The home lending journey entails processing an immense number of documents daily from hundreds of thousands of borrowers. Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs. Scaling a business that sorts through millions of documents daily, while increasing efficacy and accuracy, is no small feat. When it comes to applying for a mortgage loan, consumers expect a digital experience that’s as good as the in-person one. Roostify simplifies the home lending journey for lenders and their customers.

No time to spare: Overcoming document processing challenges with AI

Roostify provides enterprise cloud applications for mortgage and home lenders. In order to empower its customers to deliver a better, more personalized lending experience, they needed to automate and scale their in-house document parsing functionality.

As a key component of its document intelligence service, Roostify is leveraging Google Cloud’s Lending DocAI machine learning platform to automate processing documents required during a home loan application process, such as tax returns or bank statements with multi-language support. This partnership delivers data capture at scale, enabling Roostify customers to automatically identify document types from the uploaded file and to extract relevant entities such as wages, tax liabilities, names, and ID numbers for further processing, and make things move faster in the cumbersome lending process. 

Roostify’s solutions leverage Google Cloud’s Lending DocAI, which is built on the recently announced Document AI platform, a unified console for document processing. Customers can easily create and customize all the specialized parsers (e.g., mortgage lending documents and tax returns parsers) on the platform without the need to perform additional data mapping or training. All Google Cloud’s specialized parsers are fine-tuned to achieve industry-leading accuracy, helping customers and partners confidently unlock insights from documents with machine learning. Learn more about the solution from the GA launch blog and the overview video.

Integrating Lending DocAI’s intelligent document processing capabilities into the Roostify platform means more innovation for their customers and tangible results: faster loan processing times, fewer document intake errors, and lower origination costs. Additional support in Google Lending DAI for other languages and more documents like global Know Your Customer (KYC) documents or payroll reports is in the near future.

Full integration of AI solutions

Working together with Roostify’s platform team, we were able to help them solve their document processing challenge through integration of various GCP products such as Lending DocAI (LDAI), Data Loss Prevention (DLP) for redacting sensitive data, BigQuery for data warehousing and analytics, and Firestore for API status. To make it very safe and secure, all data was encrypted end-to-end at Rest and in Transit. LDAI won’t require any training data to process. It is an easy plug and play API.

Here is a sneak peek in the high level deployment architecture for LDAI in Roostify environment:

Here are the steps for processing data:

1. Receives document processing request from the client.
2. API Function directs requests to the pre-processing service. For Async requests a processing ID is generated and returned to the caller.
3. Pre-processing service sends the request for further processing (Long/short PDF conversion), calling other microservices and receives back the responses. Any error in the response received is then sent to the response processing service. 
4. If the response is synchronous, the pre-processing service directs it to the LDAI Invoker service. 
   1. If the response is asynchronous, the pre-processing service feeds it into the Cloud Pub/Sub service.
5. Cloud Pub/Sub service feeds the response back to the LDAI Invoker service.
6. LDAI Invoker service routes the request to the Google LDAI API for classification if there are multiple pages in the document.
7. Document will be split based on LDAI response and then saved in a GCS bucket for temporary storage.
8. LDAI entity interface for single page processing and then LDAI Invoker sends LDAI results to LDAI Response Processing
9. If a request is a synchronous request the LDAI Response Processor sends results to the API Function so that it can complete the synchronous call and respond to the rConnect caller.
   1. If the request is an asynchronous request the LDAI Response Processor will respond to the caller’s webhook and complete the transaction.
10. Finally, Data stored in the GCP bucket will be deleted.

All the responses that come from the LDAI API can optionally feed into BigQuery via the Response Processor, after parsing it through Data Loss Prevention (DLP) API to redact the PII/sensitive information.  Throughout the processing of both asynchronous and synchronous requests all transactions are logged using Cloud Logging.  For asynchronous transactions, the state is maintained throughout the process using Cloud Firestore.

Roostify currently uses this technology to power two different solutions: Roostify Document Intelligence and Roostify Beyond™. Roostify Document Intelligence is a real-time document capture, classification, and data extraction solution built for home lenders. It ingests documents uploaded by borrowers and loan officers, identifies the relevant documents, and extracts and classifies key information. Roostify Document Intelligence is available as a standalone API service to any home lender with any digital lending infrastructure already in place. 

Roostify Beyond™ is a robust suite of AI-powered solutions that enables home lenders to create intelligent experiences from start to close. It combines powerful data, insightful analytics, and meaningful visualization to streamline the underwriting process. Roostify Beyond™ is currently available only to Roostify customers as part of an Early Adopter program and will be rolled out to the market later this year.

Through this partnership, Roostify has enabled its customers to adopt a data-first approach to their home lending processes, which will lead to improved user experiences and significantly reduced loan processing times.

Fast track end-to-end deployment with Google Cloud AI Services (AIS)

Google AIS (Professional Services Organization), in collaboration with our partner Quantiphi, helped Roostify deploy this system into production and fast-tracked the development multifold to generate the final business value.

The partnership between Google Cloud and Roostify is just one of the latest examples of how we’re providing AI-powered solutions to solve business problems.

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.

We also open sourced the code on this Github repository, if you’re interested in learning more about this application.

In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report. 

First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax. 

Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:

  - invoke_document_ai:
    call: googleapis.documentai.v1.projects.locations.processors.batchProcess
    args:
        name: ${"projects/" + project + "/locations/eu/processors/" + processorId}
        location: "eu"
        body:
            inputDocuments:
                gcsPrefix:
                    gcsUriPrefix: ${bucket_input + report_id}
            documentOutputConfig:
                gcsOutputConfig: 
                    gcsUri: ${bucket_output + report_id}
            skipHumanReview: true
    result: document_ai_response

Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.

A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:

  - create_callback:
    call: events.create_callback_endpoint
    args:
        http_callback_method: "POST"
    result: callback_details
...
- await_callback:
    try:
        call: events.await_callback
        args:
            callback: ${callback_details}
            timeout: 3600
        result: callback_request
    except:
        as: e
        steps:
            - update_status_to_error:
              ...

In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.

Going further 

If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.

At Universe, we serve customers day and night and are always working to make sure they have a great experience, whether online or at one of our live events.

Our technology has to make that possible, and our legacy systems weren’t cutting it anymore. What we needed was a consistent, reliable infrastructure that would help our internal teams provide a fast and innovative ticket-buying experience to customers.

With our data well-managed, we could free up time for our developers to bring new web features to customers, like tailored add-ons at checkout.

Our team of about 20 software engineers needed more flexibility and agility in our infrastructure; we were using various data processing tools, and it wasn’t easy to share data across teams so that everyone saw the same information.

We also needed to incorporate streaming data into the data warehouse to ensure the consistency and integrity of data that’s read in a particular window of time from multiple sources. Our developer teams needed to be able to ship new features faster, and the data back ends were getting in the way.   

In addition, when GDPR regulations went into effect, we needed to make sure all our data was anonymized, and we couldn’t do that with our legacy tools.

Finding the right data tools for the job
To make sure our customers were getting a top-notch online experience, we had to make the right technology choices.

Our first step was to centralize multiple data sources and create a single data warehouse that could serve as the foundation for all of our reporting requirements, both internal and external.

The new technology infrastructure we built had to let us move and analyze data easily, so our teams could focus on using that data and insights to better serve our customers.

Previously, we had lots of siloed systems and applications running in AWS. We did a trial using Redshift, but we needed more flexibility than it offered in how we loaded historical data into our cloud data warehouse. Though we were using MongoDB Atlas for our transactional database, it was important to continue using SQL for querying data.  

The trial task that really sold us on BigQuery was when we wanted to alter a small table that had about 20 million rows, used for internal reporting.

We needed to add a value, but our PostgreSQL system wouldn’t allow it. Using Apache Beam, we set up a simple pipeline that moved data from the original source into BigQuery to see if we could add the column there.

BigQuery ingested the data and let us add the new value in seconds. That was a significant moment that led us to start looking at how we could build end-to-end solutions on Google Cloud. BigQuery gave us multiple options to load our historical data in batches and build powerful pipelines. 

We also explored Google Cloud’s migration tools and data pipeline options. Once we saw how Cloud Dataflow worked, with its Apache Beam back-end, we never looked back. Google Cloud provided us with the data tools we needed to build our data infrastructure. 

Cloud for data, and for users
Introducing new technologies isn’t always simple—companies sometimes avoid it altogether because it’s so hard. But our Google Cloud onboarding process has been easy. 

It took us less than two months to fully deploy our BigQuery data warehouse using the Cloud Dataflow-Apache Beam combination.

Moving to Google Cloud brought us a lot of technology advantages, but it’s also been hugely helpful for our internal users and customers. The data analytics capabilities that we’re now able to offer users has really impressed our internal teams, including developers and DevOps, even those who haven’t used this type of technology before.

Some internal clients are already entirely self-service. We’ve hosted frequent demos, and also hosted some “hack days,” where we share knowledge with our internal teams to show them what’s possible. 

We quickly found that BigQuery helped us solve scale and speed problems.

One of our main pain points had been adding upsell opportunities for customers during the checkout process. The legacy technology hadn’t allowed us to quickly reflect those changes in the data warehouse. With BigQuery, we’re able to do that, and devote fewer resources to making it happen. We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.

We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.

Two features in particular that we implemented using BigQuery have helped us improve the performance of our core transactional database. First, using Cloud Dataflow to convert raw MongoDB logs to structured rows under a BigQuery table, which we can then query using SQL to identify slow or underperforming queries. Second, we can now query multiple logging tables using wildcards, since we load Fastly logs to BigQuery. 

Along with MongoDB Atlas as our main transactional database, much of our infrastructure now runs as Google Cloud microservices using Google Kubernetes Engine (GKE), including the home page and our payment system. Kubernetes cron jobs power background scheduled jobs, and we also use Cloud Pub/Sub. Cloud Storage handles any data storage if any space constraints emerge. 

Our overall performance has increased by about 10x with BigQuery. Both our customers and internal clients, like our sales and finance teams, benefit from the new low-latency reporting. Reports that used to be weekly or monthly are now available in near-real time. It’s not only faster to read records, but faster to move the data, too.

We have Cloud Dataflow pipelines that write to multiple places, and the speed of moving and processing data is incredibly helpful. We stream in financial data using Cloud Dataflow in streaming mode, and plan to have different streaming pipelines as we grow. We have several batch pipelines that run every day. We can move terabytes of data without performance issues, and process more than 100,000 rows of data per second from the underlying database. It used to take us a month to move that volume of data into our data warehouse. With BigQuery, it takes two days.

We’re also enjoying how easy and productive these tools are. They make our life as software engineers easier, so we can focus on the problem at hand, not fighting with our tools. 

What’s next for Universe
Our team will continue to push even more into Google Cloud’s data platform. We have plans to explore Cloud Datastore next. We’re also moving our databases to PostgreSQL on GCP, using Cloud Dataflow and Beam. BigQuery’s machine learning tools may also come into play as Universe’s cloud journey evolves, so we can start doing predictive analytics based on our data. We’re looking forward to gaining even more speed and agility to meet our business goals and customer needs.    

Enterprise agility and the ability to innovate, adapt and respond quickly to the ever-changing risk and regulatory landscape is no longer a choice, but the cornerstone of successful digital transformation and commercial growth. Traditional access to and ways of managing data invariably create challenges in dealing with multiple data repositories, reconciliations, fire-drills, etc.

In response, the move to cloud is increasing significantly. It enables risk analytics and regulatory reporting at scale in a secure environment with data storage, management and encryption capabilities as a standard. In addition, as regulatory reporting requirements become more granular, machine learning can help facilitate new insights and allow for risk management to become more embedded into operational processes.

This webinar will address the day-to-day challenges in risk management and regulatory compliance, while also exploring how technological innovations can provide massive improvements and potential.

Key themes

• Real-life data challenges in the eyes of risk managers: can compliance, fraud detection and identifying liquidity positions be improved through the use of AI?
• Innovative approaches to streamline regulatory reporting to derive deeper customer insights from data at the moment of truth.
• Reimagining operations: how to modernise the data infrastructure to accommodate data explosion, drive flexibility and deliver a more cost effective outcome.