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 Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs.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.

We’re all accustomed to Google magic in our daily lives. When we need information like the time a store closes, the best route to a destination, help cooking a favorite recipe — we just ask Google. Google is known for providing useful information everywhere, wherever you are.

But for business, it isn’t so easy to get answers to important questions. In a recent study by HBR, only 30% of respondents report having a well-articulated data strategy and, at most, 24% of respondents said they thought their organization was data-driven. If you asked, most of these executives would probably tell you they’re drowning in data, so how come they’re struggling to be data-driven?

Data’s journey begins in operational databases, where the data is born as users and systems interact with applications. Operational databases are the backbone of applications and are essential ingredients for building innovative customer experiences. At Google Cloud, we have been focused on building a unified and open platform that provides you the easiest and fastest way to innovate with data. Google’s data cloud enables data teams to manage each stage of the data lifecycle from operational transactions in databases to analytical applications across data warehouses, lakes, data marts, and to rich data-driven experiences.

For example, Merpay, a mobile payment service in Japan, wanted to build its experience on a database that delivered on requirements around availability, scalability and performance. By using Cloud Spanner, Merpay was able to reduce overhead costs and ultimately devote engineering resources to developing new tools and solutions for their customers.

If you’re looking to bring the same kinds of transformation to your organization, let’s dive into the latest database announcements and how they can help you do that:

Tap into transformative database capabilities

Google’s data cloud allows organizations to build transformative applications quickly with our one-of-a-kind databases such as Cloud Spanner, Google Cloud’s fully managed relational database with global scale, strong consistency, and industry leading 99.999% availability. Leading businesses across industries such as Sabre, Vimeo, Uber, ShareChat, Niantic, and Macy‘s use Spanner to power their mission-critical applications and delight customers. Recent innovations in Spanner such as granular instance sizing and PostgreSQL interface lower the barrier to entry for Spanner, thus making it accessible for any developer and for any application, big or small.

In that effort, we are excited to announce Spanner free trial instances to give developers an easy way to explore Spanner at no cost. The new free trial for Spanner provides a zero-cost, hands–on experience to try out Spanner and comes with guided tutorials and a sample database. The Spanner free trial provides a Spanner instance with 10GB storage for 90 days. Customers can upgrade to a paid instance anytime during the 90-day period to continue exploring Spanner and unlock the full capabilities of Spanner, such as unlimited scaling and multi-region deployments. To learn more about the new free trial, you can read this blog and watch this short video. Start your free trial today in the Google Cloud console.

We’re also excited to announce the preview of fine-grained access control for Spanner that lets you authorize access to Spanner data at the table and column level. Spanner already provides access control with Identity and Access Management (IAM) which enables a simple and consistent access control interface for all Google Cloud services. With fine-grained access control, it’s now easier than ever to protect your transactional data in Spanner and ensure appropriate security controls are in place when granting access to data. Learn more about fine-grained access control in this blog.

Generate exponential value from data with a unified data cloud

Google’s common infrastructure for data management has many advantages. Thanks to our highly durable distributed file systems, disaggregated compute and storage at every layer, and high-performance Google-owned global networking, we’re able to provide best-in-class, tightly integrated operational and analytical data services with superior availability and scale at the best price- performance and operational efficiency.

In that effort, today we’re excited to announce Datastream for BigQuery, now in preview. Developed in close partnership with the BigQuery team, Datastream for BigQuery delivers a unique, truly seamless and easy-to-use experience that enables real-time insights in BigQuery with just a few steps.

Datastream efficiently replicates updates directly from source systems into BigQuery tables in real-time by using BigQuery’s newly developed Change Data Capture (CDC) and Storage Write API’s UPSERT functionality. You no longer have to waste valuable resources building and managing complex data pipelines, self-managing staging tables, tricky DML merge logic, or manual conversion from database-specific data types into BigQuery data types. Just configure your source database, connection type, and destination in BigQuery and you’re all set.

Klook, a Hong Kong-based travel company, is one industry-leading enterprise using Datastream for BigQuery to help drive better business decisions. “Prior to adopting Datastream, we had a team of data engineers dedicated to the task of ingesting data into BigQuery, and we spent a lot of time and effort making sure that the data was accurate,” said Stacy Zhu, senior manager for data at Klook. “With Datastream, our data analysts can have accurate data readily available to them in BigQuery with a simple click. We enjoy Datastream’s ease of use and its performance helps us achieve large scale ELT data processing.”

Achievers, an award-winning employee engagement software and platform, recently adopted Datastream, as well. “Achievers had been heavily using Google Cloud VMs (GCE), and Google Kubernetes Engine (GKE)” says Daljeet Saini, lead data architect at Achievers. “With the help of Datastream, Achievers will be streaming data into BigQuery and enabling our analysts and data scientists to start using BigQuery for smart analytics, helping us take the data warehouse to the next level.”

To make it easier to stream database changes to BigQuery and other destinations, Datastream is also adding support for PostgreSQL databases as a source, also in preview. Datastream sources now include MySQL, PostgreSQL, AlloyDB for PostgreSQL, and Oracle databases, which can be hosted on premises, on Google Cloud services such as Cloud SQL or Bare Metal Solution for Oracle, or anywhere else on any cloud.

Key use cases for Datastream include real-time analytics, database replication via continuous change data capture, and enablement of event-driven application architectures. In real-world terms, real-time insights can help a call center provide better service by measuring call wait times continuously, rather than retrospectively at the end of the week or month. And retailers or logistics companies that do inventory management based on real-time data can become far less wasteful than if it were based on periodic reports.

Accelerate your modernization journey to the cloud

In recent years, application developers and IT decision makers have increasingly adopted open-source databases to ensure application portability and extensibility and prevent lock-in. They’re no longer willing to tolerate the opaque costs or the overpriced and restrictive licensing of legacy database vendors.

In particular, PostgreSQL has become the emerging standard for cloud-based enterprise applications. Many organizations are choosing to standardize on PostgreSQL to reduce the learning curve for their teams and avoid the lock-in from the previous generation of databases. Google’s data cloud offers several PostgreSQL database options including AlloyDB for PostgreSQL, a PostgreSQL-compatible database that provides a powerful option for migrating, modernizing, or building commercial-grade workloads

To help developers and data engineers modernize and migrate their applications and databases to the cloud, we’re pleased to announce, in preview, that our Database Migration Service (DMS) now supports PostgreSQL migrations to AlloyDB. DMS makes the journey from PostgreSQL to AlloyDB easier and faster, with a serverless migration you can set up in just a few clicks. Together with Oracle to PostgreSQL data and schema migration support, also in preview, DMS gives you a way to modernize legacy databases and adopt a modern, open technology platform. Learn more about PostgreSQL to AlloyDB migration with DMS in this blog.

Among organizations adopting PostgreSQL is SenseData. A platform created to improve the relationship between companies and customers, SenseData is a market leader in Latin America in the field of Customer Success.

“At Sensedata, we’ve built our customer success platform on PostgreSQL, and are looking to increase our platform performance and scale for the next phase of our growth,” said Paulo Souza, co-founder and CTO of SenseData. “We have a mixed database workload, requiring both fast transactional performance and powerful analytical processing capabilities, and our initial testing of AlloyDB for PostgreSQL has given impressive results, with more than a 350% performance improvement in our initial workload, without any application changes. We’re looking forward to using Database Migration Service for an easy migration of multiple terabytes of data to AlloyDB.”

CURA Grupo is one of Brazil’s largest medical diagnostics conglomerates with over 1,600 employees, 500 qualified physicians, and 6 million examinations conducted every year. With more than 30,000 examinations performed every day, storing the database on premises was becoming increasingly unfeasible. CURA Grupo used Database Migration Service to migrate to Google Cloud, along with synchronization between their on-prem server’s database and the cloud. The result was an easy transition with just around 20 minutes of downtime.

To learn more about these exciting innovations and more, join us at Next 22 happening on October 11-13. You will also hear from customers such as PLAID, Major League Baseball, DaVita, CERC, Credit Karma, Box, and Forbes who are all innovating faster with Google Cloud databases.

At HSBC, we serve 39 million customers, in-person and online, from consumers to businesses, in 66 countries. We maintain data centers in 21 countries, with more than 94,000 servers. With an on-premises infrastructure supporting our business, we kept running into capacity challenges, which really became an innovation blocker and ultimately a business constraint.

Our teams wanted to do more with data to create better products and services, but the technology tools we had weren’t letting us grow and explore. And that data was growing continually. Just one of our data warehouses had grown 300% from 2014 to 2018.

We had a huge amount of data, but what’s the point of having all that data if we couldn’t get insights and business value from it? We wanted to serve our customers flexibly, in the ways that work best for them. 

We knew moving to cloud would let us store and process more data, but as a global bank, we were moving complex systems that needed to also be secure.

It was a team effort to create the project scope and strategy up front, and it paid off in the end. Our cloud migration now enables us to use an agile, DevOps mindset, so we can fail fast and deliver smaller workloads, with automation built in along the way.

This migration also helped us eliminate technical debt and build a data platform that lets us focus on innovation, not managing infrastructure. Along the way, we invented new technology and built processes that we can use as we continue migrating.

Planning for a cloud move
We chose cloud migration because we knew we needed cloud capabilities for our business to really reach its digital potential. We picked Google Cloud, specifically BigQuery, because it’s super fast over small and large datasets, and because we could use both a SQL interface and Connected Sheets to interact with it.

We had to move our data and its schema into the cloud—without having to manually manage every detail and miss the timelines we had set. Our data warehouse is huge, complex, and mission-critical, and didn’t easily lend itself to fit into existing reference architectures. We needed to plan ahead and automate to make sure the migration was efficient, and to ensure we could simplify data and processes along the way.

The first legacy data warehouse we migrated had been built over a period of 15 years, with 30 years worth of data comprising millions of transactions and 180 TB of data.

It ran 6,500 extract, transform, load (ETL) jobs and more than 2,500 reports, getting data from about 100 sources. Cloud migration choices usually involves either re-engineering or lift-and-shift, but we decided on a different strategy for ours: move and improve.

This allowed us to take full advantage of BigQuery’s capabilities, including its capacity and elasticity, to help solve our essential problem of capacity constraints. 

Taking the first steps to cloud
We started creating our cloud strategy through a mapping exercise, which also helped start the change management process among internal teams. We chose architecture decision records as our migration approach, basing those on technical user journeys, which we mapped out using an agile board. User journeys included things like “change data capture,” “product event handling,” or “slowly changing dimensions.” 

These are typical data warehouse topics that have to be addressed when going through a migration, and we had others more specific to the financial services industry, too.

For example, we needed to make sure the data warehouse would have a consistent, golden source of data at a specific point in time. We considered business impacts as well, so we prioritized initially moving archival and historical data to immediately take load off of the old system.

We also worked to establish metrics early on and introduce new concepts, like managing queries and quotas rather than managing hardware, so that data warehouse users would be prepared for the shift to cloud.

To simplify as we went, we examined what we currently had stored in our data warehouse to see what was used or unused. We worked with stakeholders to assess reports, and identified about 600-plus reports that weren’t being used that we could deprecate. We also examined how we could simplify our ETL jobs to remove the technical debt added by previous migrations, giving our production support teams a bit more sleep at night. 

We used a three-step migration strategy for our data: first, migrating schema to BigQuery; second, migrating the reporting load to BigQuery, adding metadata tagging and performing the reconciliation process; and third, moving historical data by converting all the SQL script into data into BigQuery-compliant scripts. 

Creating new tools for migration automation
In keeping with our automation mantra, we invented multiple accelerators to speed up migration. We developed these to meet the timelines we’d set, and to eliminate human error. 

The schema parser and data reconciliation tool helped us migrate our data layer onto BigQuery. SQL parser helped migrate the data access layer onto Google Cloud Platform (GCP) without having to individually migrate 3,500 SQL instances that don’t have data lineage or documentation. This helped us to prioritize workloads.

And the data lineage tool identified components across layers to find dependencies. This was essential for finding and eliminating integration issues during the planning stage, and for identifying application owners during the migration. Finally, the data reconciliation tool reconciles any discrepancies between the data source and the cloud data target. 

Building a cloud future
We used this first migration in our UK data center as a template, so we now have a tailored process and custom tools that we’re confident using going forward.

Our careful approach has paid off for our teams and our customers. We’re enjoying better development and testing procedures. We’ve created an onboarding path for applications, we have a single source of truth in our data warehouse, and we use authorized views for secure data access. The flexibility and scalable capacity of BigQuery means that users can explore data without constraints and our customers get the information they need, faster. 

Today, we are excited to announce the general availability of Dataplex data lineage — a fully managed Dataplex capability that helps you understand how data is sourced and transformed within the organization. Dataplex data lineage automatically tracks data movement across BigQuery, BigLake, Cloud Data Fusion (Preview), and Cloud Composer (Preview), eliminating operational hassles around manual curation of lineage metadata. 

With rising data volume spread across data silos, it can be challenging for organizations to ensure users have a self-service mechanism to discover, understand and trust the data. Organizations constantly struggle with questions such as:

• Is the data extracted from an authoritative source?
• What is the impact if I drop this table?
• The data in this table seems corrupted – where did this data come from, and when was it last refreshed?
• How is sensitive information being moved or copied? Is it in adherence to data governance practices?

To answer the above questions, organizations need to track how data is sourced and transformed, which can be complex and requires significant effort.

Dataplex data lineage describes each lineage relationship by detailing what happened and when it happened in an interactable lineage graph, providing data observability.

Data analysts who want to know if a table originates from an authoritative source can now answer this in a self-service manner with a simple look-up of lineage for the concerned table — available in Dataplex and in BigQuery for in-context analysis. 

Data engineers can reduce time to identify and resolve data issues through root cause analysis using the operational metadata trace asserting a lineage relationship. Data lineage also aids deterministic change management by providing the ability to evaluate the impact of a change and collaborate with the corresponding stakeholders to minimize any adverse impact. 

Finally, data lineage provides a map of data movement which can become the foundation for data governance practice. It enables data stewards and owners to evaluate and enforce adherence to governance requirements, especially when tracking the movement of sensitive information. 

Dataplex data lineage provides APIs for extensibility so that organizations can report lineage from various systems and have a single map of how data entries are related.

What our customers are saying

L’Oréal, the world’s largest cosmetics company, is on a mission to ‘create the beauty that moves the world.’ “Dataplex data lineage helps us understand how data moves across our organization,” said Sébastien Morand, Head of Data Engineering team, L’Oréal. “As a fully managed solution, it becomes the main entry point to diagnose data issues and evaluate the impact of a change or incident — providing insight on what happened and when it happened, including reference to the execution metadata. Directly integrated into our beauty tech data platform, data lineage helps us reduce data issues and also enables us to mitigate issues faster when it does happen.” 

“At Wayfair, we treat data-as-a-product and are building a robust data platform that provides self-service access and compliance constructs,” said Vinit Rajopadhye, Associate Director on Data Infrastructure & Data Enablement at Wayfair. “We are excited about Dataplex data lineage as it helps our data consumers trust data based on where it originates and the transformations applied.”

Hurb is an online travel agency in Brazil with a mission to optimize travel through technology. “Hurb has a rapidly growing data platform, with new data assets created and registered daily to support business decision-making and Machine Learning models,” said Vinícius dos Santos Mello, Senior Data Engineer. “Thanks to Dataplex data lineage features, we have end-to-end data observability across data in BigQuery. We can proactively address schema changes, data quality issues, and asset depreciation that could otherwise negatively affect the business.”

“As a company with many business domains and services, we handle a large volume of  data and use it to power our decision making, so it is crucial to ensure data quality. Dataplex data lineage provides a visual understanding of the flow of data across our organization, improving efficiency of impact investigations when problems occur and increasing the reliability of the data.” said Mitsunori Fukase, Data Platform Department Group Manager, DeNA.

Get started with Dataplex data lineage

You can get started with Dataplex data lineage by enabling the Data Lineage API on your project. You can learn more here.

Additional Resources:

• Dataplex data lineage labs
• Quickstart – track lineage for BigQuery table copy