About Bank BRI

Bank Rakyat Indonesia is one of the largest banks in Indonesia and is committed to increasing financial inclusions among un-banked Indonesians. Bank BRI specializes in using modern digital banking to facilitate microfinance lending across its network of over 10,000 branches and thousands of branchless agents.

Google Cloud Results

• Contributes $50M in revenue through the Apigee monetization feature
• Wins recognition for best digital bank in Indonesia from The Asian Banker in 2019
• Achieves ISO 27001 information security for open APIs, earning distinction as the only bank in ASEAN to receive certification to date
• Reduces partner onboarding from 6 months to under 1 hour with Apigee developer portal

Bank Rakyat Indonesia is making waves in Asia with its award-winning digital strategy. As a government-owned bank, Bank BRI is dedicated to changing the lives of its customers through accelerating financial inclusion across Indonesia. With an aggressive target of 84 percent of Indonesians participating in the banking system by 2022, Bank BRI is leapfrogging fintech competition thanks to its innovative digital strategy that has APIs at its core. By the end of 2019, Bank BRI expects to have reached a 70 percent financial inclusion rate among the country’s population, in part due to the adoption of the Cloud Apigee API Management Platform as the bank’s digital nucleus.

Transforming a legacy into a digital future

In the banking business, trust is essential, not only between the bank and its customers, but also between the bank and its partners. Recognizing that gaining and maintaining this trust would be key to customer and partner adoption of the new Bank BRI products and services, the bank decided to pursue ISO 27001 certification in 2018, becoming the first bank in ASEAN (the Association of Southeast Asian Nations) to become certified as information security compliant. Now the international community of partners and customers who use the bank’s APIs have yet another reason to place their trust Bank BRI.

“Apigee has become the central nervous system for all communications between the digital core banking, the microservices, the frontend, and the apps. Apigee has become our sun. Everything rotates around Apigee.”—Kaspar Situmorang, Executive Vice President, Bank Rakyat Indonesia

Kaspar Situmorang, Executive Vice President of Bank BRI, had the original vision for how the bank could transform itself into a fintech with digital technology and APIs. His team got started by implementing a web-native frontend over a new technology stack with Apigee as a second layer. This was a big change from the legacy technologies that existed when Situmorang joined the bank in 2017. Previously all of the bank’s products had their own public APIs, which were very difficult to manage, secure, and monetize.

Since deploying Apigee, it’s become much easier to manage the entire API lifecycle. Situmorang’s digital bank team of 15 uses the Apigee monetization and developer portal features while managing and securing APIs and conducting big data integrations. Apigee has become Bank BRI’s center of communications, handling all transactions between the bank and third parties.

Whereas previously it could take up to six months to onboard a new partner using host-to-host and VPN technology, now it takes less than an hour for partners to self-onboard using the Bank BRI Apigee developer portal. On the portal, partners can register, browse APIs, test in the sandbox, and go into production — all in less than an hour.

“Apigee has become the central nervous system for all communications between the digital core banking, the microservices, the frontend, and the apps. Apigee has become our sun. Everything rotates around Apigee,” says Situmorang.

Increasing financial inclusions

With more than 10,000 offices across Indonesia, Bank BRI has the largest network of any bank in ASEAN. The bank is also the biggest microfinance lender in the region. Though already present in even the most far-flung corners of Indonesia, Bank BRI is still working on increasing financial inclusion among un-banked Indonesians. With 56 million people that haven’t accessed banking services, Indonesia is in the bottom four countries for financial literacy in the world, along with Bangladesh, India, and China. It’s estimated that there is up to $8.3 billion in currency being held outside the banking system.

In order to reach this mostly rural, subset of the population, the bank launched Agent BRILink, a nationwide network of branchless agents. These agents can open new accounts, take deposits, pay out withdrawals, and process and disburse loans in under two minutes with the Pinang microfinance mobile app. To date, over 30,000 customers have received loans through Pinang. Handling its own risk-scoring and automatic payroll deductions for payments has translated into less risky and more profitable loans for Bank BRI.

“Customers download the app and scan their ID, capturing their credit score in just a few seconds. The digital offer letter says how much they’re approved for. They can then accept it, go to the approval screen, and do facial recognition. The money is disbursed immediately. GCP has transformed us into a fintech.”—Kaspar Situmorang, Executive Vice President, Bank Rakyat Indonesia

BRILink agents are bank customers who have been scored highly for reliability by the bank’s big data analyses and who maintain a minimum balance of around $800. Combining this data with the Google Maps API, Bank BRI is able to score all 75.5 million of its customers and identify which of them should be recruited as agents for underbanked areas. Since 2018, the bank has been able to appoint more than 200,000 branchless agents using the Agent BRILink app, eliminating the need for logistically challenging face-to-face meetings. This has resulted in an increase in loan volume from branchless business from $15 billion in 2017 to $26 billion in 2018.

To enable branchless agents to sign up new customers and provide banking services, all they need is a mobile phone and internet service. With many parts of rural Indonesia not covered by commercial 3G, Bank BRI has overcome this hurdle by operating its own satellite. With the connectivity the satellite guarantees, branchless agents can help customers obtain microfinancing and open new shops and businesses in all parts of the country. The satellite also provides internet service across the APAC region wherever the bank operates, from Sri Lanka to New Zealand. While it might seem unusual for a bank to operate a satellite, it’s reflective of Bank BRI’s commitment to reaching its financial inclusion targets and meeting its customers wherever they are.

“Pinang was created to win against fintechs trying to compete against us on speed, cost, and security,” says Situmorang. “The truth is that Indonesian regulators closed about 650 fintechs, mainly in the peer-to-peer lending space, because they were unsafe, too expensive, and very slow.”

Using APIs to create and monetize new products

Another way that Bank BRI is leveraging Google Cloud Platform solutions is through an innovative use of the Cloud Vision API, which enables the bank to integrate with the Indonesian government ID database. Identities of new customers — whether they’re coming in via a branch, a BRI Link Agent, or a mobile app — are automatically verified in seconds through facial recognition. With instant credit scoring and identity fraud concerns essentially eliminated, the bank can make more confident lending decisions.

“Monetization is very important to us. It enables us to define our pricing based on API calls and bill automatically based on usage. We’ve already recognized $50 million through the Apigee monetization feature.”—Kaspar Situmorang, Executive Vice President, Bank Rakyat Indonesia

“Customers download the app and scan their ID, capturing their credit score in just a few seconds,” explains Situmorang “The digital offer letter says how much they’re approved for. They can then accept it, go to the approval screen, and do facial recognition. The money is disbursed immediately. GCP has transformed us into a fintech.”

Bank BRI sees a bright digital future, in part thanks to the API product marketplace it’s creating to serve fintechs. With its digital technologies and massive customer base, the bank is sitting on a treasure trove of big data. Bank BRI already packages this data through more than 50 monetized open APIs for more than 70 ecosystem partners wanting to do credit scoring, business assessments, and risk management. Fintechs, insurance companies, and financial institutions don’t have the talent or the financial resources to do quality credit scoring and fraud detection on their own, so they are turning to Bank BRI.

“Monetization is very important to us. It enables us to define our pricing based on API calls and bill automatically based on usage. We’ve already recognized $50 million through the Apigee monetization feature,” says Situmorang.

Bank BRI is meeting and surpassing the goals it has set for itself for digitalization, increasing financial inclusion, and creating new revenue streams with APIs.

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.

Ocado is the world’s largest online-only grocery store. But Ocado isn’t your typical corner grocery store. For one thing, there’s no corner as we’re an online-only grocery retailer reaching British households, and now with a fast-growing non-food business alongside. Our customers shop online using our award-winning webshop or mobile apps and then their orders are picked and packed in one of our huge automated warehouses, the largest of their kind in the world; hours later, our vans deliver to their kitchens in one-hour delivery slots.

Google Workspace allows us to do things our way, wherever and whenever we want. Our staff use Gmail, Google Calendar and Google Drive to stay on top of their day to day work, and Google+ is helping our teams stay in touch, share information and build local communities. Ultimately, all of these tools help to make sure our customers get the groceries they order, on time and in the best possible condition.

But we didn’t stop there. We looked at other ways we could use Google’s technology to help us run our business and started using Google App Engine for building internal applications. For example, we used App Engine to create a new version of our “Where’s My Order” application, which our customers will soon be using to find out where their orders are within our production cycle. Once their order is on the road, the integration with Google Maps allows them to see exactly where their delivery van is. So we’re taking what used to be a chore, grocery shopping, and making it a simple experience that you can do from the convenience of your own home, or wherever you may be.

Our most recent project was on Google Compute Engine. Within our warehouses there are certain tasks that are repetitive and arduous for humans to do, like picking heavy six packs of bottled water into customer orders. So our robotics team is developing solutions that use robots to automate these sorts of tasks and thus release staff for other more important work. But these robots need sophisticated 3D vision systems to enable them to see what they are doing. These are very computationally intensive applications and by providing the instantly flexible and scalable computing power to crunch all those numbers, Compute Engine provided the perfect solution. And, we are already using the cloud to store and process some of the huge volumes of data that our business spits out every minute.

But with an eye to future growth and international expansion, we have plans to use Compute Engine and Cloud Storage to move other parts of our production systems to the cloud. So walk into an Ocado warehouse in the future and you might run into a robot if you’re not careful. Like I said, we’re not your typical corner grocery store.

*Google Workspace was formerly known as G Suite

Time to go outside again, I guess. I’ll need a sun hat. Sunscreen. Maybe some new sandals? What else?

With the Recommendations AI service, I might be reminded to grab a reusable water bottle and a swimsuit. Or some after-sun aloe lotion. Good thing, cause I’ll need it.

Recommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list. This service is part of our full suite of Retail solutions. When you integrate with the Retail API, you get the benefit of Google’s Product Discovery. Integrating once to reap the benefits over and over. Recommendations is the starting point, and you can easily extend into Retail Search and Vision Product Search in the upcoming future. 

The Recommendations solution is fully managed, global-scale and powered by deep learning, so you can focus on a great shopping experience and let someone else worry about the infrastructure.

Compared to baseline recommendation systems used by customers, Recommendations AI showed double digit uplift in conversion and clickthrough rates in A/B experiments controlled by the customers. You can optimize for click-through, conversion or session revenue, and fine tune the models to make sure you omit out-of-stock items or duplicates, for example.

So how does it work, and how do you get started? Read on, and we’ll walk you through the pipeline, starting with the data you already have to placement in your online store. 

Formula: Data -> Model -> Placement

You start with your catalog, the list of all the things (postcards, movies, pie recipes) that you want to show your customers. Then you ingest your PII-redacted user events -this is the historic event data like home page views, add to cart events and more along with real time user events. This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers’ activity, thus being able to predict what the shopper has a high propensity to purchase next. The user events can come from both online activity across devices or offline store purchases

The recommendation model will return a list of products, which are the recommendations. The brains of the operation, if you will. This model is trained using all the data that you ingest, using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News, that allows us to uncover shopper intent,  so it can best predict the right recommendations to show to the right people.

Every model outputs a list of product identifiers, but where do they go? They go into placements, the spots, panels, carousels on your customer’s journey interacting with your brand that you’ve set aside to highlight recommendations. A model can send recommendations to one or more placements, but each placement only receives information from one recommendation model. Your pages will then need to render the products with the right images, text or other metadata, using the product ID that is returned by the model.

What do recommendations look like?

Let’s start by browsing our postcard-selling website, where I’ve been buying some vintage California postcards already. The recommendations algorithm has caught on to my interest, showing me other potential cards to purchase based on my history:

Put your data to work

To get started we need to bring your data into the recommendation model, so it can understand your customers, your inventory, and your sales patterns. 

The model takes in the product catalog you use, and metadata about those products to better understand nuances in assortment, pricing and variables like size and style. You might already have this data stored in BigQuery or Merchant Center, and hence we provide easy integrations that you can leverage to get started even faster.

As for the user events, don’t worry if you already have systems in place to capture web and mobile activity. We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager, Javascript pixel, or even historic events from Cloud Storage, BigQuery or using inline API or JSON, so you can immediately train the models on this imported  data. All this allows you to kickstart integrating with Recommendations AI in a matter of days.

The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with. Once your data is ready to go, it takes a few days to train the model. Next onto making the data work for you.

Quickly customize your model

Setting up your own recommendations project in the console gives you the ability to choose what sort of model to train (based on what recommendations you want to generate) and your objective. Are you optimizing for click-through rate–more people click on the recommendation links or products–or for conversion rate–more people choose or buy what was suggested or revenue ?

Different models can be optimized for different optimization goals.; the GCP console explains what each one can do and how you can choose to optimize it.

Let’s unpack some of this terminology real quick.

We’ve got three model types:

• Recommended for you – Means we think these are items you’ll want to buy, based on your history; this is usually used on a home page to showcase items.
• Others you may like – Means if you’re browsing the page of a water bottle, we will recommend  alternative brands of water bottles that you may like as well as a backpack, based on your engagement  history.
• Frequently bought together – Means that when anyone buys sunscreen, we notice that they often also buy aloe lotion, so we will surface those items when someone adds any one of them to their cart.

And then we have three business objectives that the models optimize for:

• Click-through rate – How frequently did somebody click on a recommended item?
• Conversion rate– How frequently did somebody add a recommended item to their cart?
• Revenue per session – How much money did the recommendations generate for you?

Deliver anywhere along the journey

Now that you’re all set up in the Retail AI console, you can test out the recommendations in the console, even before you deploy to production.

You can integrate Recommendations into your frontend by calling the Predict APIt. The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations. 

On top of that you can use the recommendations for other parts of your customer’s journey. Email promotions, storefront kiosks, display ads or follow-up notifications can include recommendations based on past activity and cart contents. The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process.

More best practices, and guides, are available inside our documentation.

How to get started

Training your own models can be tedious, time-consuming, and expensive. On top of that it requires deeper data science expertise to set up. Let us do it instead!

You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit..

To get started today you’ll need to make a Cloud project and enable the Retail API, which then allows you to access all the recommendation tools in one menu. Bring in your catalog and purchasing data, define a placement or two, and you can start putting recommendations on your site in a matter of days.