McKinsey & Company: The advance of #analytics in claims management| #bigdata #insurance

Harnessing the potential of burgeoning data and computer power to add value must become ingrained in insurers’ every activity.

The use of data and analytics to underwrite risk is nothing new for insurance carriers.

Yet in a digital world, it is revolutionizing their business.

An industry in which 80 percent of all auto insurance claims are adjudicated automatically, and 80 percent of all life insurance policies are issued straight through without requiring any of the usual health checks, is no distant pipe dream. Neither is one in which the cost of acquiring a customer falls by as much as 70 percent because of precision marketing and personalization.

Such is the power of analytics.

The convergence of several technology trends is behind this revolution.

The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual reality applications, and billions of mobile phones.

Data storage capacity has increased, while its cost has plummeted.

And data scientists now have unprecedented computing power at their disposal, giving birth to ever more sophisticated algorithms.

As a result machine and deep learning are on the horizon.

“We’re moving from computer science, where computer coders write very explicit, line-by-line instructions, toward starting to train machines to look for information that could be valuable,” says Scott Simony, head of industry at Google.Yet data and technology alone do not deliver value, as too many companies have discovered their cost. While some are seeing good results, others admit they have seen little effect to date from their investments in analytics

It is important that this changes quickly, as those slow to adopt the technology at scale will surely struggle to compete. They will struggle against other insurers that use analytics to improve their core business by streamlining internal processes, raising revenue and cutting costs in the process. And they will struggle in the longer term as data and its analysis begin to break down business models and industry boundaries.

In personal auto insurance, we can already see how data from sensors fitted to vehicles will put premiums under pressure as driving becomes safer. And we only have to glance at other industries to understand how, in a world in which data and analytics are king, powerful new competitors with large customer bases for their core businesses can rapidly invade other sectors. Chinese e-commerce giant Alibaba also owns one of the world’s largest technology finance companies, which includes among its services insurance.

Here then, is how companies can move quickly to build their analytics muscle across the organization, avoiding common problems and ensuring their investments translate into business value.

There are four phases.

Phase 1:Building insights

The starting point is to be clear about how analytics can deliver insights and add value, and choose the use cases that will demonstrate this. Too often, companies give scant thought to the business problem they are trying to solve, instead getting carried away with refining data, gleaning perfect insights, or investing heavily in technology infrastructure.It is also important to understand what analytics can and cannot do. It cannot, for example, predict outcomes with pinpoint accuracy, particularly in low-frequency, high-severity, or shock-prone lines of business. For instance, the market for directors and officers liability insurance endured waves of litigation over the past decade—and subsequent spikes in claims—resulting from events such as the financial crisis and new regulations governing options backdating. It would have been difficult to predict any of these events with analytics.

Read all: The advance of analytics | McKinsey & Company


KPMG: Building trust in D&A #bigdata

You can’t make sound business decisions if you don’t trust your data and analytics.

Yet only around a third of all organizations have a high level of confidence in their customer insights or the analytics they receive on their business operations.

In their report, KPMG International explores the current trust gap affecting organizations around the world.Based on a global survey of more than 2,000 organizations, the report shares insights and recommendations on suggested processes, practices and governance for building trust in D&A using KPMG’s four anchors of trust, a framework for assessing quality, effectiveness, integrity and resilience.

Should you trust your analytics?

Find out in KPMG International’s latest report.

“As analytics increasingly drive the decisions that affect us as individuals, as businesses and as societies, there must be a heightened focus on ensuring the highest level of trust in the data, the analytics and the controls that generate desired outcomes.”

Read more at : Building trust in D&A | KPMG | GLOBAL

From algorithms to advertising: 7 steps to introducing AI to marketing

Artificial intelligence — once the rarefied domain of big-name, ambitious projects like Google’s self-driving car or IBM’s Watson — is now finding its way into everyday business.

In advertising and marketing specifically, brands might not be completely overhauling their existing ad tech and martech stacks to make room for AI just yet, but many are getting a feel for it by experimenting with single-touch AI solutions that focus on isolated tasks, like recommendations, ad buying and optimization.

The coming wave of AI in marketing will be defined by the automation of complex, multi-step processes — not just one-off aspects of a larger campaign. For brands, this will mean relinquishing control, trusting the technology to come in and quickly understand processes comprised of numerous tasks, channels, people and procedures, without messing things up.

Before handing over the reins, it’s helpful to understand how AI works — and how entire human thought processes are converted into algorithms.

For all its complexity, here’s a simplified look at seven steps to introducing an AI that can automate holistic digital marketing programs from start to finish.

  1. Obsessively observe and unravel every step in the process

Creating artificial intelligence for “self-driving” marketing technology is not so different from creating AI for a self-driving car. In the case of the car, it must know how close it is to other cars. It must know how to make a turn and ensure that it’s in the right position at the end of the turn, when to hit the gas pedal to go faster, what the road conditions are like, and so on — all without the driver telling it what to do.Like driving a car, many of the thought processes that go into the day-to-day execution of marketing programs also happen automatically and largely on the subconscious level. Transforming these subtle processes into a tangible series of algorithms means isolating logic and reasoning that humans often aren’t even aware they’re engaging in.This begins with the acute observation of marketers and account managers as they execute each step of a process, over and over again. Often, things that seem trivial — like determining which image and headline combo work best for Facebook, how much budget to spend where, or picking keywords for a search campaign — are critical parts of a larger process.

  1. Understand why human marketers make the decisions they do

The AI doesn’t just need to know what steps to take; it ultimately needs to understand why each decision was made, whether it was based on experience, logic and reasoning or simply knee-jerk instincts.This requires asking marketers and account managers to describe their decision-making process, which can be difficult considering that, as we’ve already discovered, they have no idea what motivated them half the time: Why did you keep these words and ditch those ones? How did you decide your bid size? Say you see a keyword doing well and you increase it by 20 percent — how did you choose 20 percent? What is the best time of day to send stuff to that person? Okay, what about that other person?

  1. Teach technology how to understand abstract information, such as creative Data in the form of words and numbers are unquestionably the domain of AI.

So, what happens when technology is asked to process and make decisions that are more creative in nature?For a human, understanding why certain images and text make more sense as a first interaction with a consumer rather than as a secondary or final interaction is almost second nature. A machine, on the other hand, needs to be told (or programmed) with this knowledge in order to be able to judge images and text, determine where they should appear along the journey, and not have to rely on humans to make these decisions for it.

Read all: From algorithms to advertising: 7 steps to introducing AI to marketing

Where is data-driven marketing headed in 2017? | Econsultancy

‘Data-driven’ is one of those terms which seems unnecessary for marketing.

Surely all marketing uses data to some extent, so why does there need to be a distinction?

As marketing increasingly moves to digital platforms, however, the concepts behind the term ‘data-driven marketing’ have become distinguished from more traditional marketing and even have their own vocabulary.Terms like programmatic buying, real-time bidding (RTB), data management platform (DMP), customer data platform (CDP), and attribution modeling are now standard lingo when talking about using data for marketing nowadays. Without some grasp of these terms and the concepts behind them, marketers can quickly become lost when speaking with others in the biz. Perhaps, then, it does make sense to talk about ‘data-driven’ marketing differently from other marketing which focuses more on the ‘four Ps’ or ‘STP marketing’.

For readers who feel that they need to catch up in this area, Econsultancy has a number of blog posts on these topics and Econsultancy subscribers can consult our recent research covering programmatic, data-driven branding and the role of the CRM in data-driven marketing.But for those who are familiar with these concepts, the next question is:

For the answer go to the Source: Where is data-driven marketing headed in 2017? | Econsultancy

Gil Press: Top 10 Hot Artificial Intelligence (AI) Technologies


The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises.

A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.


“Artificial Intelligence”

Coined in 1955 to describe a new computer science sub-discipline, “Artificial Intelligence” today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what’s hot and what’s not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making.

Based on Forrester’s analysis, here’s a list of the 10 hottest AI technologies:

  1. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
  1. Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.
  2. Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.

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