This week, how companies can easily deliver appropriate explainable Artificial Intelligence.
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Revealed – the top five insurtech trends reshaping America’s Insurance landscape
Insurtech hits record deals and financing
The insurtech space continues its rapid ascent as manifested in the market’s growing range of capabilities and business models – and those who will benefit the most, according to experts, are the insurance companies and investors with a deep understanding of “emerging technology and commercial trends.”
“Insurers and private equity (PE) firms are investing in insurtechs that improve business efficiency and penetrate new markets by creating fresh approaches to traditional insurance activities,” explained Martin Spit, principal at EY-Parthenon and Ernst & Young LLP US, and EY Americas Insurance Strategy and Transactions Leader, in a recent article.
Six Data Functions You Ought to Automate
Aspects of your data pipelines that should be treated as repeatable patterns
Any good Solutions or Data Architect should have various issues on their table at any given point in time. That’s normal. What’s abnormal is that the same problem finds its way back to that table. This is the one repeatable pattern you want to get rid of.
Solving the solution in conceptually the right place should always be the status quo of the architect. Although this is always desired, time pressures will make us do silly things. However, when your solutions are built on repeatable patterns it somewhat forces your hand.
How to build your data quality team
“Data quality is a team affair“
As the adage goes, a workman is only as good as his tools. There is no disputing that, but you can never overlook the power of qualification, aptitude, and experience when it comes to data quality. You need to select a data quality team that is acquainted with the high dynamism of the digital world and is up to date with contemporary
data management tools and techniques.
The data steward or the data architect or the data leadership/management team should understand both the IT and business aspects of the whole arrangement for a more harmonious strategy. One should understand the objectives of the organization, the type of business in, the demanding market conditions plus the impact of data across these and be conversant with the big picture. Team members, on the other hand, should be selected on merit. You should have the very best individuals in terms of data governance, and data quality initiatives, so your project is planned and implemented with strong business demands and governance in mind.
Harnessing the power of data analytics to drive compliance
Key takeaways to building a data-driven program
The age of data analytics in corporate compliance programs and regulatory enforcement is here.
Not long ago, the use of data analytics and artificial intelligence by corporate compliance departments was a compliance luxury, the preserve of a few well-heeled international conglomerates. Today, these technologies are routinely implemented for diverse corporate initiatives.
Understanding how regulators view the use of data analytics in driving compliance and how data analytics can inform effective compliance programs is critical to ensuring that companies of any size can rise to meet the moment.
So: what is data analytics, and how can it be used to enhance your compliance program?
Nine Steps In Career Transition
Career transition requires work, knowledge and support.
More American workers changed careers (not just jobs) in the past 18 months than at any time this century – and probably much longer ago. In my career coaching practice, I experienced a 45% increase in career transitions since January 2021.
And much of this was going on while we were all hunkering down during the pandemic – and while the “great resignation” (which was not so great, as it turns out) was on the loose.
Sure. Why not? Work patterns and time management took on entirely different proportions; workers by the droves were getting fed up, burnt out, or otherwise demotivated; and to add fuel to the fire, the amount of open jobs in America ballooned to more than 11 million – and then stayed that way month after month. With nearly twice as many open jobs as there were people to fill them, the unprecedented labor turnover rate reached its highest sustained level ever, yet the labor supply was insufficient to fill the open jobs that employers were desperate to fill.
Creating successful artificial intelligence programs doesn’t end with building the right AI system. These programs also need to be integrated into an organization, and stakeholders — particularly employees and customers — need to trust that the AI program is accurate and trustworthy.
This is the case for building enterprisewide artificial intelligence explainability, according to a new research briefing by Ida Someh, Barbara Wixom, and Cynthia Beath of the MIT Center for Information Systems Research. The researchers define artificial intelligence explainability as “the ability to manage AI initiatives in ways that ensure models are value-generating, compliant, representative, and reliable.”