelligent” dashboards
  • Natural language search 

How AI helps in integrated receivables:

  • Automatch incoming payments with invoices 
  • Reduce manual effort with automation
  • Post receivables faster. 

How AI helps in banking applications: 

  • Analyze and compare reports with OCR and NLP
  • Cross compare reports in time and content
  • Improve reporting accuracy and compliance

Key drivers of risk: 

  • Accuracy and objective 
  • Transparency (this is a big deal in banking). 
  • Bias and ethics: just because we can do certain things does not mean that we should do it.

Your homework: 

  • Avoid a fear of missing out. Ground yourself in business objectives and values. 
  • Get a scout. Stay on top of use cases, solutions and trends. 
  • Build a foundation of AI-readiness with enabling technologies. 
  • Clarify what AI means to you and your vendors.

Tags   •   Finance


Leave a Reply

Your email address will not be published. Required fields are marked *


I've been surfing online greater than 3 hours as of late, but I by no means discovered any attention-grabbing article like yours.
It's beautiful worth sufficient for me. In my view, if all site owners and bloggers made good content as you probably did,
the internet will probably be much more helpful than ever before.


Like!! I blog quite often and I genuinely thank you for your information. The article has truly peaked my interest.


This is a wonderful article, Given so much info in it, These type of articles keeps the users interest in the website, and keep on sharing more ... good luck.


I am looking for and I love to post a comment that "The content of your post is awesome" Great work!

Clementine Macken

I just want to tell you that I'm newbie to blogging and certainly liked you're blog. Almost certainly I’m want to bookmark your blog post . You actually come with beneficial stories. Thanks a bunch for sharing with us your webpage.


Such a very useful article. Very interesting to read this article.I would like to thank you for the efforts you had made for writing this awesome article.


Touche. Solid arguments. Keep up the amazing effort.

Related Posts

Recent Posts

Comparison of Popular AI Frameworks - Comparison of Popular AI Frameworks   Introduction "A computer program is said to learn from experience E with respect to…
Here’s The Main Reason Why Most AI Projects Fail - How’s your AI project coming along? If training data challenges are getting in the way of your goals, it may…
How AI is Transforming Telehealth - As technology continues to advance, artificial intelligence (AI) has become an everyday reality. And one industry it is rapidly transforming…
The Best States for New Businesses in the AI Space - The Best States for New Businesses in the AI Space The growth of AI businesses is becoming explosive. While the…
How Levatas Teaches Spot New Tricks -   One of the key components to Levatas’ success: partnerships. Customers looking for accurate analog gauge reading and thermal heat…
How AI is Revolutionizing Education -   Artificial intelligence has become increasingly relevant in a number of major industries. We read a lot about how it’s…
Three Amazing Ways AI is Revolutionizing Healthcare - It may not seem like it was too long ago when the idea of artificial intelligence playing a major role…
How 5G is Going to Impact AI in Automation Within Telecom - During this webinar, an industry expert discussed how an automation project comes to life from the initial business problem through…
How Automation Projects Come to Life in Telecom - During this webinar, an industry expert discussed how an automation project comes to life from the initial business problem through…
The Future of AI in Marketing - During this webinar, industry experts discussed where AI in marketing was heading in the future. We’ve included a short transcription…

Popular Posts

Does Healthcare AI Meet Basic Ethics Principles? - Ingrid Vasiliu-Feltes Chief Quality and Innovation Officer MEDNAX, Health Solutions Partner Over the past decade we have noticed an exponential…
Machine Learning and Artificial Intelligence in Banking - Artit "Art" Wangperawong Distinguished Engineer US Bank Introduction Every company’s AI journey is different. We’re all trying to figure out…
Machine Learning for Pricing and Inventory Optimization @ Macy’s - Jolene Mork Senior Data Scientist Macy's Iain Stitt Data Scientist Macy's Bhagyesh Phanse VP, Data Science Macy's Overview In this…
Artificial Intelligence & Cybersecurity: Math Not Magic - Wayne Chung CTO FBI Introduction The field of cybersecurity has slowly progressed from an art to a science. It has…
AI/ML in Investment and Risk Management: Recent Applications, Use Cases, and Implementation Challenges - Arvind Rajan Managing Director - Head of Global & Macro PGIM Fixed Income Introduction Investing is a completely different ballgame…
Top AI Conferences - Interested in learning the latest in AI this year? We’ve compiled a list of the top artificial intelligence conferences in…
Machine Learning in Production: From Research to the Customer - Ameen Kazerouni Lead Data Scientist Zappos Overview In this presentation Ameen Kazerouni, the Lead Data Scientist at Zappos, walks through…
How COVID-19 is Impacting the State of AI in Banking - On this panel, industry experts (listed above) discussed The State of AI in Banking and how COVID-19 is affecting it.…
“Ask Me Anything” with Zappos’s Head of AI/ML Research & Platforms, Ameen Kazerouni - Ameen Kazerouni Head of AI/ML Research & Platforms Zappos Family of Companies Ai4 recently hosted an "Ask Me Anything" session…
The Autonomous Pharmacy: Applying AI and ML to Medication Management Across the Care Continuum - Ken Perez VP of Healthcare Policy Omnicell, Inc. Ken applies artificial intelligence (AI) and machine learning (ML) solutions to medication…