Neo4J & Google Cloud: Graph Data in Cloud to Address Challenges in FinServ Industry
6910
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Over the last decade, financial service organizations have been adopting a cloud-first mindset. According to InformationWeek, lower costs and enhanced scalability were the biggest drivers for cloud adoption in financial services, and cloud-native applications allow access to the latest technology and talent, enabling adopters to rebuild transaction processing systems capable of supporting very high volumes and low latency.
Both Neo4j and Google Cloud have been using relationship-based data representations since the beginning, and we’re dedicated to using this technology to help financial services customers drive business transformation. We are excited about the prospects of financial services (FinServ) cloud systems and believe that graph data in the cloud can help solve significant challenges in the industry.
Data Challenge #1: Risk Management and Compliance
First among the top concerns for any CIO moving to the cloud is risk management and compliance. Disconnected, uncontextualized, or stale data create opportunities for fraud and financial crimes to occur. The fact is when it comes to FinServ, the question is not “if” but rather how often an attack will occur. Unfortunately, incidents have been trending upward over the last decade, and COVID has only exacerbated this reality. Financial crimes affect the bottom line both in the remediation of these crimes and in intangibles like brand value.
Add to this the complexity of international banking, which makes “compliance” a moving target. Penalties due to noncompliance are a constant concern to any FinServ organization.
The tabular representation of information with a fixed number of columns that never change prevents a description of an ever changing world with changing characteristics. Relational databases are great if the world you describe does not move fast but have limitations when data structures are highly interlinked and not homogeneous.
Neo4j Aura on Google Cloud provides a foundation for creating dynamic, futureproof, scalable applications that adhere to the security standards and protocols today’s financial services organizations require to meet the challenges of finding and preventing bad actors. This also includes enterprise scalability; reaching over 1 Billion nodes and relationships to streamline queries and provide solutions that meet regulatory and privacy compliance across geographies. Neo4j has helped some organizations save billions of USD in fraud in the first year of deployment alone.
What makes graph technology the best choice for fraud detection use cases is that the relationships between the data-points are as important as the data-points themselves. Let’s take as an example, one John Smith approaches a multi-national banking institution to manage the primary account for his new holding corporation.
While no one has any record of John R Smith Holdings LLC, the bank’s application built on graph technology understands that there are several well-known entities owned by John Smith Holdings. The application also identifies several well-known board members who bank with this institution. Due to this relationship-driven approach, the bank now understands John R Smith is not “John Smith,” who previously attempted to open an account for his holding corporation, which had no information associated with it prior to two months ago.
Data Challenge #2 Manual Processes and Inefficiencies
The ubiquity of the cloud offers an opportunity to deploy automation at unprecedented levels to tackle the errors and inefficiencies that manual processing allows to creep into processes. When data comes from disparate, perhaps legacy systems – which may have become siloed and “untouchable” over the years – further complexity arises. As an example, if someone in sales types “John Smith” into a CRM system not knowing that John R Smith is the spelling in the customer data master, it may result in two separate and potentially conflicting records. Being able to join those records together in a mastered view helps to solve this problem. In addition, low data quality equates to an increase in risk, costs, and implementation times for new systems.
Neo4j Aura on Google Cloud provides automation and artificial intelligence (AI) that reduces manual processes and the errors that accompany them. In this graph architecture each node, which can represent a person, will have labels, relationships, and properties associated with it. This allows for the use of AI which can easily understand that John Smith in the CRM is the same John R Smith in the customer master. The information contained in Neo4j can be connected bi-directionally to ensure consistency across applications and data sources.
One of the benefits of this approach is that linking information allows organizations to keep the full value of the data, rather than forcing the data into predetermined tabular representations, with the risk of losing valuable information and insights.
Data Challenge #3: Customer Engagement and Insight
Another significant concern is the high expectations today’s customers have for every interaction. End users are accustomed to predictable experiences on their digital devices, and FinServ apps are no exception. Added to this, the “Covid economy” has driven digital adoption significantly across demographics; even among customers who might traditionally have used in-person services. This also equates to increased expectations for personalized, predictable experiences with every digital interaction. We know that latency has always been a key consideration for financial trading, but a recent ComputerWeekly study showed that every financial organization should ensure their visible latency is at 10 milliseconds or less. Customers no longer accept their broadband is at fault.
Finally, blind spots in the customer journey often result in dissatisfaction, which ultimately leads to increased churn. Without gaining actionable insights from your customers, there is no room to innovate and iterate on what they are looking for in your products and services. And this translates to losing market share and competitive advantage.
The NoSQL architecture, specifically the dynamic schema and structure of Neo4j Aura gives you the ability to take charge of your data and make changes according to your development cycles or newer data models. This equates to faster builds, more comprehensive releases and a wider, richer data-set that can be contextualized and understood instantly. Graph technology is the logical choice for building a Customer 360 application. Under this approach organizations not only get valuable insight into the individual client’s behavior and patterns, but also those of their family, friends and colleagues. This allows for stronger personalization, targeted campaigns and successful execution, resulting in increased customer satisfaction and retention levels.
Graph Technology on Google Cloud
Neo4j is a recognized leader in graph database technology and the only fully integrated graph solution on Google Cloud, helping to fill a common need for Google Cloud customers. Both Neo4j and Google Cloud are invested in continuing to grow our partnership and mutual product direction.
You can find and deploy the Neo4j graph database straight from the Google Cloud marketplace, whether you want to download the software for an on-premises deployment, use the virtual machine image, or use the hosted solution, Aura on Google Cloud, the graph database-as-a-service. In any deployment, you get the same enterprise-grade scalability, reliability, and connectivity along with successful, repeatable use cases you can rely on to resolve your particular challenges and integrated billing.
For a real-world example of how graph technology can optimize financial services, you can read our Case Study with fintech Current. Current, a leading U.S. financial technology platform with over three million members, used Neo4j Aura on Google Cloud to create a personalization engine based on client relationships.
To learn more about Neo4j Aura on Google Cloud for FinServ organizations, register for our webinar on Thursday, December 16 with Jim Webber, Chief Scientist, CTO Field Ops at Neo4j and Antoine Larmanjat, Technical Director, Office of the CTO, Google Cloud.
6721
Of your peers have already downloaded this article
4:30 Minutes
The most insightful time you'll spend today!
Unlocking the Power of Data and Creativity with
the Cloud: The WPP Story
5177
Of your peers have already watched this video.
24:40 Minutes
The most insightful time you'll spend today!
Case Study: How Texas’ Largest Grocery Chain Successfully Modernized its Legacy Mainframes
H-E-B, like many enterprises, is moving away from legacy mainframes in favor of microservices and public cloud infrastructure. With hundreds of applications powering their 100+ year-old grocery business (with more than 400 stores in Texas and Mexico), H-E-B needs to be confident that the platform they are building will provide them the agility and security to continue to innovate for their customers.
In this session, the H-E-B engineering team provides details on how they’ve started breaking down their Curbside and Home Delivery monoliths into microservices, why they chose to make Kubernetes a first-class citizen, and why they’re leveraging Anthos as a hybrid cloud platform.
The grocer began to map out a two- to four-year modernization plan in 2017. Initially, the enterprise signed on with Google Cloud and used GKE to move toward a container-first approach to app delivery. Later, it decided to adopt Anthos. Today, Anthos gives H-E-B tighter control over compliance and better proximity to its retail data.
Join the discussion with Joe Rodriguez, Platform Engineering Manager for H-E-B, and. Justin Turner, Sr. Software Engineering Manager for Curbside and Delivery Fulfillment at H-E-B, to learn about the lessons that led to the company’s successful transformation. Find out how Anthos, when deployed on-premises, will expedite their journey to microservices. Learn about the challenges that come with adopting a hybrid modernization strategy and how Anthos plays a critical role in their success in this session.
Explore Google Cloud’s Bi-monthly Technical Learning Series for Innovators in Public Sector
3359
Of your peers have already read this article.
1:30 Minutes
The most insightful time you'll spend today!
Cloud engineers face a constant barrage of new cloud services, products, and innovations. By late 2021, Google Cloud alone had released thousands of new features across hundreds of services. Couple this with other technologies and service releases, and it quickly becomes a herculean task for engineers to navigate, consume, and stay current on the ever changing technology landscape. We have heard from engineers this often leads to anxiety and frustration as engineers struggle to keep up. They are faced with a plethora of training options but often lack the time and funding.
Google Cloud has reinvigorated technical training to make it more informative and applicable to public sector customers and partners. We aim to maximize your training experience so you can get targeted training when you need it. The Google Cloud Public Sector Technical Learning Series addresses customer feedback and provides fun and practical training. Sessions are currently running every two weeks.
“Short and sweet” technical topics geared to subjects you care about
Generic training doesn’t always resonate with public sector technologists. Our new curriculum targets specific public sector use cases, is delivered by customer engineers, and can be accomplished in less than two hours. This means participants can apply the learnings directly to real-life challenges quickly.
Easy to find, easy to enroll
Training opportunities should always be at your fingertips. Our automated training platform will ensure that you only need to enroll once. The system will automatically notify you of upcoming sessions so you can plan in advance and at your convenience. Sessions will be offered on a recurring basis to meet the needs of your organization.
Fun and engaging
Typical training sessions often include a sea of glazed eyes, unresponsive to basic prompts, falling asleep at our desks, we have all been there. But it doesn’t have to be this way. Our goal is to infuse Google culture into our training through interactive exchanges and tangible rewards to keep participants inspired and engaged.
Traditional technology training doesn’t always help you navigate the nuts and bolts of how to effectively introduce a product into an organization. But we know that technology doesn’t operate in isolation; it supports and becomes part of a living organism, managed by humans and confined by other components of an organization’s structure (e.g. existing systems or decentralized business units).
Part of a larger community of like-minded engineers
Learning with – and from – a community of peers is one way to overcome the challenges and complexities of applying new technology within a complex organization. We created the Public Sector Connect community for this very reason. It is one example of how we surface best practices for public sector innovators. During weekly “Coffee Hours” and working sessions, our community members share their journey and lessons learned with each other. We know that innovation evolves through iteration and diverse perspectives, and Public Sector Connect is committed to helping surface critical challenges and solutions, and connecting those who are solving similar problems. Join the community today.
Telus Ensures Workers’ Safety Using Edge and 5G
3195
Of your peers have already read this article.
1:30 Minutes
The most insightful time you'll spend today!
Editor’s note: In February 2021, Google Cloud and TELUS announced a 10-year strategic alliance to drive innovation of new services and solutions across data analytics, machine learning, and go-to-market strategies that support digital transformation within key industries, including communications technology, healthcare, agriculture, and connected home. By December 2021, TELUS had completed a pilot for a use case that leveraged Google Cloud AI and Machine Learning solutions and Telco Edge Anthos to increase safety in the workplace and save lives in manufacturing facilities. The use case leverages Multi-Access Edge Computing (MEC) to move the processing and management of traffic from a centralized cloud to the edge of TELUS’ 5G network, making it possible to deploy applications and process content closer to its customers, and thus yielding several benefits including better performance, security, and customization. Today, we invite Samer Geissah, Head of Technology Strategy and Architecture at TELUS, to share how the company is delivering on its promise to use this technology to drive meaningful change, starting with workers’ well-being.
Whenever a new technology buzzword comes along I think: what problems does this solve, and for whom is this going to make a real difference? That’s because at TELUS, we see innovation as a means to act on our social purpose to drive meaningful change, from modernizing healthcare and making our food supply more sustainable, to reducing our environmental footprint and connecting Canadians in need. Multi-Access Edge Computing (MEC) is a buzzword that offers an opportunity to do just this. That’s why we want to leverage cloud capabilities and optimize our network’s edge computing potential, tapping into our award-winning high-speed 5G connectivity to help solve some of industry’s most complex challenges.
The reason why this presents such a great opportunity is that companies across industries still rely on maintenance-heavy on-premises systems to manage core computing tasks. But, with cloud capabilities delivered at the edge of our 5G network, we open a new world of possibilities for them. For example, manufacturers who currently rely on IoT-enabled equipment in their facilities can deliver new experiences by running advanced AI-based visual inspections directly from 5G-enabled devices–all without the need for local processing power or extra on-site space. In fact, it’s this example that inspired our new use case, where our Connected Worker Safety solution can be applied across a range of business verticals to help improve safety, prevent injury, and save lives, demonstrating how the perfect combination of skilled people and digital technology can make the world a safer place.
Empowering intelligent decision making at the edge
Be it a farm, manufacturing facility, hospital, or a factory floor, workers should be able to work in environments where their health and safety are held as the highest priority. But how can employers ensure that their remote, frontline, and in-office employees are safe and healthy at all times? We’ve found the answer by combining Google Cloud AI/ML capabilities and Anthos as a platform for delivering workloads, with our network’s infrastructure.
Together with Google Cloud, we have been leveraging solutions with the power of MEC and 5G to develop a workers’ safety application in our Edmonton Data Center that enables on-premise video analytics cameras to screen manufacturing facilities and ensure compliance with safety requirements to operate heavy-duty machinery. The CCTV (closed-circuit television) cameras we used are cost-effective and easier to deploy than RTLS (real time location services) solutions that detect worker proximity and avoid collisions. This is a positive, proactive step to steadily improve workplace safety. For example, if a worker’s hand is close to a drill, that drill press will not bore holes in any surface until the video analytics camera detects that the worker’s hand has been removed from the safety zone area.
A few milliseconds could make all the difference when you are operating heavy equipment without guards in place. So, to power the solution’s predetermined actions with immediate response times, we worked with Accenture and hosted the application on an Anthos bare metal Google Cloud environment running on our TELUS multi-edge access computing.
Because all the conditions in our model are programmable, this solution can be replicated at scale across a variety of practical scenarios other than factory floors. The actions in response to the analysis are also programmable, which means companies can use this technology to look at workers’ conditions and decide the best course of action to educate, assist, and protect them. All this is done through a single pane of glass ecosystem, making it easy to customize this solution to meet various business needs.
Meanwhile, leveraging our existing global networks to process data and compute cycles at the edge eliminates the need to transport data to a central location for real-time computation. This means that we can offer this solution to partners while optimizing latency and lowering costs.
Powering blink-of-an-eye communication with Anthos
To put the importance of lowering speed into perspective, consider that the average latency of blinking your eye is about 300 milliseconds. From a safety point of view, preventative processes need to be much faster than that. For this use case, our machine learning models running on edge are currently processing data at a tenth of the time it takes for you to blink your eyes, and we’re aiming to lower that latency further to help build even safer systems.
Our plan is to deploy Anthos clusters on bare metal to our customers across Canada to take advantage of our existing enterprise infrastructure, making it possible for us to run our solution closer to partners and eventually enable just one millisecond of latency.
At that point, we’ll be able to power new use cases that require near real-time feedback, leaving absolutely no room for error. This could include remote surgery, platooning of fleets on autonomous vehicles, and many other cellular vehicle-to-everything (V2X) solutions that require high-speed communication for platform operators to manage remote edge fleets in far-away places.
Improving workers’ safety while enabling new sources of revenue
Although edge computing and 5G have been around for a while, we believe that use cases like this are only just starting to demonstrate the incredible speed of change and high potential that these models provide. The next step for us is to develop our workers’ safety solution and get it to market, making TELUS an early adopter of new 5G solutions at the edge that can help our business and industry partners make workplaces safer.
It’s a great win to be able to combine efforts with Google Cloud and reduce latency in a context where timing can impact and save lives, and I’m confident that workers’ safety is just the beginning of a series of industry challenges that we’ll address together.
How One Company Uses AI and Data Analysis to Boost Revenue
9470
Of your peers have already read this article.
4:30 Minutes
The most insightful time you'll spend today!
AI, deep learning, and image recognition is transforming the shopping experience. These technologies enable consumers to use product images or screenshots rather than text to search for similar products. This improves the customer experience and enables retailers with online and offline outlets to provide a genuine omnichannel experience.
The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.
—Renjie Yao, Data Platform Lead, ViSenze
Visual commerce provider ViSenze is helping some of the world’s leading retailers improve conversion rates through image-based search.
The business’s products also enable media companies to use the platform to turn images and videos into engagement opportunities—driving new and incremental revenues.
Created through NExT—a research center established by the National University of Singapore and Tsinghua University of China—ViSenze now operates in the United States, United Kingdom, India, China, and Singapore. The business is backed by Japan-based internet and ecommerce company Rakuten and cross-border investment specialist WI Harper Group.
Growth in SMB and Mobiles
Renjie Yao, Data Platform Lead at ViSenze, sees opportunities for growth in the small-to-medium business sector, where companies do not have the resources to build similar technologies, and with mobile device OEMs to integrate ViSenze natively on smartphones.
Phone owners can activate a “shopping lens” on camera and gallery apps to capture an image of a product. They then receive matching results from more than 800 partner merchants and retailers and can then click through to product pages on partner apps or mobile websites. Alternatively, they may use a photo to compare products sold on different sites or shop matching styles.
“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs.”
—Renjie Yao, Data Platform Lead, ViSenze
The ViSenze API analyzes the contents of a selected or clicked image and sends the information back to the organization’s visual commerce platform. The platform feeds back similar results based on that information.
The ViSenze offering also extends to image analysis for the tagging of product attributes—such as a white turtleneck cardigan with full sleeves—to provide an improved search experience.
Data Vital to ViSenze
Capturing and analyzing large volumes of data is integral to ViSenze. “We have to understand how consumers interact with our customers’ ecommerce websites and apps,” says Yao. “For example, we need to know who has looked at a particular pair of jeans on a website and whether that visit led to a conversion. We can then tell that customer whether they need to make more stock available.”
ViSenze also relies on data to provide high-quality training for its image recognition models and its domain-specific models for online retail.
Protect Customer Data
Data is vital to ViSenze—but customer privacy is most important. “All the data we collect is transparent to our customers, meaning they can decide what they do not want us to collect. In addition, all personal data processing complies with privacy protection regulations in each region, such as the General Data Protection Regulation in Europe.”
A Quick Move to the Cloud
ViSenze started operations using servers, storage, networking, and associated systems in an on-premises data center operated by NExT.
However, to support rapid growth, the business decided to move its workloads to the cloud. ViSenze opted for a multi-cloud architecture, using in part a Google Cloud data infrastructure.
“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs,” says Yao. “We could connect different components with the click of a mouse.” Further, the business found it could easily configure rules and pipelines to route data logs to relevant Google Cloud services.
The review found Google Cloud’s extensive managed services would also remove administration and maintenance tasks from ViSenze’s in-house technology team—freeing team members to focus on more valuable tasks.
In addition, Google Cloud provided the security features—including custom hardware running hardened operating systems and file systems and encryption of data at rest and in transit—needed to protect sensitive information. Finally, the location of Google Cloud regions in several countries would enable the business to meet regulatory and data sovereignty requirements.
A Three-Month Implementation
ViSenze opted to move to Google Cloud in mid-2017 and completed a three-month implementation using internal resources. “The process was very smooth and intuitive, and we had no problems building our entire data platform within Google Cloud,” says Yao.
The business now uses an architecture comprising Google Kubernetes Engine to manage and orchestrate Docker containers running in Google Cloud Platform; BigQuery to provide an analytics data warehouse, with Google Data Studio providing customizable visualization and reports; Stackdriver to monitor and manage virtual machine instances and services inside Google Cloud; Cloud SQL to manage its relational databases for real-time analytics; Compute Engine to provide compute resources; Cloud Storage to store files and objects; Cloud Pub/Sub to provide real-time messaging between applications; and Cloud Functions to build event-driven applications.
After collecting the request logs of users in virtual machine instances and Docker containers, ViSenze distributes them in three directions. “We export raw logs into Cloud Pub/Sub for indexing inside an Elasticsearch search engine, and to a BigQuery data warehouse for further analytics,” explains Yao. “We also use Cloud Functions-created applications to obtain the logs from Cloud Pub/Sub to perform some real-time calculations.”
“We are currently using Airflow workflow management on Compute Engine as our hosted ETL platform, but are likely to move to Cloud Composer in future.”
500 Million Records Per Day
With Google Cloud providing its data infrastructure, ViSenze is well positioned to meet internal and customer demands for more granular insights. The business is now processing 500 million records per day through BigQuery and saves up to one year’s aggregated data—excluding any personal data—in the data warehouse for analysis.
The nature of ViSenze’s business means most reports are generated for data processed on an hourly, daily, or monthly basis. “BigQuery is extremely stable and performance optimized, regardless of the volume of data it processes,” says Yao. “Across BigQuery and other Google Cloud Platform services, we’ve recorded 99.99% availability over the past year.”
The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.
“With BigQuery, we have saved the equivalent of two full-time engineers and now need only half of one person’s time to maintain our whole data platform,” says Yao.
“In addition, BigQuery integrates closely with Data Studio, enabling non-technical people in our product and business teams to create dynamic, detailed analysis dashboards. We now use Data Studio to create nearly 50 separate reports.”
The business has now grown to offer access to more than 1 billion users and a listing of more than 400 million purchasable products.
Next Steps
ViSenze is now researching the potential of the Cloud AutoML suite of machine learning products to improve the training of its models and run a fully managed NoSQL database through Cloud Datastore.
“A NoSQL database service is the only missing piece of our architecture for now, and using Cloud Datastore would enable us to focus almost exclusively on our business,” says Yao. “With Google Cloud Platform, we are ideally positioned to continue providing support to our business team and help them continue expanding into new markets.
“In addition, we can help retailers and consumers to unlock the potential of the web and apps to transform the purchasing experience.”
More Relevant Stories for Your Company
Google Cloud’s 101 Illustrated References for Cloud Engineers and Architects
Many people are visual learners; I am definitely one of them, and judging by the tremendous response you all showed to Google Cloud Sketchnotes on LinkedIn and Twitter, you are too. -Priyanka Vergadia, Lead Developer Advocate, Google "A picture is worth a thousand words" Shortly after I started creating and
RISE with SAP on Google Cloud is An Engine of Progress for Cloud Migrations!
The practical benefits of migrating SAP systems to the cloud aren’t lost on most businesses. Running SAP in the cloud lets companies simplify tasks, scale quickly, and reduce costs. But as a growing number of organizations are discovering, the cloud is more than the sum of improved processes and workflows.
Developers and Practitioners’ Guide for Moving On-prem Data Warehouse to BigQuery
Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements
Indian social media platform ShareChat saw a 50% reduction in costs after migrating to Google Cloud
One of the key trends that emerged from the lockdown brought about by the COVID-19 outbreak was the significant rise in people seeking entertainment online. According to data released by Carat India, smartphone usage increased by 1.5 hours a week and social media consumption nearly doubled to 280 minutes a
