Swift Decoder
Company : Honeywell
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Target Industry : Walmart, Ikea, Airline Industries and in almost every industry, including healthcare, retail, transportation, logistics, warehouse automation, hospitality and access control.
SwiftDecoder is Honeywell’s comprehensive and user-friendly SDK designed to empower customers in developing native or hybrid applications for camera-enabled devices. With SwiftDecoder, barcode decoding becomes seamless and, allowing you to unlock the full potential of your applications.
Research
PLACEHOLDER
SwiftDecoder is based on Honeywell-patented, field-proven algorithms, that deliver the speed, range and accuracy to help boost performance. It offers flexibility with more than 100 symbologies supported. Fewer misreads per millions demonstrates high accuracy. SwiftDecoder is also compatible with a wide range of hardware such as mobile phones, tablets, PCs, payment terminals, industrial cameras and purpose-built devices.
Challenge
SwiftDecoder is a machine vision-enabled software, designed for the development of a wide range of applications. The software development kit provides image processing at the edge and on the cloud, helping to improve operational productivity and performance, while delivering a great user experience and excellent customer satisfaction. The software allows for streamlining complex workflows in almost every industry, including healthcare, retail, transportation, logistics, warehouse automation, hospitality and access control.
Opportunity
Digital transformation of the syndicated loan market.
Streamline the exchange of information and bring transparency for all parties; Thereby, saving time and money, removing the need for rekeying which reduces errors, reconciliation and queries.
USER & PERSONA
For each deal, the agent communicates with 200-1,000 lenders, each capable of handling over 1,000 deals and processing several hundred thousand transactions annually.
Challenge
Software developers can build applications capable of:
• Analyzing a digital image
• Identifying and decoding over 100 barcodes symbologies
• Capturing and parsing data from driver licenses and motor vehicle documents
• Capturing and parsing data from travel documents (including passports and boarding passes)
• Capturing OCR (OCR-A/B) data with the expanded capabilities to have templates for custom documents and tags.
In addition, SwiftDecoder provides various scanning capabilities such as:
• Preview and select
• Batch scanning
• Targeting modes
• Windowing modes
Opportunity
SwiftDecoder supports augmented reality, including our ‘Preview and Select’ and ‘Batch Scan’ capabilities. Where there are several barcodes present, search-and-find functionality can highlight a specific barcode to help improve workflows, especially in warehouse, transportation and logistics applications. Barcode location coordinates can also be used to overlay visual information for the end user.
KEY OBSERVATION FROM USER TESTING
Insights
Terminology varies between countries and businesses, so it was important to use system agnostic terms.
Opportunity
Competition
The 3 biggest banks in the world, announced they were partnering to solve the same problem.
This only further proved our solution was one that was needed.
Our senior product manager, decided to pivot and leverage the fact that the overwhelming majority of people in the syndicated loan market used Finastra’s other product Loan IQ. So, the idea of a Loan IQ module was born.
Outcome
The communication between lenders and agents was streamlined. And allowed lenders to view and process over 30 types of transactions, automated deal mapping and transaction processing and integrated with Loan IQ.
KEY OBSERVATION FROM USER TESTING
Connecting Disparate
Systems
I had a creative strategy to automatically map deal data based on key data and weighing those factors for recommendations.
The issue was that the lender’s serving system might not have all the data or match what we have. I kept thinking of how the user needed to do extra work to map deal data between the 2 systems.
I brought up my concern in a meeting and showed the idea of auto-mapping in a mockup. After a few collaborative meetings with the business analysts, some key pieces of data were pinpointed that would be similar. For the North American markets, there were 2 key data points to help identification. With that I advocated for the giving weighted values for these data points for recommendation ranking, and if they all matched, then Fusion LenderComm would automatically map them.