Teaching an Old Dog New Tricks: Pet Claims Automation at RSA Group

There are several factors simultaneously driving integration of AI in radiology. Firstly, in many countries around the world there is a discrepancy between the number of doctors trained in radiology and the rising demand for diagnostic imaging. This leads to greater demands for work efficiency and productivity. For example, the number of radiology specialists (consultant work- force) in England went up 5% between 2012 and 2015, while in the same period the number of CT and MR scans increased by 29 and 26 percentage points respectively. In Scotland, the gap widened even further (The Royal College of Radiologists 2016). Today, the average radiologist is interpreting an image every three to four seconds, eight hours a day (Choi et al. 2016).Secondly, the image resolution of today’s scanners is continuously improving – resulting in an ever greater volume of data. Indeed, the estimated overall medical data volume doubles every three years, making it harder and harder for radiologists to make good use of the available information without extra help from computerized digital processing. It is desirable, both in radiological research and in clinical diagnostics, to be able to quantitatively analyze this largely unexploited wealth of data and, for example, utilize new measurable imaging biomarkers to assess disease progression and prognosis (O’Connor et al. 2017). Experts see considerable future potential in the transformation of radiology from a discipline of qualitative interpretation to one of quantita- tive analysis, which derives clinically relevant information from extensive data sets (“radiomics”). “Images are more than pictures, they are data,” American radiologist Robert Gillies and his colleagues write (Gillies et al. 2016). Of course, this direction for radiology will require powerful, automated procedures, some of which at least will come under the field of artificial intelligence.

Dr. Partho Sengupta needed a way to accurately identify disease patterns resulting from echocardiograms in order to improve diagnostics and save more lives. Specifically, he wanted to distinguish between two disparate diseases: cardiomyopathy, which directly impacts the heart muscle and often leads to heart failure, and pericarditis, which acts as if the heart is involved but doesn’t actually affect the heart. While both diseases present with similar heart conditions, the treatments are vastly different. For pericarditis, the treatment may include medication and, rarely, surgery. However, if the diagnosis is cardiomyopathy the patient undergoes medical management (i.e. a pacemaker) or in extreme cases, a heart transplant. Misdiagnosis of these disease conditions can put the patient’s life at risk and be very expensive for the hospital. Dr. Sengupta, therefore, looked to Saffron’s Natural Intelligence Platform to help his team increase the diagnosis accuracy of these medical conditions.

To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.

KeyBank, one of the nation's largest bank-based financial services companies, embarked on a national digital bank initiative following the acquisition of Laurel Road, a digital consumer lending business. The initiative aimed to build a digital bank focused on healthcare professionals looking to refinance student loans and buy homes. A significant challenge was reducing the time to market for new products by democratizing data and decoupling systems across the IT landscape. Like many large enterprises, KeyBank had a variety of vendor applications, custom applications, and other systems that were tightly coupled to one another. New projects often required developing specific point-to-point integrations for exchanging data, which did not address the needs of other downstream systems that could benefit from the same data.

BharatPe, a fintech company founded in 2018, aimed to make digital payments more accessible for over 10 million small offline merchants and kirana store owners in India. However, the company faced challenges in managing the massive amounts of data generated daily from payment processing to business analysis. Prior to using Google Cloud, BharatPe managed its legacy data warehouse with limited capacity to run a large number of queries. The company ran key performance indicator (KPI) reports, without the ability to understand real-time data patterns. Loading three months of data for quarterly reports took more than 30 minutes on the legacy system, and in some cases, queries failed because the system could not scale to support analytical needs. Additionally, BharatPe operates in a multi-cloud environment for disaster recovery and needed a data platform that could run queries against data, regardless of where it resides.

Barclays, a global bank operating in over 50 countries and serving nearly 60 million customers, faced a significant challenge in improving and streamlining the customer experience. The bank's relationships with customers were multifaceted, with customers engaging with the bank through various channels such as mobile, online, and branch locations. The bank had identified 400 different customer journeys, and the challenge was to align these across the enterprise and improve them in a way that positively impacted customers quickly. The director of operations at Barclays, Mike Gamble, recognized the need to transform the bank's processes around customer journeys. He aimed to enhance the understanding of how various functions contribute to delivering customer experiences and then expedite the rollout time of these new processes to achieve the end goal for the customer.