When ATB Financial decided to migrate its vast SAP landscape to the cloud, the primary goal was to focus on things that matter to customers as opposed to IT infrastructure. Based in Alberta, Canada, ATB Financial serves over 800,000 customers through hundreds of branches as well as digital banking options. To keep pace with competition from large banks and FinTech startups and to meet the increasing 24/7 demands of customers, digital transformation was a must. To support this new mandate, in 2019, ATB migrated its extensive SAP backbone to Google Cloud. In addition to SAP S/4 HANA, ATB runs SAP financial services, core banking, payment engine, CRM and business warehouse on Google Cloud.
In parallel, changes were needed to ATB’s legacy data platform. The platform had stability and reliability issues and also suffered from a lack of historical data governance. Analytics processes were ad hoc and manual. The legacy data environment was also not set up to tackle future business requirements that come with a high dependency on real-time data analysis and insights.
After evaluating several potential solutions, ATB choseBigQuery as a serverless data warehouse and data lake for its next-generation, cloud-native architecture. “BigQuery is a core component of what we call our data exposure enablement platform, or DEEP,” explains Dan Semmens, Head of Data and AI at ATB Financial. According to Semmens, DEEP consists of four pillars, all of which depend on Google Cloud and BigQuery to be successful:
Real-time data acquisition: ATB uses BigQuery throughout its data pipeline, starting with sourcing, processing, and preparation, moving along to storage and organization, then discovery and access, and finally consumption and servicing. So far, ATB has ingested and classified 80% of its core SAP banking data as well as data from a number of its third-party partners, such as its treasury and cash management platform provider, its credit card provider, and its call center software.
Data enrichment: Before migrating to Google Cloud, ATB managed a number of disconnected technologies that made data consolidation difficult. The legacy environment could handle only structured data, whereas Google Cloud and BigQuery lets the bank incorporate unstructured data sets, including sensor data, social network activity, voice, text, and images. ATB’s data enrichment program has enabled more than 160 of the bank’s top-priority insights running on BigQuery, including credit health decision models, financial reporting, and forecasting, as well as operational reporting for departments across the organization. Jobs such as marketing campaigns and month-end processes that used to take five to eight hours now run in seconds, saving over CA$2.24 million in productivity.
Self-service analytics: Data for self-service reporting, dashboarding, and visualization is now available for ATB’s 400+ business users and data analysts. Previously, bringing data and analytics to the business users who needed it while ensuring security was burdensome for IT, fraught with recurrent data preparation and other highly manual elements. Now, ATB automates much of its data protection and governance controls through the entire data lifecycle management process. Data access is not only open to more team members but it is faster and easier to acquire without compromising security. And it’s not just raw data that users can access. ATB uses BigQuery to define its enterprise data models and create what it calls its data service layer to make it easier for team members to visualize their data.
AI-assisted analytics and automation: Through Google Cloud and BigQuery, ATB has been able to publish data and ML models that provide alerts and notifications via APIs to customer service agents. These real-time recommendations allow customer service agents to provide more tailored service with contextualized advice and suggested new services. So far, the company has deployed more than 40 ML models to generate over 20,000 AI-assisted conversations per month. Thanks to improved customer advocacy and less churn, the bank has realized more than CA$4 million in operating revenue. During the ongoing COVID crisis, the system was also able to predict when business and personal banking customers were experiencing financial distress so that a relationship manager could proactively reach out to offer support, such as payment deferral or loan restructuring. The AI tools provided by BigQuery are also helping ATB detect fraud that previously evaded rules-based fraud detection by using broader sets of timely and accurate data.
Thanks to the speed and ease of moving data from SAP to BigQuery, ATB is using artificial intelligence (AI) and machine learning (ML) to do things it previously hadn’t thought possible, including sophisticated fraud prevention models, product recommendations, and enriched CRM data that improves the customer experience.
Using the power of Google Cloud and BigQuery, ATB Financial has been able to draw more value from its SAP data while lowering cost and improving security and reliability. Speed to provide data sets and insights to internal team members has improved 30%. The bank also has seen a 15x reduction in performance incidents while improving data governance and security. Dan Semmens projects that the digital transformation strategy built on Google Cloud and BigQuery has both saved millions compared to its on-premises environment and has also realized millions in new business opportunities.
Semmens is looking toward the future that includes initiatives like Open Banking and greater ability to provide real time personalized advice for customers to drive revenue growth. “We see our data platform as foundational to ATB’s 10-year strategy,” he says. “The work we’ve undertaken over the past 18 months has enabled critical functionality for that future.”
Learn more about how ATB Financial is leveraging BigQuery to gain more from SAP data. Visit us here to explore how Google Cloud, BigQuery, and other tools can unlock the full value of your SAP enterprise data.
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