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Driving business transformation in financial services using Google Cloud and AI/ML

Michael Baldwin
Director, Product Management at Google Cloud AI & Industry Solutions
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Google Cloud Next 2020
July 14, 2020, Online, San Francisco, CA, USA
Google Cloud Next 2020
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Driving business transformation in financial services using Google Cloud and AI/ML
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About speakers

Michael Baldwin
Director, Product Management at Google Cloud AI & Industry Solutions
Tom Shane
Product Manager at Google Cloud AI

I lead Google Cloud's product management team for Financial Services, Industrial/Manufacturing, Media/Gaming, and the Public Sector. We build AI-powered solutions to help our customers transform their businesses. Past Google experience includes: co-founding product manager of Google Pay India (formerly Tez) while based in Singapore, helping grow Chromebooks to be the #1 device in US K-12 education, and launching Google's operations in Sub-Saharan Africa. Began my career as a management consulting analyst and spent my summer during business school in the White House Office of Science & Technology Policy. I enjoy building new products and leading cross-functional teams with audacious missions.

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About the talk

This session covers how Google Cloud partners closely with financial services organizations to drive business transformation. Watch highlights of customer stories as well as Google Cloud’s differentiated AI products and solutions now.

Speakers: Tom Shane, Michael Baldwin

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Google Cloud Next ’20: OnAir → https://goo.gle/next2020

Subscribe to the GCP Channel → https://goo.gle/GCP

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product: Cloud AI & Industry Solutions; fullname: Tom Shane, Michael Baldwin;

event: Google Cloud Next 2020; re_ty: Publish;

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Everyone on Michael Baldwin, I lead Financial Services product management for Google cloud with me today is my colleague, Tom, Shane also Financial Services product manager. Thanks for joining us, and I both spend our days thinking about how we can help financial institutions transform their businesses with artificial intelligence and machine learning today, will be over view of some of the major areas where we see this transformation happening and how Google Cloud can help. We know the business transformation is top of mind for many of our financial services, customer

time, with financial services institutions, navigating, the pandemic interest rates, are an all-time low customer. Expectations are changing with the shift to digital example. Digital banking, has increased from 63% in 2019 to 72% today. Regulatory requirements of our evolving in compliance costs, are increasing also the financial services industry. As a whole is facing new and creative types of fraud. For example, the dollar value of transactions grew 35% in April year-over-year, and finally, with the move to digital disruption is accelerating,

for instance, the pandemic drove a 72% increase in the use of apps in Europe. Table cover three areas where we Cai helping to unlock business transformation in financial services. First day I can help reimagine the customer experience to deliver things such as personalized. Digital banking, contact centers with AI, powered virtual agents and smoother. Customer customer experiences for traditionally paper. Intensive process, he's like Monday. Second financial institutions are looking for ways to use data and analytics to transform how they detect and

manage risk. And finally financial institutions in class. AI platform is flexible secure and compliant. It empowers them to build their own AI powered solutions to solve their most critical business problems. As we talked about, you examples of AI, powered transformation of financial services, you'll see that AI is in a science experiment, is being used to solve real-world problems, across banking, Capital, markets insurance, and payment is working with our customers to identify their top business problems. Then figuring out how a I can be most helpful for

them and sell them for their ultimately. Our goal is to make it as easy as possible to deploy these Solutions in real world and and workflows. Dwight recently surveyed, over 2,700, Global it and line of business Executives to obtain a global view of how organizations are adopting benefiting from and managing AI technology. And as you can see, from this recent the white survey shown here on the slide 81% of customers are reporting. Pay back on there. A i in less than two years, when AI is absorbed, in this way it can lead to True business transformation. The

first, let's talk about how artificial intelligence can help transform the customer experience. Customer experience is obviously a broad area. Let's talk through a few examples of what this can mean. Many financial institutions today struggle to get a single view of the customer. Sometimes known as customer 360 with the continued shift from physical branches. At digital financial institutions are looking for new ways to deliver high-quality and helpful experiences across all of their customers waiting up omni-channel operations across web mobile contact. Center's, physical branches in ATM

when it comes to acquiring new, customers financial institutions. Once on board in days, not weeks are looking to use AI Powers intelligence to help retain the customer. Finally, as customer 360 becomes a reality, will be able to unable to experience, is to sell the right product to the right customer at the right time. And also Drive other personalized, insights to help with customer retention in new business acquisition. Or contact center. A eye solution is one example of how a i is helping reimagine customer experience first. Let me share a quick. Overview of our of

our contact Centre. A eye solution is to deliver exceptional, customer service and increase. Operational efficiency using artificial intelligence agents to converse, naturally with customers and expertly underline article features. For example, dialogflow, which automates basic chat and voice, interactions a feature Called Agent assist, which makes human agents more effective and insight, which unlocks insights about call drivers and customer service interaction. Customer support is particularly relevant and

financial services. And financial institutions have seen a thousand percent increase in call volume. Since the onset of the pandemic customers are calling in with questions, like our branch is still open. Are you waving any fees right now? And of course, security is Paramount financial services. So our teams are hard at work, making sure that we have Bank level security built into this product. Next, I want to share how we're working to make a lending more operationally, efficient and create smoother. Customer experiences are lending document ailsa is built on top of

our document AI technology organizations unlock inside from the mountains of documents using artificial intelligence by turning unstructured data into structured data, which means you can now actually understand in you. With mortgage rates low. There's a surgeon mortgage application. This solution provides a bundle of specialized models focused on specific document type used in one day. And with this solution were able to work with wonders to quickly onboard customers in speed up the processing of paper intensive mortgage application. Also when the US Congress,

authorized the paycheck Protection Program, as part of the pandemic will make the same technology available to help with the processing of PPP loan. Necklace, talk about insurance dealing. With a car accident, can be a major headache for the automobile owner, and it's time-consuming process for the ensure we worked with USAA and ensure and a Google part Mitchell to build a model that analyzes images of cars that have been an accident and make damaged Parts. Estimates can be converted into an estimate of cost needed to repair the car taking a Razor's time and leading to a smoother customer

experience, things over to Tom. Shane my colleague and fellow product manager for financial services who will walk us through. How many in also will share an overview of how Rai platform is being used by developers and data scientist inside financial institutions to deliver on their top business priorities, Michael. Google Cloud thinks about how we can help. Apply a, i n m, l to transform Financial Services in the risk management space. You typically think about 40 things. First is, mitigating risk, across channels and

digital experience is. Michael said financial institutions are increasingly looking to move to a digital-first posture in the post covid environment. That provides great benefits from a customer experience perspective, but it does open up new, vectors of risk and potential fraud. For example, Rising stands in the retail banking segment, II moving to a holistic and contextual analysis. Listic, we mean, extracting signals from traditional structured data sources also combining that with insights from unstructured data to form. One holistic or customer 360 view that

allows Financial Services organizations to context. Reanalyze customers an event in detecting anomalies are risks that need to be reviewed. Normal behavior for that customer event, dynamically and rapidly respond to changing wrist types. For example, changing macro economic and market conditions for new fraud types in the post covid world. And finally being able to offer that worry-free and seamless customer experience Management systems that operate in the background or keeping accounts and assets state, but they don't want those distance inappropriately

impact. Their customer experience, for example, of false positive alert on a fraudulent Next, I want to talk about a customer who's working to help transform the risk management process is working with Google Cloud to leverage the latest in machine learning to dynamically assess potential, Financial crime risk in holistically. Look at customers of knowledge of combating financial crime which combined with Google's expertise, in developing ml solution, is already waiting to encouraging results in this area. You may have heard from our Co Thomas kurian

keynote. The Google cloud is focused on building a digital transformation Solutions, powered by Google's AI. In Risk Management we're focused on giving you a preview 2 of those Solutions today. The first is anti-money laundering rml and the second is know your customer kyc want to talk about some of the challenges and opportunities in each of these areas. When I customers ask us about ml deaf and talk about challenges around the current rule based system and that sophisticated, Bad actors can sometimes. Get around these systems, leading to potential Miss money, laundering

event and the system often lead to many false positive. I'm at they decide which is about four days of quality and the lack of that holistic or customer 360 view to inform their ml efforts. And finally, we do that all of these false positives or creating heavy amounts of manual work, just taking away from higher value opportunities. In terms of opportunities for Innovation Google Cloud. Can help extract insights from data and apply a, i n m l Technologies to better detect and prevent and identify those real money laundering event and then help organization operate more

efficiently, by reducing false positives, as well as providing more insights in the Alert review process. Know your customer kyc is another area that financial institutions often. Is this about your challenges such as manual profetizar around looking at documents and combining that with other information, we are on this. Sometimes, impacts the customer onboarding process either Delana or leading to express about reach the customer to collect information. And finally, we hear about challenges of integrating all this information in the bank's overall system, we think there's three

key opportunities to help improve this process. The first is helping an automation part about animation. We mean, looking at all of the documents received in the kyc process in automatically extracting insights and it is information from these documents and putting them into Downstream processing fees to a rapid and digital onboarding process especially important in the postcode environment. And finally, we think extracting insights from these documents can help better identify potential risks sooner as well as inform. Financial Services organization, overall

customer 360. Next, I want to turn to the Capital Market space, and bny Mellon is using machine learning to predict treasury settlement failures or treasury market is one of the largest and most liquid in the world. Hover 1 to 2% of transactions fail, everyday equates to about 70 billion dollars. So bny Mellon is leveraging the Google Cloud to develop end and pipelines to better predict. When these settlements might fail at being, why melons get in front of these potential failures and help mitigate them before the daily cut off? Outside of Solutions in the customer

experience and risk management space. We also know financial institutions want to build their own AI in a male models for a range of different use cases and business problems that we're focused on building and deploying a flexible platform to allow Financial Services organizations to build out running large-scale ml system we believe that responsible add equals 6. Escalade I think about responsibly I think about 40 pillars, the first is robustness and that's making sure results. Pay

robust overtime. II is ensuring that these systems are understandable. The third is considering the fantasy implications of these systems and the fourth is incorporating security by Design to keep both the data and the assets secure throughout the entire model life cycle. In terms of robustness a Google, we found that transitioning to end and machine learning pipeline. Greatly helps in this way, we see that and then I'll system is much more than just the model. It's about consistently extracting and validating data, internet data distributions, don't shift overtime. It's

about applying the right features and the right model to the problem training, the model, evaluating the results and ultimately deployment. To production is a great model that works well in development. To production, doesn't replicate result in production introduced, which is coming soon to help with this problem. With Native services such as our data. Warehouse, big Corey and I are at service training platform. We also focus on making. Sure ml systems are understandable. We know that this is key in financial services for a couple key reason. The first being that the systems,

often underpin high-value, business problems or the results, need to be robust, and they need to be understandable. Third, these systems often interact with humans. Whether it's in a model of governance, approval process or in Downstream processes such as an analyst review in a fraud alert. And finally, we know these models need to meet regulatory obligations are explainable. A eye solution based on Google's experience, understand and interpret, this solution provides feature, attributions to say which attributes are features are most

predicted of Any Given prediction in this. Hypothetical example, you see in the lower right-hand corner, you can see that the amount feature or attribute is most protective of this particular production. Google is also very committed to a i n n l, furnace, we released our add principles which govern applications in her school supplies and a male technology and we've also released a fairness toolkit. I want to highlight a couple components of it. First is the what if tool, which allows organizations to dynamically dissect their data and results II

Spanish indicators, which allows organizations to provide categories and then slice different fairness performance metrics by those categories to evaluate differences across the ribs. And finally, I want to talk about a responsible at practices, provide guidelines to organizations implementing a system. Next, I want to talk about security and how do clouds wear incorporating this by Design in Rai platform and solutions. We know, security is actually table Stakes for financial services organization, focused on a couple areas. The first is preventing data exfiltration with our

BBC, service controls and ensure, no external internet access the second choice. So customers can use their own encryption keys to encrypt and decrypt data. And customers can choose where data processing happen and where their data is stored in terms of data residency and finally, always ensuring authorized an appropriate access to that day. They're fine grain identity and access management controls as well as our access. Finally I want to turn to a customer who's using Google Cloud. A i n m l infrastructure to the cloud in large part to leverage

the latest man extractor. They're using Google Cloud tensor Processing Unit or TP. Use a purpose-built chips by Google for machine learning training and in front and you can see you in two Sigma moved the tensorflow workloads, two gpus. It's not boosting productivity, which allows them to spend less time worrying about managing their assistance in Wartime crafting results. In summary. Google cloud is focused on providing data, powered, innovation, in financial services. We're building out at powered Solutions, and customer experience and risk

management for financial services organization. And we're also focused on enabling it. Digital platform to allow Financial Services organizations to build and deploy their own. An ml solution in a way that meets the requirements. We look forward to meeting you and working with you to help transform the future Financial Services. If you're interested in learning more, please follow the URL on the slide. Thank you so much for taking time out of your day and getting we look forward to working with you.

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Michael Baldwin
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