When I reflect back on the past year and the pandemic, I’m struck by how the reliance on remote work and operations has changed the fundamentals of business forever. For the financial services industry, this rings particularly true. Many conversations I’m having right now with organizations revolve around embracing a transformation cloud, and thinking of cloud computing not just as an infrastructure decision, but also as the locus for transformation throughout the company. 

Today, as we welcome the industry to our Financial Services Summit, we’ll demonstrate just how Google Cloud accelerates a financial organization’s digital transformation through app and infrastructure modernization, data democratization, people connections, and trusted transactions. We hope you’ll join us.

How we’re helping financial services firms build their transformation clouds

At Google Cloud, we continue to focus on areas where we can bring the best of our capabilities to banking, capital markets, insurance, and payments customers around the world. Our work with financial services industry customers has given us a deep understanding of the real-world, specific use cases that matter most to them. This groundwork led us to engineer products and solutions that are open and flexible, not ones that force them to rip out existing investments in ERP or other early IaaS cloud implementations. 

It’s why we’ve engineered solutions such as Lending DocAI, Open Banking with Apigee, and Datashare for financial services to help transform the industry. These solutions were created with our customers’ security and compliance top-of-mind and are built at the intersection of user-optimized experience, technology, and sovereignty.

At their core, financial institutions want to drive growth, reduce costs, mitigate risk, stay compliant, and increase efficiency. As a result, when we partner with them on their transformation journeys, we consider three essential focus areas: 

• Enabling the human experience and connected interactions
• Building an open and intelligent data foundation for better insights
• Providing the most trusted and secure cloud in the industry

Enabling humans and connected interactions

A company’s transformation is about more than technology; people and culture ultimately drive change. HSBC, for example, recognized its business users would benefit from guided answers to common questions around risk policy compliance, and turned to Google Cloud to leverage AI and machine learning bots to assist employees, ease the burden on policy experts, and improve the user experience. Using Dialogflow, a core component of Contact Center AI, HSBC was able to build a conversational platform that quickly and accurately addresses user needs at scale. 

Another example is Equifax, which used Google Workspace to support collaboration not only internally between employees, but also externally with customers. Customers can use Google Cloud solutions for financial services to build these sorts of technology-enabled human interactions quickly and easily—supporting organizational change at scale.

Building an open and intelligent data foundation for smarter, faster insights

The real impact of Google Cloud solutions for financial services comes when the whole company has access to the right information at the right time, and can act more intelligently on that data. Our solutions help businesses safely leverage their data and get a complete 360-degree view of their customers’ information, which can often be scattered across multiple systems (CRM, lending, credit, etc.). This helps financial institutions improve the overall customer experience—and sometimes even develop new products quickly. 

Indeed, all financial institutions are looking for ways to grow revenue and reduce expenses, and data can be a critical ingredient to doing both effectively. As daily transactions rise, so does the volume and complexity of data. But to implement new customer experience innovations (and new revenue streams), financial institutions must first capture data effectively. This is why AXA Switzerland, for example, uses real-time analytics on Google Cloud to gain cross-industry insights about future trends and customer preferences.

Financial services organizations also need the confidence of building on a platform that provides choice, flexibility, and agility to move fast. It’s why we have an open cloud approach that allows Google Cloud services to run in different physical locations such as on-premises, other public clouds, and the edge. Customers can also harness the power of data and AI through our open APIs, machine-learning services, and analytics engines on any major cloud platform. This is why companies like Macquarie Bank are taking advantage of Google Cloud’s open, hybrid architecture to modernize and empower its developers.

Compliant and secure to address risk and regulatory needs

As a highly regulated industry, financial services is focused on security and compliance, risk and regulations, and fraud detection and prevention. Google Cloud offers unique capabilities to earn customers’ trust as part of our continuing work to be the most trusted cloud in the industry. Google Cloud provides a secure foundation that you can verify and independently control. Our cloud technology reduces risk and data loss, because it is built on comprehensive zero-trust architecture. Finally, we offer a shared-fate model built on best practices in risk management via automation, guidance, and insurance. This is why customers like BBVA have confidence anywhere their systems may operate. 

On the regulatory front, global legislators and regulators continue to focus on the stability of the industry that only a decade ago went through one of the biggest liquidity crises in history. With this oversight comes strong expectations of risk mitigation. Google Cloud offers a single, global set of controls, reviewed by financial institutions and regulators around the world, and verified in collaborative audits—making compliance simpler and less costly for our customers.

Finally, Google Cloud allows financial services firms to operate confidently with advanced security tools that help protect data, applications, and infrastructure, as well as their customers from fraudulent activity, spam, and abuse. We help protect your data against threats, using the same infrastructure and security services we use for our own operations, ensuring you never have to trade-off between ease of use and security. Google Cloud encrypts data at-rest and in-transit. And we now also offer the ability to encrypt data-in use, while it’s being processed for customer VM and container workloads.

Let us help you with your transformation cloud journey

We’ve seen leading financial services companies embrace Google Cloud to help them move beyond infrastructure toward the next phase of their cloud evolution. This is an era where no company is better positioned to lead than Google Cloud, and we’re excited to help you with your journey.

Learn more about Google Cloud for financial services.

Need a quick overview of Google Cloud analytics technologies? Quickly learn these 11 Google Cloud products—each explained in under two minutes.

BigQuery in a minute

Storing and querying massive datasets can be time consuming and expensive without the right infrastructure. This video gives you an overview of BigQuery, Google’s fully-managed data warehouse. Watch to learn how to ingest, store, analyze, and visualize big data with ease.

Firestore in a minute

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps, at global scale. In this video, learn how use Firestore and discover features that simplify app development without compromising security.

Cloud Spanner in a minute

Cloud Spanner is a fully managed relational database with unlimited scale, strong consistency, and up to 99.999% availability. In this video, you’ll learn how Cloud Spanner can help you create time-sensitive, mission critical applications at scale.

Cloud SQL in a minute

Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud. In this video you’ll learn how Cloud SQL can help you with time-consuming tasks such as patches updates, replicas, and backups so you can focus on designing your application.

Memorystore in a minute

Memorystore is a fully managed and highly available in-memory service for Google Cloud applications. This tool can automate complex tasks, while providing top-notch security by integrating IAM protocols without increasing latency. Watch to learn what Memorystore is and what it can do to help in your developer projects.

Bigtable in a minute

Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. In this video, you’ll learn what Bigtable is and how this key-value store supports high read and write throughput, while maintaining low latency.

BigQuery ML in a minute 

BigQuery ML lets you create and execute machine learning models in BigQuery by using standard SQL queries. In this video, learn how you can use BigQuery ML for your machine learning projects.

Dataflow in a minute

Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing. In this video, learn how it can be used to deploy batch and streaming data processing pipelines.

Cloud Pub/Sub in a minute

Cloud Pub/Sub is an asynchronous messaging service that decouples services that produce events from services that process events. In this video, you’ll learn how you can use it for message storage, real-time message delivery, and much more, while still providing consistent performance at scale and high availability.

Dataproc in a minute

Dataproc is a managed service that lets you take advantage of open source data tools like Apache Spark, Flink and Presto for batch processing, SQL, streaming, and machine learning. In this video, you’ll learn what Dataproc is and how you can use it to simplify data and analytics processing.

Data Fusion in a minute

Cloud Data Fusion is a fully managed, cloud-native, enterprise data integration service for quickly building and managing data pipelines. In this video, you’ll learn how Cloud Data Fusion can help you build smarter data marts, data lakes, and data warehouses.

Not long ago, building AI into recommendation engines was a daunting, expensive task that could take years to get off the ground. But as Bazaarvoice has shown, with the help of cloud services, the time from AI investment to business outcomes is shorter than ever. 

Bazaarvoice is the leading provider of product reviews and user-generated content (UGC) solutions that help brands and retailers understand and better serve customers. Its 2019 acquisition of Influenster.com, a community of consumer reviewers 6.5 million strong, expanded the Bazaarvoice portfolio with a platform where consumers can share their candid opinions — and share they have, over 54 million times. 

After the acquisition, Bazaarvoice expanded the site’s product diversity by 53%, to more than 5.4 million unique products. To keep user engagement high, Influenster must be seen as both a source of trusted, transparent reviews and a place for customers to discover useful, relevant products for the first time. By introducing shoppers to new products Influenster not only provides value to customers but also helps brands collect consumer insights. 

Influenster started out as a place where people gathered to share their honest thoughts on beauty products but quickly expanded to nearly every category, from Art to Wearables. Because of the much smaller scope, the site started and flourished under a rules-based recommendation engine. However, as Influenster expanded its scope under Bazaarvoice, a more robust recommendation system became necessary. In its earliest days, Influenster was successful because of the human perspective it offered: For every product there was a litany of reviews and images that made users feel as if they were getting an endorsement on a product from a friend. 

The Bazaarvoice engineering team asked themselves how they could keep that same feeling of personalization with an ever-growing catalog of items and categories. They needed recommendations that could scale with the site, rather than requiring more rules be constructed each time a new product category was introduced. They also needed to ensure the Influenster experience would remain performant even towards unknown members.

Bazaarvoice tested out several recommendation engines, benchmarking each against their current rules-based system. In the end they decided on Google Cloud’s Recommendations AI because of its transparent billing, ease of integration and setup, and naturally, its proven results.

Transparent Billing

“Part of what the engineers loved was they knew exactly what it was going to cost as it scaled” says Nick Shiftan, SVP, Content Acquisition Services Product Unit for Influenster. The goal was to build once and innovate rather than leave a wake of technical debt only to be tackled when costs grew unexpectedly out of control. Google Cloud’s straightforward and pay-as-you-go billing allowed them to anticipate how costs would grow as user interactions did and plan accordingly.

Ease of integration

“I’m positively surprised how Google packed such a complex system in a very easy-to-use API” remarks Eralp Bayraktar, the Software Engineering team lead overseeing the project. Because the original team was made of just one full-time engineer the ease of integration became an even more critical feature. Not only does Recommendations AI pull from years of suggestion expertise in Google Search and YouTube, but in combination with Ad’s Merchant Center, it also creates a streamlined process for importing product metadata. From there, creating a model becomes a matter of picking the preferred recommendation type and then the business objective to optimize for. Once the model is created and the API integrated into the website, the code is already deployed at the global scale: There are no further architectural considerations to ensure recommendations are available to users worldwide. For Bazaarvoice, this meant going from ideation to production in one month.

Proven Results

“We have used it for product recommendations and off-loaded our DB-tiring business logic to Recommendations AI, which resulted in overall faster response times and much better recommendations as proven by our A/B tests,” Eralp continues.  

Bazaarvoice began by A/B testing Recommendations AI against their rules-based system. Early on in the experimental phase they noticed a clear and consistent 60% increase in the click-through rate over their original recommendation system. 

Even more impressive was the performance on Unknown Members. For every person that signs up for an account on Influenster.com there are many other visitors that come to the website and leave without fully registering. This is typically referred to as the “cold start” problem in the industry — how do you figure out what to recommend to those people without their history, behavior, or preferences? Recommendations AI gives you the option to input and train on unknown users, and by providing metadata on products, it can provide high-quality suggestions to registered members and first-time users alike. 

With a mind to the future, Eralp concludes his thoughts on Bazaarvoice’s experience: “It enables discovery by adding an adjustable percentage of cross-category products [for] healthier [traffic distribution] across all our catalog. We are investing in data science and having the Recommendations AI as the baseline is a good challenge for us to thrive.” 

To learn more about Recommendations AI and how it can help your organization thrive, check out our recently published 4 part guide which kicks off with an overview on “How to get better retail recommendations with Recommendations AI.” This series also covers data ingestion, modeling, as well as serving predictions & evaluating Recommendations AI. You can also easily get started with our Quickstart Guide.

In the past decade, the Healthcare and Life Sciences industry has enjoyed a boon in technological and scientific advancement. New insights and possibilities are revealed almost daily. At Google Cloud, driving innovation in cloud computing is in our DNA. Our team is dedicated to sharing ways Google Cloud can be used to accelerate scientific discovery. For example, the recent announcement of AlphaFold2 showcases a scientific breakthrough, powered by Google Cloud, that will promote a quantum leap in the field of proteomics. In this blog, we’ll review another omics use case, single-cell analysis, and how Google Cloud’s Dataproc and NVIDIA GPUs can help accelerate that analysis.

The Need for Performance in Scientific Analysis

The ability to understand the causal relationship between genotypes and phenotypes is one of the long-standing challenges in biology and medicine. Understanding and drawing insights from the complexity of biological systems abounds from the actual code of life (DNA) through to expression of genes (RNA) to translation of gene transcripts into proteins that function in different pathways, cells, and tissues within an organism. Even the smallest of changes in our DNA can have large impacts on protein expression, structure, and function, which ultimately drives development and response – at both cellular and organism levels. And, as the omics space becomes increasingly data- and compute-intensive, research requires an adequate informatics infrastructure. An infrastructure that scales with growing data demands, enables a diverse range of resource-intensive computational activities, and is affordable and efficient – reducing data bottlenecks and enabling researchers to maximize insight. 

But where do all these data and compute challenges come from and what makes scientific study so arduous? The layers of biological complexity begin to be made apparent immediately when looking at not just the genes themselves, but their expression. Although all the cells in our body share nearly identical genotypes, our many diverse cell types (e.g. hepatocytes versus melanocytes) express a unique subset of genes necessary for specific functions, making transcriptomics a more powerful method of analysis by allowing researchers to map gene expression to observable traits. Studies have shown that gene expression is heterogeneous, even in similar cell types. Yet, conventional sequencing methods require DNA or RNA extracted from a cell population. The development of single-cell sequencing was pivotal to the omics field. Single-cell RNA sequencing has been critical in allowing scientists to study transcriptomes across large numbers of individual cells. 

Despite its potential, and the increasing availability of single-cell sequencing technology, there are several obstacles: an ever increasing volume of high-dimensionality data, the need to integrate data across different types of measurements (e.g. genetic variants, transcript and protein expression, epigenetics) and across samples or conditions, as well as varying levels of resolution and the granularity needed to map specific cell types or states. These challenges present themselves in a number of ways including background noise, signal dropouts requiring imputation, and limited bioinformatics pipelines that lack statistical flexibility. These and other challenges result in analysis workflows that are very slow, prohibiting the iterative, visual, and interactive analysis required to detect differential gene activity. 

Accelerating Performance

Cloud computing can help not only with data challenges, but with some of the biggest obstacles: scalability, performance, and automation of analysis. To address several of the data and infrastructure challenges facing single-cell analysis, NVIDIA developed end-to-end accelerated single-cell RNA sequencing workflows that can be paired with Google Cloud Dataproc, a fully-managed service for running open source frameworks like Spark, Hadoop, and RAPIDS. The Jupyter notebooks that power these workflows include examples using samples like human lung cells and mouse brains cells and demonstrate acceleration between CPU-based processing compared to GPU-based workflows. 

Google Cloud Dataproc powers the NVIDIA GPU-based approach and demonstrates data processing capabilities and acceleration, which in turn have the potential of delivering considerable performance gains.  When paired with RAPIDS, practitioners can accelerate data science pipelines on NVIDIA GPUs, reducing operations like data loading, processing, and training from hours to seconds. RAPIDS abstracts the complexities of accelerated data science by building upon popular Python and Java libraries effortlessly. When applying RAPIDS and NVIDIA accelerated compute to single-cell genomics use cases, practitioners can churn through analysis of a million cells in only a few minutes.

Give it a Try

The journey to realizing the full potential of omics is long; but through collaboration with industry experts, customers, and partners like NVIDIA, Google Cloud is here to help shine a light on the road ahead. To learn more about the notebook provided for single-cell genomic analysis, please take a look at NVIDIA’s walkthrough. To give this pattern a try on Dataproc, please visit our technical reference guide.