This week, why do IT professionals need AI benefits more than most people?
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4 valuable areas of digital transformation in 2023 for Insurance, 3rd one is the best
This article looks at the four most valuable areas of Digital Transformation in Insurance in 2023 and how businesses will create better customer experiences with digital transformation.
How Embedded Insurance Can Help Serve Customers, Close the Protection Gap
With inflation putting pressure on policyholders and insurers, Tim Hardcastle, CEO and co-founder of INSTANDA, discusses how embedded insurance can help transform distribution and capture market share.
Insurers are battling to attract new policyholders as costs per claim rise. McKinsey estimates show rising costs contributed to a $30 billion increase in loss costs in 2021, over and above historical loss trends. With policyholders wading through inflation and higher costs in day-to-day life, insurers must be creative and thoughtful about how to grow their business. Digital transformation offers some encouraging rays of hope.
One innovation breathing new life into the industry is embedded insurance. With APIs (application programming interfaces) now being the norm rather than the exception, insurers have more opportunities to weave their products into third-party customer journeys.
Top Five Data Science Trends That Made An Impact In 2022
Data science is a technology that is evolving and changing by the day. Therefore, deepfakes and data privacy trends will no longer sustain the long run.
With the increasing amount of data and the increasing awareness of data-driven culture, global businesses strive to adopt a data science approach. Undoubtedly, data-driven intelligence has become the highest parameter to succeed in the digital world.
However, Covid changed the world overnight. Most data science models became useless—at least for some time. Everyone raced to retrain and redeploy their existing data science models. Some experienced bottlenecks, while others created new data science processes without glitches. With this perspective, we can say that creating data science processes has become more flexible; we can put them into production faster and more efficiently than before.
How machine learning is transforming insurance claims
Here are some of the use cases for machine learning in claims as well as the challenges limiting adoption.
Machine learning (“ML”) has been one of the most prolific areas when it comes to high-impact use cases for the insurance industry. And within insurance, claims management offers one of the most promising areas to apply this technology due to the large amount of data available to train algorithms and the consistency of principles applied in the claims assessment process. Here, we look at some of the use cases for ML in claims and challenges limiting adoption.
Focus on fraud
Fraud detection is perhaps the area where we see the most advanced adoption of ML among insurance companies. Start-ups such as Shift Technology, Friss and Owl Labs have seen strong demand from carriers and attracted significant capital from investors to support their growth. These tools function by applying cutting-edge data science to large historical claims data sets, enriched with third-party data.
How to develop a data masterplan
With the data analytics era just getting started and the centralized data factories already clogged up, it’s time to consider a new approach.
Insurance companies large and small are coming to the same conclusion: Our enterprise data warehouses can’t keep up with demand. Experts see this and are looking for a solution. Will it be Data Mesh? Data Fabric? Data Virtualization? I use ‘data warehouse’ to include data warehouse, data lake, delta lake, and/or cloud data warehouse.
To understand the path forward, it helps to unpack why the data warehouse is falling behind. Let’s cite three clear reasons: first, one data producer can’t keep up with many data consumers; second, data intelligence is closer to the source systems (e.g. claims, premiums), not in the back-end data warehouse; and, finally, the data warehouse is an extra hop. The limitations of the data warehouse are about skills and organization design more than technology.
How Blockchain Can Help Measure And Prove ESG Milestones
Solving global sustainability, equity and ethics challenges requires innovation and alignment on many fronts. Blockchain is one piece of this puzzle.
Environmental, social and corporate governance (ESG) has become one of the most discussed, debated and buzz-worthy topics of the past several years. Organizations face significant and growing demand to raise their standards and accountability across an array of issues that are widely viewed as ethical imperatives.
In FTI Consulting’s 2022 Resilience Barometer survey, an overwhelming majority—86% of corporate leaders—said they have been spending more on ESG and sustainability, and most also said they are under extreme pressure to improve ESG. More than two-thirds indicated they feel that they lack adequate expertise to cope with escalating scrutiny.
Featured Article
Why IT professionals need ai benefits more than most people
The people building artificial intelligence are the ones who need AI the most.
Article by @ZDNET & @joemckendrick
There are many interesting use cases for artificial intelligence, from drug discovery to autonomous transportation. But the people seeing the most benefits from AI technologies to date are technologists themselves — automating their operations and quality assurance, enabling faster application development, greater network optimization, and eliminating manual task work.
That’s the word from a recent survey of 7,502 IT executives and professionals around the world, commissioned by IBM’s Watson group. Overall, 35% of companies now report using AI in their businesses — up from 31% a year ago, with an additional 42% exploring the technology. It’s being applied through off-the-shelf solutions such as virtual assistants, as well as being embedded in existing business operations — especially IT processes.
The irony, of course, is that the people charged with building out AI-driven applications and systems — IT teams — need AI the most to support their efforts. This isn’t totally surprising, as AI development and implementation makes things much more complex, requiring greater levels of automation.