Top AI Use Cases in Banks


During this webinar, industry experts discussed the top AI use cases in banks. We’ve included a short transcription of the webinar, beginning at 13:39 of the webinar.

Stuart Neilson, Citi: So the question is, which AI use cases have you found to be the most compelling? This time we can start with Javed.

Javed Ahmed, Metis Corporate Training: Yeah, there’s certainly a lot of compelling use cases, from underwriting, risk management, compliance, the scope for analytics is pretty broad. I think one of the things that’s really newer is this focus on using best practices to ensure that things like biased inputs and bias data don’t make their way into lending decisions. And so there’ll certainly always be a frontier of better risk modeling, better exposure and the fintechs are attacking that frontier pretty aggressively right now and adding additional layers of disintermediation. 

I think within the bank, one thing that’s really changing is that we can systematically use our machine learning models to ensure that we’re not incorporating biases into our decision-making and that’s an area that is growing very fast. People are not doing it as systematically as they really could be. 

Stuart Neilson, Citi: Right, it has certainly always been true that in human-based decision-making processes and machine-based decision-making processes, you have to worry about the bias. There are ways that you can deal with the bias and certainly in a lot of the financial industry context, you absolutely have to worry about the bias. But then intelligently designed artificial intelligence will be able to do that. Any other comments, Ruchi or Anusha?

Ruchi Gupta, HSBC: I can say something. Basically, I think one of the use cases which I have seen in AI is in terms of streamlining in risk management. Especially in the development and challenge of our models. Any time you develop a champion model, which is actually used for all your processes, you always develop a challenge in order to see what you could have done better. So the AI definitely allows you to use different kinds of techniques to see what I could have done to make my model better. 

This also gives you sometimes a lot of insights about your data by different technologies or something like this you can use. This is one of the use cases I’ve seen a lot.

Anusha Dandapani, Barclays: From my perspective, to add on to that, AI use cases are very effective when it comes to credit card fraud detection, identifying anomalies in credit cards and other different actions. Second, is surveillance and outlier monitoring use cases of AI. That is what we are focused on right now, to identify anomalies and bad behavior and the things that they think are humanly impossible for us to do. 

So all these use cases are augmenting our human decision-making process. We are not getting rid of the human involved in this process. We are just helping other humans to make better decisions. It’s basically enabling them to make smart and intelligent decisions. 

Certainly other use cases that I’m interested in and also optimistic about are cyber security and preventing adversity attacks that can come about to an institution. Also doing anti-money laundering network analysis. We’re trying to understand what is the state of the transactions that are exposed to the underground network that you don’t want to be part of. Also very interesting coming use cases are customer segmentation and driving insights about your customer or 360 degree or holistic view of your customer even before you end up working with your customer. These are important and also a set of use cases where there is human involvement.

Explainability is a very important consonant in the regulatory things that we are exposed to, but how to go about improving the explainability is based on the testing and the stuff that we build around the decision making models. We do that through including transparency and testing them effectively while also identifying potential human bias that can get introduced in the kinds of algorithms or the choice of the models that you choose to solve that problem. Good monitoring is also important. If you don’t monitor these models, there is this concept of models drift that can get introduced over a period of time. So model monitoring will improve the trust that is on these AI based models.

 

Learn more and watch the full video on YouTube: https://youtu.be/tDhJJ1Dd-IA


Comments

Leave a Reply

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

Related Posts

Recent Posts

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…
How AI Has Changed Marketing - During this webinar, industry experts discussed how AI has changed the marketing industry. We’ve included a short transcription of the…
Key Takeaways From Ai4 2020 - Artificial Intelligence Creates the Demand of Innovation, Autonomy, and Personalization Amidst a Crisis There is a seemingly quiet, yet enormous…
Computer Vision Versus Other ML Projects - During this webinar, industry experts discussed computer vision projects versus other machine learning projects within an enterprise setting. We’ve included…
Computer Vision in the Enterprise - During this webinar, industry experts discussed if computer vision computer is commonplace within enterprises that have machine learning models in…
How AI is Enabling Banks to Provide a Better User Experience - During this webinar, industry experts discussed how AI is enabling banks to provide a better user experience for having both…

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…