Largest Retail Bank in America Designs Processes for Automation 50% Quicker
Company Size
1,000+
Region
- America
Country
- United States
Product
- Blueprint Enterprise Automation Suite
Tech Stack
- RPA
- Business Process Modeler
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Predictive Quality Analytics
- Process Control & Optimization
Services
- System Integration
About The Customer
This customer is a Fortune 100 bank and is one of America’s leading financial institutions, serving individual consumers, SMEs, and large commercial clients. The bank offers a full range of banking, investing, and other financial products and services. They are committed to providing unmatched service and security for their customers. The bank is one of the top 10 largest in America based on assets and processes an average of 5 million payments per day. They have been a customer since 2015 and have over 150,000 employees with an annual revenue of $80 billion.
The Challenge
This Fortune 100 company is one of America’s top 10 largest banks based on assets, and as a result, they process an average of 5 million payments per day and deal with consumer data. The method for processing these payments has historically been painstakingly manual, slow, and prone to error which means money lost—one error can be calculated at $1,000 lost due to the fees and labor associate with investigating and rectifying the error. Beyond that, customers wanted their payments processed faster and with greater accuracy. The bank understood that in order to maintain a sustainable competitive advantage in the marketplace, they must cut costs, eliminate errors that pose serious regulatory risk, and enable employees to focus on work that is driving more value to the business.
The Solution
The bank chose the Blueprint Enterprise Automation Suite to scale their RPA initiatives and improve process quality. Given the high volume of transactions that the bank needs to process every day, it was important that they could identify key day-to-day activities that employees took part in. Using Blueprint’s Business Process Modeler, the bank modeled, decomposed, and optimized the processes it wanted to automate, while accounting for regulatory dependencies using Blueprint’s Trace and Impact Explorer. By leveraging contextually rich Digital Blueprints, the bank was able to develop bots 30% quicker while streamlining compliance handling and mitigating risk. Key aspects of the Enterprise Automation Suite that drove automation success include BlueDocs Document Importer, Test Script Mining, Impact Explorer, Process Modeler, and Centralized Repository.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Real-time In-vehicle Monitoring
The telematic solution provides this vital premium-adjusting information. The solution also helps detect and deter vehicle or trailer theft – as soon as a theft occurs, monitoring personnel can alert the appropriate authorities, providing an exact location.“With more and more insurance companies and major fleet operators interested in monitoring driver behaviour on the grounds of road safety, efficient logistics and costs, the market for this type of device and associated e-business services is growing rapidly within Italy and the rest of Europe,” says Franco.“The insurance companies are especially interested in the pay-per-use and pay-as-you-drive applications while other organisations employ the technology for road user charging.”“One million vehicles in Italy currently carry such devices and forecasts indicate that the European market will increase tenfold by 2014.However, for our technology to work effectively, we needed a highly reliable wireless data network to carry the information between the vehicles and monitoring stations.”
Case Study
Safety First with Folksam
The competitiveness of the car insurance market is driving UBI growth as a means for insurance companies to differentiate their customer propositions as well as improving operational efficiency. An insurance model - usage-based insurance ("UBI") - offers possibilities for insurers to do more efficient market segmentation and accurate risk assessment and pricing. Insurers require an IoT solution for the purpose of data collection and performance analysis
Case Study
Smooth Transition to Energy Savings
The building was equipped with four end-of-life Trane water cooled chillers, located in the basement. Johnson Controls installed four York water cooled centrifugal chillers with unit mounted variable speed drives and a total installed cooling capacity of 6,8 MW. Each chiller has a capacity of 1,6 MW (variable to 1.9MW depending upon condenser water temperatures). Johnson Controls needed to design the equipment in such way that it would fit the dimensional constraints of the existing plant area and plant access route but also the specific performance requirements of the client. Morgan Stanley required the chiller plant to match the building load profile, turn down to match the low load requirement when needed and provide an improvement in the Energy Efficiency Ratio across the entire operating range. Other requirements were a reduction in the chiller noise level to improve the working environment in the plant room and a wide operating envelope coupled with intelligent controls to allow possible variation in both flow rate and temperature. The latter was needed to leverage increased capacity from a reduced number of machines during the different installation phases and allow future enhancement to a variable primary flow system.
Case Study
Automated Pallet Labeling Solution for SPR Packaging
SPR Packaging, an American supplier of packaging solutions, was in search of an automated pallet labeling solution that could meet their immediate and future needs. They aimed to equip their lines with automatic printer applicators, but also required a solution that could interface with their accounting software. The challenge was to find a system that could read a 2D code on pallets at the stretch wrapper, track the pallet, and flag any pallets with unread barcodes for inspection. The pallets could be single or double stacked, and the system needed to be able to differentiate between the two. SPR Packaging sought a system integrator with extensive experience in advanced printing and tracking solutions to provide a complete traceability system.
Case Study
Transforming insurance pricing while improving driver safety
The Internet of Things (IoT) is revolutionizing the car insurance industry on a scale not seen since the introduction of the car itself. For decades, premiums have been calculated using proxy-based risk assessment models and historical data. Today, a growing number of innovative companies such as Quebec-based Industrielle Alliance are moving to usage-based insurance (UBI) models, driven by the advancement of telematics technologies and smart tracking devices.
Case Study
MasterCard Improves Customer Experience Through Self-Service Data Prep
Derek Madison, Leader of Business Financial Support at MasterCard, oversees the validation of transactions and cash between two systems, whether they’re MasterCard owned or not. He was charged with identifying new ways to increase efficiency and improve MasterCard processes. At the outset, the 13-person team had to manually reconcile system interfaces using reports that resided on the company’s mainframe. Their first order of business each day was to print 20-30 individual, multi-page reports. Using a ruler to keep their place within each report, they would then hand-key the relevant data, line by line, into Excel for validation. “We’re talking about a task that took 40-80 hours each week,” recalls Madison, “As a growing company with rapidly expanding product offerings, we had to find a better way to prepare this data for analysis.”
