Say Goodbye to Manual W2 & Payslip Processing with Document AI
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Documents like payslips and W2s are crucial to processes such as employment and income verification for mortgage loans, personal loans, personal finance, and benefits processing. Unfortunately, efficiently extracting data from these documents at scale can be challenging and time-consuming, with many organizations relying on manual examination of documents or automated approaches that don’t adequately capture the document data needed for given tasks. Google Cloud built Document AI to remove these barriers, empowering customers to deploy powerful machine learning models to more quickly process documents, save money, and discover insights. We’re excited to expand Document AI’s capabilities with the recent release of improved pre-trained models for W2s and payslips, built on Document AI Workbench.
Pre-trained models let developers focus on core application logic and leave the complex task of information extraction from the documents to Google’s AI technology. In many cases, the primary driver for automated data extraction is operational efficiency and cost savings, but Document AI can also open new possibilities. For example, a financial services company might use Document AI to enable fully self-serve loan applications on mobile devices, helping the organization to differentiate itself with simple, fast customer experiences.
We’ve heard from customers that more granular entity extraction from W2 and payslip documents is particularly important, with organizations requiring support for a wider variety of layouts and formats. The recent launch of the stable release of these pretrained models addresses these requests.
Here is what is new with W2 parser:
- The parser improves accuracy and entity specificity thanks to the ability to break down long entities such as addresses into fine-grained sub-entities like StreetAddressOrPostalBox, AdditionalStreetAddressOrPostalBox, City, State, and ZIP code.
- It can handle a wider variation of W2 forms, including multi-copies (2,3,4-ups) issued by various payroll vendors. The model is not limited to specific tax years, which means it should be able to process W2 for 2022 or beyond provided there are not significant changes to the format.
- It introduces eight new entities for Box 12 that represent both codes and values, enriching understanding of the various taxable and non-taxable components of the W2 recipient’s income.
Here is what is new with Payslip parser:
- Bonus, commissions, holiday, overtime, regular pay, and vacation are now part of earning_item/earning_this_period and earning_item/earning_ytd. The parser captures types of earnings beyond those categories, and maps them to their respective earning rates, hours, and pay (both for the period and year-to-date). This helps in building a more detailed understanding of the components of the payslip recipient’s income
- The parser now returns year-to-date and current-period taxes and deductions.
- Direct deposits are linked to corresponding bank account numbers.
- The parser now returns page numbers, state and federal tax exemptions, and filing statuses.
While these parsers have become more useful out of the box, with this release, the ability to uptrain makes them easy to modify as new needs arise. Uptraining lets developers further improve the accuracy of these models and extract additional fields with minimal development work. It also lets developers customize existing parsers to support new document types that are similar. For example, the parser is trained on U.S. data and could be uptrained to create a payslip parser for the U.K.
We’re pleased that parsers are already making a difference for customers. Bryan Jackson, CTO at lending automation firm Gateless, said, “High accuracy data extraction is critical to the success of our Smart Underwrite solution, and Document AI provided better results than competitors. Using the latest W2 & Payslip pretrained parsers, we saw a 48% increase in performance on pay stubs and a 15% performance improvement in W2s. The ability to easily uptrain models as new document variations are introduced ensures we continue to deliver optimal outcomes for our customers.”
Additional pre-trained models available as release candidates include parsers for 1040, 1099R, 1120, and 1120S documents. Check for details here. To learn more, talk to a Google Cloud sales executive about how Document AI can help your business, and check out our Document AI breakout session from Google Cloud Next ’22.
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Breakthrough Insights Across the Life Sciences Value Chain
New science and patient centricity require increasing reliance on data and deeper insights.
Accenture’s life sciences customers have been demanding answers to this need, and want a solution with an open platform.
Enter INTIENT, Accenture’s life sciences platform that is powered by Google Cloud.
INTIENT generates insights throughout the life sciences value chain from early research through pivotal clinical trials all the way through to delivering care to patients.
In this video, Kevin Julian, Senior Managing Director, Life Sciences, Accenture and Michael Stapleton, Senior Industry Executive, Accenture, discuss enables ground-breaking possibilities through applied intelligence, including the development of personalized therapeutics tailored to a patient’s genetic makeup, the utilization of real world data/evidence in clinical trials, better identification of patients for trials, development of digital therapeutics, and improved patient outcomes — in other words, better regimen adherence.
Google Cloud’s Firebase Realtime Database and BigQuery AllowsCastbox to Ramp Up Customer Experience
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Demand for spoken audio content such as podcasts remains robust despite the proliferation of video services and other entertainment options for consumers. Shibin Li, Co-founder of Castbox, credits growth of the global podcast platform to the following: speed and availability, market-leading features, the proliferation of smart devices to deliver audio content, and activities — such as driving and working around the house — that make consuming video difficult.
Founded in 2016 and headquartered in Beijing, China, Castbox enables users to locate, access, and create spoken audio content. Available on iOS and Android, Castbox supports 50 million podcasts, on-demand radio programs, and audiobooks in 70 languages from 175 countries. The platform hosts about 2 million users per day and is the largest podcast platform on Android.
Castbox includes a range of features that build on its core service to provide a high-quality user experience. These features include curated podcast recommendations and in-audio search.
A combination of services
At its inception, Castbox relied on a combination of a multinational cloud services, as well as Google Cloud Platform (GCP) services, including the Google BigQuery analytics data warehouse and Cloud APIs to provide programmatic interfaces with Google Cloud services, and a range of services from the Google mobile development platform Firebase.
However, as Castbox matured and its user base expanded, the business increased its reliance on Google Cloud Platform and Firebase.
“We needed to access stable cloud services as we could not tolerate long periods of downtime that would compromise the user experience,” says Li. “Furthermore, we had to support up to 50,000 concurrent connections, and potentially more in future, without disruption.”
“Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it. This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”
—Shibin Li, Co-Founder, Castbox
Competitive differentiation
Castbox also found machine learning-powered Google Cloud Platform APIs could help deliver features, such as in-audio search, that differentiate the podcast platform from its competitors. In addition, Firebase SDKs and Firebase A/B Testing would enable Castbox to create and analyze new applications, as well as make adjustments based on user feedback. Firebase Realtime Database would allow the business to support tens of thousands of concurrent user connections.
The diligence of the Google Cloud team in advising Li and her team about forthcoming products and services also swayed Castbox towards Google technologies. The business gained the opportunity with Google to join several programs that offered early access to Google innovations.
Signature in-audio search service
Castbox now uses Google Cloud Platform services in the Tokyo, Japan, and U.S. East regions. Cloud Speech-to-Text API plays a key role in delivering Castbox’s signature in-audio search service. This service enables users to search transcriptions of audio content on the platform for words or phrases. The search results incorporate the title of the podcast and the search term in context (for example, within the sentence or sentence excerpt in which it appears). Each use of the word or phrase is time-stamped so it can easily be found. The API enables Castbox developers to apply neural network algorithms to achieve audio-to-text conversion accuracy rates of greater than 96%, while search queries typically experience latency of just 50 milliseconds.
In addition, the latency of comparison data, converting audio to text, is only about 250 milliseconds, contributing to the processing of about 12 minutes worth of audio to text in just 10 minutes. “We can process about 20 hours of audio files in one day,” Li says. “This enables us to transcribe and index all the new episodes of a podcast in that period.”
50,000 concurrent connections
With Firebase Realtime Database, Castbox now supports up to 50,000 concurrent connections to its platform with an average latency per connection of just 10 milliseconds. “Firebase Realtime Database also allows us to continue operating in offline mode, which is extremely helpful if we experience any network disruptions,” Li explains. “When we come back online again, any data is simply synchronized with the database.”
Google BigQuery and the analytics capabilities of Firebase SDKs also enable Castbox to monitor and analyze user behaviors. “Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it,” Li explains. “This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”
“Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.”
—Shibin Li, Co-Founder, Castbox
Castbox also uses its analyses of Google BigQuery data to amend banners and summaries on its platform to encourage users to listen to additional content. Furthermore, the service is prepared to make surprise recommendations of content to users based on the preferences and reactions of users with similar tastes.
“We analyze a pool of data growing at up to 30 GB per day,” Li explains. “Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.” These analyses also support Castbox’s decision to start creating original content, such as finance and economic news, for its platform.
Checking weekly changes
Castbox does not rely only on analyzing user data to deliver a high-quality experience. The business aggregates user feedback from emails and Google Play reviews to make weekly changes to its platform. It then uses Firebase A/B Testing to check whether these changes are met with a positive user response.
“We are extremely pleased with Google Cloud Platform and Firebase. We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”
—Shibin Li, Co-Founder, Castbox
Castbox’s positive experiences with Google Cloud Platform are encouraging the business to grow its use of the product. “We are trying to move some more services to Google Cloud Platform because it is very stable and scalable,” Li says. The business is keen to explore the capabilities of Cloud Pub/Sub to provide low latency messaging between applications, Cloud Spanner to deliver a distributed relational database service, and Cloud Dataflow to transform and enrich data in stream and batch modes.
“We are extremely pleased with Google Cloud Platform and Firebase,” Li concludes. “We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”
Embedded Intelligence Helps Businesses Prepare for the Unknown
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The disruptions of 2020 elevated the importance of having the right data and insights to pivot quickly when necessary. Here’s a look at how businesses can use embedded intelligence to prepare for uncertainty and meet ever-changing customer expectations.
At work and in life, some unpredictability is always a part of the package. Ten years ago, your business might have experienced sudden product demand or a system outage that slowed down deliveries. Servers or data platforms might have run out of capacity earlier than expected.
In 2020, though, the concept of unpredictability in business reached new heights. These disruptions have elevated the importance of embedded intelligence—that is, having machine learning built into the tools people use every day, so that when a pivot is necessary, everyone has the data and insights they need at their fingertips. In fact, 2020 was so disruptive, with so many changes in customer behavior and so many ripple effects, a lot of historical data and forecasting assumptions may not be helpful in 2021. This only increases the onus on businesses to make the freshest data actionable for more of the workforce.
A decade or so ago, the idea of embedded intelligence might have seemed like science fiction. You might remember hearing that analytics would be able to make predictions, and that technology would be able to take on the complex work of predicting retail demand or helping to create a responsive supply chain. But insufficient hardware, older architecture models, slow queries, and untrustworthy data often got in the way.
Now, that concept has become reality as enterprise decision-making has moved from legacy tools to cloud-powered data intelligence services. Today, it’s possible to perform complex analytics tasks and obtain valuable, trusted outputs much faster than ever before. That speed and scale has allowed businesses to tackle entirely new projects and release new features and products very quickly. In addition, APIs have become a lot more intelligent, making it easy to connect siloed solutions. No matter the industry, businesses can access the technology to get to the bottom of what customers need.
Related: Top 5 trends for API-powered digital transformation in 2021
Meeting ever-changing customer expectations with embedded ML
Bringing embedded analytics to real-world uses continues to evolve, with a number of inspiring examples surfacing in the past year. As a result of the pandemic and shifting public health guidelines, many businesses didn’t know month by month if they’d be interacting with customers primarily through in-person or digital channels. And even if both channels were available, it wasn’t obvious how changing customer behaviors would net out.
At patient engagement platform Force Therapeutics, for example, daily activity on their virtual care platform went up by over 140% during the pandemic. With such a large influx of incoming data, it would have been difficult—if not impossible—for a team of humans to gather, organize, and draw insights from all of that information, especially in a timely enough manner to be of use to healthcare providers.
To deliver the necessary care when and how it was needed, Force Therapeutics required a machine learning solution that could identify patient needs based on a wide range of data. Using an embedded analytics platform, they created an application that allowed them to monitor the progress of post-op patients, answer questions, or triage concerns remotely. The platform also enabled providers to check for spikes and anomalies, in order to identify patients who needed to come in due to a critical issue.
Amidst all of the disruption, it became clear that teamwork is essential, and that effective teamwork relies on having the right data-driven tools to get the job done.
Likewise, home delivery became a bigger part of consumers’ routines. This increased pressure on companies to adapt quickly to changes that might prevent packages from arriving on time, such as worsening weather conditions or upstream supply chain disruptions. Amidst all of the disruption, it became clear that teamwork is essential, and that effective teamwork relies on having the right data-driven tools to get the job done.
One example of this can be seen in Google Cloud customers who are using public data to accelerate their journey from data to actionable insights. Some retailers are utilizing the Google Cloud Public Datasets Program to leverage NOAA’s Global Surface Summary of the Day (GSOD) and Severe Weather Data Inventory datasets in order to better understand disruptive weather events, reroute their supply chains to prevent disruptions, and predict their in-store inventory needs to support communities as they recover from natural disasters.
Implementing ML without the complexity
The idea of embedded ML has been hyped for years, but for many use cases, the status quo tools have not caught up to the enthusiasm. Many business intelligence tools rooted in older database architectures require intense engineering work to deliver insights, queries are often slow, and the output is not always consistent or accurate. Part of the challenge is that building ML pipelines is difficult. Data in a database or data warehouse typically needs to move to an intelligence platform so models can be trained, and the models then need to be deployed and integrated into business workflows.
But modern data warehouses such as BigQuery let users train models in the warehouse itself, without having to move the data—and once the models are created, they can be applied and integrated into business processes using simple SQL. When it comes to embedding ML into enterprise processes, these modern approaches significantly lower the barrier for entry. Plus, tools like Looker, Google Cloud’s platform for modern BI and data applications, were created specifically for modern data needs, with the assumption that data needs would constantly evolve and that iterations should be made quickly without eating up inordinate engineering resources.
For Commonwealth Care Alliance (CCA), Looker was originally implemented to alleviate their pain points around data bottlenecks and data chaos. But when the pandemic hit, the nonprofit, community-based healthcare organization pivoted to make use of Looker’s tools to better serve patients. CCA used BigQuery and Looker to combine numerous datasources, create a predictive model that assesses risk, and distribute that model to its clinicians. This has given response teams the insights to determine who is too high risk to come in for care so they can reach out with home care solutions.
This kind of agility is not a one-time antidote to a one-time disruption, but rather the norm to which organizations must aspire if they want to remain competitive and protect themselves against future disruptions.
This same functionality is also helping businesses like SoundCommerce. Retailers like Constellation Brands, Eddie Bauer, and FTD/ProFlowers use SoundCommerce’s out-of-the-box data platform, which is powered by BigQuery and Looker, to collect retail data from any source and build a model around the metrics and relationships that are most crucial to retail. This has saved brands hundreds of manual reporting hours each month, and reduced platform licensing costs by almost 75%. Just as importantly, during the uncertain times of 2020, brands that used SoundCommerce were able to align real-time and predictive business decisions across marketing and operations with critical retail KPIs like contribution margin and customer lifetime value (CLV).
As 2020 showed us, we can never predict the future—but we can prepare for unpredictability by having the agility to always improve, and by positioning ourselves to make quick, intelligent pivots when the time comes. Last year was in many ways a rubicon: This kind of agility is not a one-time antidote to a one-time disruption, but rather the norm to which organizations must aspire if they want to remain competitive and protect themselves against future disruptions.
Looking for an ‘easy button’ to speed up your BI workloads running on BigQuery? Check out our latest announcement about BI Engine on the Google Cloud Blog.
Debanjan Saha is GM of Data Analytics at Google Cloud, where he leads the strategy and execution of analytics services in GCP. Prior to joining Google, Debanjan was VP of Amazon Aurora and RDS at Amazon Web Services. Earlier in his career Debanjan held multiple executive and technical leadership positions at IBM and Tellium, an optical networking pioneer that he helped grow from an early stage start-up to a public company.
Debanjan is a Fellow of the IEEE and a Distinguished Scientist of the ACM. He has co-authored a book, 50 patent applications, and numerous technical articles including award winning papers and Internet standards. He received MS and PhD degrees from the University of Maryland, and a B.Tech from IIT, all in Computer Science. In 2019, Business Insider named him as one of the top 10 technology executives transforming business.
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Measuring and Improving Speech-to-Text Accuracy
Google Cloud’s Speech-to-Text API has a large number of uses including making customer service teams more effective and increasing their ability to improve customer experience.
Google Cloud’s Speech-to-Text API provides incredible accuracy out of the box. What many might not know is that it also has new tools for enhancing accuracy and customizing the model for your industry, domain, or use case.
In this video, Calum Barnes, Product Manager, Google Cloud, offers an overview of Google Cloud’s Speech-to-Text abilities, then he talks about how you can measure the accuracy of speech to text on your own data. He also discusses what you can do using Google Cloud tools to improve your Speech-to-Text accuracy levels.
Come learn how Google measures accuracy and how you can use its tools to customize your model and improve accuracy. Barnes will walk you through the basic concepts and introduce a lab that you can complete later on your time.
Headless e-Commerce is the Next Big Thing in Retail
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Headless Ecommerce
In the last couple of years there has been a shift in the way retailers approach ecommerce: where in the past development efforts were prioritized around building a solid foundation for backend transactions and operations now it is clear that companies in this space are focusing on differentiating themselves by creating unique shopping experiences that increase engagement and reduce friction.
But how can development teams spend the necessary time designing and writing code for this kind of interactions while also having to seamlessly maintain ecommerce vital components like online catalogs, shopping carts and checkout payment processes? Enter headless commerce.
Headless commerce (HC) helps companies of all sizes to innovate, develop and launch in less time and using fewer resources by decoupling backend and frontend. Headless solution providers empower online retailers by offering a balance between flexibility and optimization through pre-built api-accessible modules and components that can be easily plugged into their frontend architecture. This translates into rapid development while keeping desired levels of security, compliance, integration and responsiveness.
This composable approach enables dev teams not only to create new features but also connect other ecommerce components with less effort which is critical when responding to business trends. But above all, the main benefit retailers receive from HC, is owning and controlling the frontend for an engaging customer journey as well as quickly launching new experiences.
Google Cloud + commercetools
commercetools, a leader in the headless commerce space, has partnered with Google Cloud to make their cloud-native SaaS platform available in the Google Cloud Marketplace. With a flexible API system (REST API and GraphQL), commercetools’ architecture has been designed to meet the needs of demanding omnichannel ecommerce projects while offering real flexibility to modify or extend its features. It supports a variety of storefront providers like Vue Storefront, offers a large set of integrations and supports microservice-based architectures. All this while providing access to multiple programming languages (PHP, JS, Java) via its SDK tools.
commercetools and Google Cloud provide development teams with all the tools to build high-quality digital commerce systems. Google Cloud’s scalability, AI/ML components, API management capabilities and CI/CD tools are a perfect fit to build frontend shopping experiences that easily integrate with the commercetools stack. Developers can take advantage of this compatibility by:
- Integrating systems with Google’s Retail Search, Recommendations AI and Vision Product Search
- Injecting serverless functions into commercetools using Google Cloud Functions
- Extending and integrating commercetools via Events handled by Pub/Sub
- Managing 3rd party, legacy, microservices and commercetools APIs with Apigee
- Selecting the Google Cloud region commercetools uses for zero latency for custom apps
- Expanding their microservice ecosystem with components like Cloud Storage, Cloud SQL, Firestore and BigQuery
Additionally, commercetools allows ecommerce solutions to tap into the wider Google Ecosystem by providing authoritative data via Merchant Center, advertising via product listing ads and selling via Google Shopping.
Architecture Overview
As mentioned previously, headless commerce is increasingly preferred by retailers who want to own and control the ‘front-end’ for providing and enabling an engaging and differentiated user and shopping experiences.
The approach involves a loosely coupled architecture that separates ‘front end’ from the ‘back end’ of a digital commerce application. The front end is typically built and managed by the retailer. They want to leverage an independent software vendor (ISV) offered, ready-to-use ‘back-end’ commerce building blocks for capabilities, such as product catalog, pricing, promotions, cart, shipping, account and others.
Most retailers want to invest their time and resources in building a front end that requires an agile development model to introduce new and tweaking existing user experiences to acquire and retain customers. A few retailers that do not have an in-house web development team may choose an ISV that offers ready to use front end. The front end is a web app and designed as a progressive web application (PWA) on Google Cloud. The backend is a headless commerce offered by an ISV, such as commercetools. The backend commerce capabilities are built as a set of microservices, exposed as APIs, run cloud-native and implemented as headless. It is commonly referred to as the “MACH” solution. The API-first approach of the architecture allows easily integrating ‘best of breed’ capabilities built internally and/or offered by 3rd party ISVs.
Leveraging Google Cloud Components
The architecture of the front end will be implemented on Google Cloud and will integrate with the ISV’s headless commerce back end that runs natively in Google Cloud.
The front end will be designed using cloud-native services for
- PWA web app development (Google Kubernetes Engine, CI/CD services),
- Google Product Discovery solution that includes Retail Search and Vision API Product Search for serving product search (text and image) and Recommendations AI for serving recommendations.
- Storage (Cloud Storage), Database (Cloud SQL, Cloud Firestore), and edge caching for content delivery (Cloud CDN)
- Networking (Cloud DNS, Global Load Balancing), and
- Security (Cloud Armor for DDoS, API Defense for API protection)
Additionally, API management (Apigee on Google Cloud) can be used to orchestrate interactions of the front end with the APIs of the backend commerce services. The API management’s capability will be used for accessing the services of on-premises systems, such as ERP, order management system (OMS), warehouse management system (WMS) as needed to support the functioning of digital commerce application. Alternatively, depending on the frontend capabilities, developers can use middleware to build custom services and route requests.
What’s next?
A considerable number of retailers have adopted headless commerce and are now focusing on adopting best practices and leveraging the agility that comes with this approach. Just like commercetools offers robust components that meet the retailer’s backend operational needs (Product Catalog, Order Management, Carts, Payments, etc), Google Cloud’s Compute, Networking, Severless and AI/ML services provide the agility and flexibility required by development teams to quickly and easily extend their frontend capabilities.
commercetools and Google Cloud work seamlessly together because they both prioritize ease of integration, scalability, security and iterability while providing ready-to-use building blocks. It also helps that commercetools backend runs on Google Cloud. Once an initial foundation of Google Cloud and commercetools has been established, adding new commerce modules and extending functionally of the current ones becomes a straightforward process that allows to route efforts to innovation initiatives. In the end, the main beneficiaries of this technical synergy are the shoppers that enjoy experiences which increase engagement and minimize friction.
Alternatively, retailers can also save time and resources by relying on frontend integrations. commercetools offers a variety of third-party solutions that can effortlessly be added to a headless commerce architecture. These integrations as well as other important headless commerce extensions will be explored in future blog entries. In the meantime, all the necessary tools to leverage headless commerce can be found in just one place:
Get started with commercetools on the Google Cloud Marketplace today!
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