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2603, 2024

Poisoned Data Pipelines: When Tainted Healthcare AI Corrupts Insurance AI Models

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For insurance providers, the integrity of data flowing in from healthcare providers and other sources is critical for training reliable automated underwriting, claims processing, and risk modelling systems. However, there are concerning scenarios where if upstream healthcare AI systems become compromised by data poisoning attacks, that corruption could potentially cascade through the entire AI pipeline - undermining insurance models in turn. Data poisoning, where adversaries subtly manipulate training datasets to induce vulnerabilities and errors in machine learning systems, has emerged as a critical threat as [...]

1005, 2023

AI in the Financial Services Industry – our take!

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Financial Services industry has always been in forefront of leveraging technology, digitally transforming and AI is no different. We noted AI initiatives in this sector being leveraged by organizations to not just create new products and services, but also help them transform processes, infrastructure and redefine their existing business models. The areas of focus been around Risk Management, Customer Experience Management (primarily in Customer service, Customer Acquisition) or Business Process Re-engineering. Some common areas where AI is being leveraged for rapid gains or early results [...]

2404, 2023

Sustainable Mission With Fission

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Two most important aspects of sustainable tech are, how technology can help us in sustainability and how technology itself can be sustainable. When we talk about information technology sustainability, data centres are always on top of our minds because they consume a massive amount of energy to keep the vast internet/networks running 24x7. Data centres are the reason the internet as we know it exists now, and they provide multinational organisations with the computing power that they need to keep their organisation’s day-to-day operations running. [...]

2410, 2019

Scaling Hadoop up to the Cloud

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The advent of Hadoop for processing Big Data many years ago solved the problem of needing to scale hardware to specialized and impractical dimensions, both from the specification as well as cost points of view. Hadoop distributes processing and storage to smaller units of hardware and allows new hardware to be added as required. These smaller units of hardware could be cheap, non-specialized commodity server hardware. This makes the proposition of working with Big Data more attractive from the point of view of the investment [...]

1312, 2015

Adoption of Telematics in Auto Insurance

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Telematics is the application of a combination of technologies to gather data from automobiles and transmit it to a receiving organization for various purposes. It could also be two-way communication, and this again depends on the purpose. The use of telematics is an example of an end point technology that forms one part of the larger world of IoT (Internet of Things), but the concept of transmitting data to and from a vehicle actually came into existence many decades ago. It was first pioneered by [...]

2011, 2015

Blockchain Technology: The Next Disruption in the BFSI Sector?

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much interest. Why is it growing? In almost every financial transaction there is an element of trust involved. Before a seller hands over a good or service in exchange for money they need to be trust that the buyer has enough money to pay for the purchase, the same money is not simultaneously being committed for spending elsewhere, and that the payment will be duly made at the agreed time and mode. In the simplest form it could be a small cash transaction in a [...]

1311, 2015

Traits of a Great Data Scientist

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Data scientists are a rare breed. It’s not easy to find the perfect blend of skills in information technology, statistics and business. Having said that, more and more are slowly beginning to be emerge after building up their profiles through a mix of experience and training. We’re still a long way off, however, in terms of bridging the gap between supply and demand in the talent market. Data scientists think data. They look for it, have enough programming knowledge to extract