Investment management is a heavily data-driven industry—portfolio managers and investment researchers require a large number of data sources to guide them in shaping their investment strategies. 

New cloud capabilities and technologies enable investment managers to process data faster than ever before and iterate on ideas quickly to fuel innovation in the signal generation process and gain a competitive edge. 

Using the cloud for investment research workflows makes it easier to onboard data from data providers, spin up large compute workloads in the midst of market volatility or during heavy research cycles, and manage complex machine learning or natural language workflows to gain market insights.

We hear from industry leaders that they’re exploring new ways to run investment research. “Differentiated investment strategies require new types of information sources, and new ways to process that information,” David Easthope, senior analyst, Market Structure and Technology, Greenwich Associates. “And that, of course, relies heavily on having access to reliable and scalable storage, computational, and AI / ML resources. More specifically, quantitative strategies can benefit from the computational platforms and embedded AI/ML capabilities the cloud can offer.” 

Google Cloud gives investment managers essential components to work and operate faster as they bring their investment research workflows to the cloud. Here are the key highlights:

1. Simplify, speed up your data acquisition, discovery, and analytics

The foundation of any investment strategy starts with data—acquiring it, detecting patterns, and analyzing it for insights. Enabling data providers to easily share large datasets such as tick history within a high-performance analytics engine can greatly reduce the data engineering overhead when possible.

Once data is onboarded, you can tag business and technical metadata related to your datasets and provide portfolio managers the ability to discover these datasets via a search interface.

We further review analytics options for various scenarios, including aggregating massive datasets, creating dashboards, and incorporating streaming analytics workloads.

2. Take advantage of burst compute workloads

Data engineers and researchers require ready access to burst compute capabilities to perform backtesting, portfolio simulations and run risk calculations. Cloud works well for these workloads due to its elasticity, consumption-based models, and hardware evolution.

Many investment managers are shifting to a container-based strategy along with a Kubernetes-based scheduler for greater consistency, scaling and efficiency in environments with a large number of researchers. Cloud managed services and a rich suite of CI/CD tools can make this vision a reality while improving security and developer productivity.

3. Tackle machine learning (ML) and model deployment with the help from cloud

Quantitative researchers scour vast amounts of market and alternative data sources searching for signals and correlations, while ML engineers have the challenge of taking these signals and moving them to production.

Google Cloud empowers users to create and operationalize their models without wasting valuable time with a comprehensive set of MLOps tools. 

In this paper, we explore multiple solutions for ML and model deployment. Those capabilities reduce the amount of time operationalizing ML models, so quants and data scientists have more time to devote to differentiating activities. 

4. Get the data you need in less time with Natural Language and Document AI

Thousands of financial filings, news articles, and sell-side research reports are generated every day, and it’s difficult for humans alone to process this volume of information. These documents are often generated in many languages and the ability to do entity recognition, sentiment or syntactical analysis in those languages, or perhaps translate them into the language of the portfolio manager is of critical importance. Google Cloud provides these capabilities through pre-trained models, or allows you to train high-quality models with your own datasets.

Getting started

There are plenty of emerging technologies, tools, and approaches available to help investment managers today. At Google Cloud, we can help you access, organize, and utilize these essential components to make your research faster, reliable, and more valuable.

To learn more about these four keys to better investment research, check out our whitepaper for more.

At Google Cloud, we continue to invest in key technologies to progress towards our true north star on invisible security: making strong security pervasive and simple for everyone. Our investments are based on insights from our world-class threat intelligence teams and experience helping customers respond to the most sophisticated cyberattacks. Customers can tap into these capabilities to gain perspective and visibility on the most dangerous threat actors that no one else has. 

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), accelerate our ability to help the people who are responsible for keeping their organizations safe. These new models not only give people a more natural and creative way to understand and manage security, they give people access to AI-powered expertise to go beyond what they could do alone. 

At the RSA Conference 2023, we are excited to announce Google Cloud Security AI Workbench, an industry-first extensible platform powered by a specialized, security LLM, Sec-PaLM. This new security model is fine-tuned for security use cases, incorporating our unsurpassed security intelligence such as Google’s visibility into the threat landscape and Mandiant’s frontline intelligence on vulnerabilities, malware, threat indicators, and behavioral threat actor profiles. 

Google Cloud Security AI Workbench powers new offerings that can now uniquely address three top security challenges: threat overload, toilsome tools, and the talent gap. It will also feature partner plug-in integrations to bring threat intelligence, workflow, and other critical security functionality to customers, with Accenture being the first partner to utilize Security AI Workbench. 

The platform will also let customers make their private data available to the platform at inference time; ensuring we honor all our data privacy commitments to customers. Because Security AI Workbench is built on Google Cloud’s Vertex AI infrastructure, customers control their data with enterprise-grade capabilities such as data isolation, data protection, sovereignty, and compliance support. 

Preventing threats from spreading beyond the first infection

We already provide best-in-class capabilities to help organizations immediately respond to threats. But what if we could not just identify and contain initial infections, but also help prevent them from happening anywhere else? With our AI advances, we can now combine world class threat intelligence with point-in-time incident analysis and novel AI-based detections and analytics to help prevent new infections. These advances are critical to help counter a potential surge in adversarial attacks that use machine learning and generative AI systems. That’s why we’re excited to introduce:  

• VirusTotal Code Insight uses Sec-PaLM to help analyze and explain the behavior of potentially malicious scripts, and will be able to better detect which scripts are actually threats. 
• Mandiant Breach Analytics for Chronicle leverages Google Cloud and Mandiant Threat Intelligence to automatically alert you to active breaches in your environment. It will use Sec-PaLM to help contextualize and respond instantly to these critical findings.

These new updates build on the existing AI in Google’s industry-leading solutions. For example, Chronicle Security Operations already uses frontline intelligence, integrated reasoning, and machine learning to identify initial infections, prioritize impact, and contain threats. Another example is reCAPTCHA Enterprise, which uses image noising capabilities to help protect your site from adversaries that leverage novel AI advances, greatly enhancing our defenses against bots. 

Adding intelligence to reduce toil

At Google Cloud, we help organizations modernize security wherever they are, in part by simplifying their security tools and controls whenever possible. Advances in generative AI can help reduce the number of tools organizations need to secure their vast attack surface areas and ultimately, empower systems to secure themselves. This will minimize the toil it takes to manage multiple environments, to generate security design and capabilities, and to generate security controls. Today, we’re announcing: 

• Assured OSS will use LLMs to help us add even more open-source software (OSS) packages to our OSS vulnerability management solution, which offers the same curated and vulnerability-tested packages that we use at Google.
• Mandiant Threat Intelligence AI, built on top of Mandiant’s massive threat graph, will leverage Sec-PaLM to quickly find, summarize, and act on threats relevant to your organization. 

These announcements build on existing capabilities that help customers centralize visibility and control, detect targets, and improve security across their platform. For example, Security Command Center (SCC) uses always-on machine learning to detect malicious scripts executing in the customer container environment and immediately alert the customer. In addition, Cloud Data Loss Prevention leverages machine learning to find and classify data, and with Confidential Computing you can collaborate on, train, and deploy sensitive and regulated AI models in the cloud, all while preserving confidentiality. 

Evolving how practitioners do security to close the talent gap 

At Google, we believe that to truly democratize security, we need to first acknowledge that AI will soon usher in a new era for security expertise that will profoundly impact how practitioners “do” security. Most people who are responsible for security — developers, system administrators, SRE, even junior analysts — are not security specialists by training. 

Imagine a world where novices and security experts are paired with AI expertise to free themselves from repetition and burnout, and accomplish tasks that seem impossible to us today. To help power this evolution, we’re embedding Sec-PaLM-based features that can make security more understandable while helping to improve effectiveness with exciting new capabilities in two of our solutions: 

• Chronicle AI: Chronicle customers will be able to search billions of security events and interact conversationally with the results, ask follow-up questions, and quickly generate detections, all without learning a new syntax or schema.
• Security Command Center AI: Security Command Center will translate complex attack graphs to human-readable explanations of attack exposure, including impacted assets and recommended mitigations. It will also provide AI-powered risk summaries for security, compliance, and privacy findings for Google Cloud.

These new releases bolster our existing efforts to tackle these issues through capabilities like IAM Recommender, which suggests permissions better suited to actual usage patterns. We will soon be augmenting this capability to cover organizational policies, further enabling the administrator to help improve the security posture of their organization. In addition, Mandiant Automated Defense applies machine learning to help reduce the repetitive Tier 1 alert triage problem and address alert fatigue. 

Offering availability 

VirusTotal Code Insight, available now in Preview, is our first example of putting Security AI Workbench to work for our customers. We will be rolling out other offerings to trusted testers in coming months, and they will be available in Preview more broadly this summer. Click here for the demo. 

Security AI Workbench, including Sec-PaLM and partner integrations, in addition to the product innovations described in our demo, are all building blocks for a larger effort to elevate security across the ecosystem. So far, that effort: 

• Provides assistive functions to rapidly develop IT generalist talent to Tier 1 security operator status in a way that wasn’t previously feasible. Security Command Center now can summarize threat intelligence insights and findings for Google Cloud, and Chronicle can quickly generate YARA-L rules or other detections. 
• Provides advanced functions such as iterative query and multivariate detection generation, conversational filtering and interaction with results, and smart case awareness to empower advanced Tier 2 and 3 security operators to focus on threat analysis instead of struggling with process and toil. Mandiant Threat Intelligence users now can elevate their core competencies to hunt, investigate, and remediate threats — using the same tools our own Mandiant experts use. 
• Fuses threat intelligence and AI-based analytic capabilities, which are unsurpassed in the market. VirusTotal Code Insight enables security teams to help gain insights and identify threats in suspicious code. This can significantly enhance their ability to detect and mitigate potential attacks.

However, this is just an initial step. We’ll continue to iterate and innovate, and we encourage customers and partners to leverage Security AI Workbench in new and exciting ways. Moving forward, we anticipate many new use cases to emerge over time. 

Building a safer future

While generative AI has recently captured the imagination, Sec-PaLM is based on years of foundational AI research by Google and DeepMind, and the deep expertise of our security teams. This work includes new efforts to expand our partner ecosystem to provide businesses with security capabilities at every layer of the cybersecurity stack. We have only just begun to realize the power of applying generative AI to security, and we look forward to continuing to leverage this expertise for our customers and drive advancements across the security community.

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.

We are thrilled to announce that Google has been named a Leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021 report. For more than a decade, BigQuery, our petabyte-scale cloud data warehouse, has been in a class of its own. We’re excited to share this recognition and we want to thank our strong community of customers and partners for voicing their opinion. We believe this report validates the alignment of our strategy with our customers’ analytics needs.

“Customers like Google’s frequency of data warehouse releases, business value, future proof architecture, high-end scale, geospatial capabilities, strong AI/ML capabilities, good security capabilities, and broad analytical use cases,” according to the Forrester report. Today’s data leaders require a data warehousing platform that provides both depth and breadth and with BigQuery, organizations are able to unlock deeper data science and machine learning capabilities while promoting data democratization and providing the highest levels of availability. 

Google BigQuery: 5 out of 5!

Forrester gave Google BigQuery a score of 5 out of 5 across 19 different criteria, including:

Today, customers across the globe use BigQuery to run business critical analytics workloads to enable BI acceleration, IoT analytics, customer intelligence, AI/ML-based analytics, data science, data collaboration, and data services. Customers such as Verizon, Wayfair, HSBC, Twitter, AirAsia, KeyBank, The Home Depot, and Vodafone have anchored their digital transformation efforts on BigQuery—unlocking deeper insights for their people. 

Customers use BigQuery across all industries, to solve issues like credit card fraud detection, predictive forecasting, anomaly detection, log analytics and many more. Forrester recognized this work and gave BigQuery a 5/5 score for supporting vertical and horizontal use cases. 

More BigQuery advantages 

Google is the first hyperscale provider to offer a multi cloud data analytics solution. With BigQuery Omni, customers can perform cross-cloud analytics with ease, and drive business outcomes they couldn’t achieve with the siloed approach offered by other vendors. 

We designed BigQuery to be highly scalable and more open and interoperable so that customers can join data across SQL databases, traditional unstructured data lakes in object storage, and even spreadsheets using any analysis tool. 

Recent innovations like BigQuery BI Engine lets us provide the best analytics experience by delivering sub-second query response times from any business intelligence tool, from Google’s Looker and Connected Sheets to Tableau, Microsoft Power BI, ThoughtSpot, and others. Our goal is to meet customers where they are rather than force them into a one-size-fits-all approach to data analysis. 

With BigQuery, organizations gain both breadth and depth of capabilities to transform their analytics strategy. Forrester also gave BigQuery 5 out of 5 in:

Data-powered innovation 

These advantages enable our customers to accelerate their digital transformation and reimagine their business through data-powered innovation. Google’s leadership across AI, analytics, and databases comes together in a single data cloud platform that provides everyone with the ability to get value out of their data faster.

We bring decades of research and innovation in AI to our customers through industry-leading AI solutions, that in turn helps our customers solve their biggest problems. Our support of open standards and APIs enables interoperability between a variety of services for ingestion, storage, processing and analytics across the data cloud platform. And finally, we believe our multi-layered security approach throughout the data stack ensures redundancy and reliability so that customers can have the peace of mind that their data is always protected. 

We are honored to be a leader in this Forrester Wave™ and look forward to continuing to innovate and partner with you on your digital transformation journey. 

Download the full Forrester Wave™ :Cloud Data Warehouse, Q1 2021 report. And check out these smart analytics reference patterns. To learn more about BigQuery, visit our website, and get started immediately with the free BigQuery Sandbox.

In the United States, Tax Season descends upon the country every April, requiring millions of Americans to spend hours deciphering cryptic documents and performing complex math just to figure out what they owe. Wouldn’t it be grand if there was a way for a computer to take all the relevant documents and extract out exactly what the IRS is looking for? Lending Document AI from Google Cloud supports common document types used for Income Tax Filing, such as W-2s and 1099s. These advancements in machine learning technology now makes it possible to alleviate some anxiety leading up to April 15th.

Lending Document AI is a Document Understanding solution that allows for classification and parsing of documents commonly used in the mortgage lending industry. The data in these unstructured files is then converted into a structured format, which can be stored in a database or used for analysis and calculations. You can read more about the product in the announcement blog post. For this tax filing use case, we will focus on automatically classifying and parsing the 2020 editions of the following forms:

W-2

1099-DIV

1099-INT

1099-MISC

1099-NEC

This sample application creates an automated pipeline where the user can bulk upload a collection of PDFs, the Lending Document Splitter & Classifier will classify each document and send each PDF to the appropriate specialized parser to extract the data, which can then be used to calculate an individual tax return and fill out a 1040 Form.

Overview

Let’s explore how this application works. You can check out the sample code in this GitHub Repository.

Here is an outline of the architecture of this application. As you can see, it utilizes Cloud Run and Firestore in Native Mode for the web application in addition to Document AI.

The User uploads multiple PDF files to the web application, hosted on Cloud Run.

An API call is made to the Lending Document Splitter & Classifier for each PDF file.

The output of the classifier (e.g. W-2, 1099-MISC, etc.) is then mapped to an appropriate specialized parser in the Google Cloud Project.

Each document file is sent to the appropriate specialized parser that matches the document type.

The entities are extracted by the parser processor and the data is written to Firestore.

The raw data is now retrieved from Firestore and displayed to the User showing the file classification and extracted values from each form.

The data values from all the forms are used together to calculate an individual income tax return.

The Calculated Tax Rates/Incomes/Deductions are displayed to the User in a Tabular Format matching the IRS Form 1040. The app also displays which form data was used for each field. (Some output fields use values from multiple forms, such as line 25b.)

Step-by-Step directions

Want to try this out for yourself? Here’s how you can deploy and run this application using a Google Cloud Project. You can run this in Cloud Shell (Quickstart) or on your local machine.

NOTE: The Lending Processors in this Demo are in Limited GA as of March 2022. If you have a business use case for these processors, you can fill out and submit the Document AI limited access customer request form.

Install dependencies

1. Clone the GitHub Repository to get the sample code.

git clone https://github.com/GoogleCloudPlatform/document-ai-samples.git

2. Enter the directory for the tax pipeline demo

cd document-ai-samples/tax-processing-pipeline-python

3. Install Python and the Google Cloud SDK if they aren’t already installed.
4. Install the python libraries:

pip install -r requirements.txt

5. Create a new Google Cloud project, and enable billing if you don’t already have one.
6. Enable the Document AI API:

gcloud services enable documentai.googleapis.com

7. Setup application default credentials:
   gcloud auth application-default login

Deploy demo application

1. Edit the config.yaml file, adding your own Project Details docai_processor_location: us # Document AI Processor Location (us OR eu)
   docai_project_id: YOUR_PROJECT_ID # Project ID for Document AI Processors
   firestore:
   collection: tax_demo_documents # Set with your preferred Firestore Collection Name
   project_id: YOUR_PROJECT_ID # Project ID for Firestore Database
2. Run setup scripts to create the processors and Cloud Run app in your project. python3 setup.py
   gcloud run deploy tax-demo –source .
3. Visit the Deployed Web Page (You should get a link from the deployment command)

Calculate Tax Returns Homepage

4. Upload Documents. I created some sample documents you can download from the sample-docs folder of the repository.

This demo currently supports the following Document Types (2020 Editions)

W-2
1099-DIV
1099-INT
1099-MISC
1099-NEC

5. Click “Upload” Button, wait for processing to complete.
   The page will display the steps completed for each document file. These are also written to stdout for troubleshooting purposes.

6. View the extracted values from each file.

7. Click “Calculate Taxes” to see the tax calculation output

Conclusion

Warning: This is NOT financial advice, for educational purposes only.

Congratulations! You now have a fully functional tax processing application that can also be modified for use with other workflows that require data from multiple specialized documents.

The Document AI API is flexible and modular enough that most of the code in this example can be reused for any specialized processor.

Now tax returns can be filed with minimal manual effort!

If you want to learn more about Document AI, check out the Cloud Documentation and these videos:

• Getting started with the Document AI platform
• Process billions of pages and cut operational costs with DocAI

And if you want more hands-on experience, I recommend following these step-by-step codelabs to get started with the key features of Document AI:

• Optical Character Recognition (OCR) with Document AI (Python)
• Form Parsing with Document AI (Python)
• Specialized Processors with Document AI (Python)
• Managing Document AI processors with Python