CEI Debuts Photo-Based Estimating at RIMS 2019

The largest risk event of the year, RIMS 2019 Annual Conference & Exhibition, concluded just last week in Boston, where CEI debuted its DriverCareSM photo-based estimating module to fleet risk managers.

With more than 10,000 attendees, RIMS 2019 provided a captive audience for CEI to demonstrate how photo-based estimating can speed claims processing while also reducing costs and liability.

CEI walked attendees through the module’s three-step process, including:

  1. Taking a photo of damaged vehicle with your phone
  2. Sending photos to repair shop for an estimate
  3. Estimate is approved, driver is notified, and repairs begin

The module is part of the CEI DriverCare platform, which is designed to meet all collision and safety needs by integrating claims, repair, training and risk management for drivers. Through a combination of best-in-class programs and proprietary technologies, DriverCare is recognized as the smart and easy way to maximize safety and productivity on the road.

In addition to providing a rapid method for claims processing, photo-based estimating and other DriverCare modules can help fleet risk managers minimize claims and liability expenses and reduce claims frequency.

Thank you to The Risk Management Society for hosting this year’s conference and exhibition. We’re already looking forward to RIMS 2020 in Denver.

To obtain pricing and learn more about DriverCare, contact us.

CEI Introduces Advancements to DriverCareSM at NAFA 2019

Just a few weeks ago, CEI attended the NAFA 2019 Institute & Expo in Kentucky, the industry’s largest and most comprehensive annual event for fleet professionals. The event served as a showcase for CEI to introduce advancements to its flagship DriverCareSM platform, which provides everything fleet professionals need to reduce costs and protect drivers in one program.

If you’re not familiar, the CEI DriverCare platform is designed to meet all collision and safety needs by integrating claims, repair, training and risk management for drivers. Through a combination of best-in-class programs and proprietary technologies, DriverCare is recognized as the smart and easy way to maximize safety and productivity on the road.

Some of the newest features revealed at NAFA 2019, include:

  • Integrated and mobile telematics
  • Interactive truck training modules
  • Smart message-driven photo handling
  • Predictive modeling
  • Enhanced data visualization and more

DriverCare Integrated Telematics serves as a barometer for safety between MVR events and accidents, helping proactively monitor, improve and reward driving behaviors. Features include:

  • Five telematics tiers complement DriverCare risk levels
  • Factors include cornering, breaking, acceleration and speed
  • Easy-to-read trends dashboard at fleet and individual level
  • Color-coded performance metrics for fleets and drivers
  • 60-day telematics accident lookback by driver

New and interactive truck training modules include Road to Safety, which shows drivers how to overcome common fleet driver mistakes that contribute to collisions, injuries, unexpected repair costs, and downtime. Additional new modules include Hazard Awareness, which places users into a variety of scenarios to test their ability to detect common driving hazards, as well as Pro-Tread, which is offered in partnership with Instructional Technologies, and is an e-learning library of training modules proven to change driver behavior through increased driver focus, engagement and retention.

With thousands in attendance, the I&E Expo Floor bustled with fleet professionals from around the world, representing all fleet segments including Corporate, Government, Law Enforcement, Global and more. The event was the ideal venue to debut these advancements to DriverCare and educate fleet professionals on how they can improve their safety and accident cost outlook.

Thank you to NAFA, the world’s premier not-for-profit association for professionals who manage fleets, for hosting yet another great conference. We look forward to next year.

To obtain pricing and learn more about DriverCare, contact us.

Cybersecurity: How Well Protected Is Your Company Against the Rising Tide of Malicious Disruption?

My theme in this continuing blog is that business disruption – the kind that results from a continuous strategic embrace of cutting-edge technology – is not just a good thing, but a requirement. Call it creative destruction. But there’s another kind of technology-powered disruption that is simply destructive: the hacking of an organization’s digital infrastructure.

How pervasive is the threat? Consider the following:

• Computer Ventures, a researcher and publisher covering the global cyber economy, estimates that cyberattacks cost the global economy $3 trillion in 2015, a figure is likely to double to $6 trillion a year by 2021.
• PWC, the accounting and consulting firm, says that 32 percent of U.S. organizations were victims of cybercrime in 2016, and projects that 34 percent will have become victims by the end of 2018.
• Microsoft estimates that average cost of a data breach to a business is $3.8 million, and that the average attacker resides in a network for 146 days before being detected.
• A University of Maryland study found that hackers were attacking computers and networks “at near-constant rate”, with an average of one attack every 39 seconds.

U.S. consumers and businesses of all sizes are being targeted, by individuals, organized crime and even state-sponsored agencies. What they’re after is consumer personal data, credit card numbers, login credentials, access to funds, technology and intellectual property, ways to cripple operating systems and – in a recent and rapidly growing trend – ransom to restore them.

The lure of rich bounty from cybercrime has attracted some of the brightest rogue minds and hostile regimes, and they’re widely believed to be working on the cutting edge of digital technology. In addition to the fact that hundreds of thousands of new viruses and other kinds of computer malware are created every day, a concern is that some hostiles are working on quantum computers that could hack the most sophisticated encryption systems while being invulnerable to hacking themselves.

As bad as 2017 and 2018 were, 2019 is likely to even worse, as the following evolving and emerging threats are gaining momentum (my thanks here to the Lazarus Alliance):

Phishing Schemes. Nearly all successful cyber-attacks begin with a phishing scheme. Business email compromises, a highly targeted spear phishing attack, are responsible for more than $12 billion in losses globally.

Cloud Cyber Security Threats. Cloud computing has transformed the ways in which we live and conduct business, but it has also given hackers a broader attack surface and created a host of brand-new cyber security threats and vulnerabilities, from cloud malware to misconfigured Amazon Web Service (AWS) buckets. Cloud security must be addressed differently than on-premises security, and solid cloud security starts with a secure cloud migration.

Use of Shadow IT Apps. More than 80 percent of employees admit to using unauthorized IT apps at work, which are known as shadow IT. Most of the time, their motivations aren’t malicious or negligent; they’re just trying to do their jobs better. However, shadow IT usage can be a serious compliance and cyber security threat. The best course of action is to open a dialogue with your employees to determine why they believe these shadow applications are better than your existing, vetted applications.

Cryptojacking. Cryptojacking malware, which allows hackers to hijack enterprise computer equipment for the purpose of “mining” cryptocurrencies, has become more common than ransomware. These attacks can be hard to detect, but they can be snuffed out by upping endpoint security and monitoring network traffic for unexplained spikes at odd times.

Ransomware. Cryptojacking malware may be more common today, but that doesn’t mean ransomware has fully gone away. While cryptojacking can waste energy and slow productivity of your systems, ransomware can stop you dead in your tracks.

Unsecured Internet of Things (IoT) Devices. A recent Mozilla report shows that there may be up to 30 billion IoT devices by 2020. Both the public and private sector are scrambling to secure the Internet of Things. In recent weeks, the National Institute of Standards and Technology released guidelines for securing medical IoT devices, and Microsoft launched a public review of its new solution for developing secure smart devices. Ask your IT team how many things really need to be connected and automated before adopting them, and always look to secure these devices with regular updates and passwords.

What business leaders must do

Cyber defense is critical to every business, and it’s too important for senior executives not to be familiar and up-to-date on cyber threats and counter-measures, especially those in charge of companies that may not have invested enough in computer security in recent years. A sound approach to cyber security rests on two key practices: an ongoing review and dialog between CEOs and their Chief Information Officers about the organization’s security technology, and a continuing program to educate all employees about cyber threats and the organization’s computer security policy.

The 21st Century’s Mega Disruptor: Machine Learning

Of all the technological advances mankind has achieved over the millennia that have changed the way we live and work, Machine Learning (ML) may well prove to be the one with the most powerful and disruptive effects. And while one application – autonomous vehicles – is still in its infancy, ML applications in many other business applications are already here and more are on the way.

ML grew out of the computer science of artificial intelligence (AI). Broadly speaking, AI refers to computers programmed to perform tasks that normally require human intelligence, like recognizing images and speech and making decisions. As remarkable as that is, ML goes one step further: it creates computers that can learn from experience and actually improve their performance.

Another way of putting this has to do with the way ML tames Big Data. We’re all familiar with the Big Data challenge: the Internet and a proliferating number of sensors in everyday items are generating massive amounts of data that exceed our ability to extract actionable information. ML programs enable computers to sort through immense data streams at lightning speed and find meaningful patterns that they weren’t pre-programmed to recognize. In turn, ML programs can use those patterns to make predictions of future events.

Here’s how Josefin Rosén, an ML and AI expert, describes it:

Machine learning enables computers to find hidden insights without being explicitly programmed where to look. They can change their behavior and improve their algorithms by themselves, and every time an error is made and recognized, the algorithm corrects itself and begins another iteration of learning.

The first ML application was created in 1952 by a pioneer in AI and computer gaming at IBM named Arthur Samuel. But as an independent discipline, machine learning didn’t begin to flourish until the 1990s, when the field changed its goal from achieving artificial intelligence to creating computers than can independently solve problems of a practical nature.

Now, businesses are rushing into the field with billions of dollars being invested. A recent survey of 500 chief information officers across 11 countries found that 89 percent said their organizations are using, piloting or developing strategies to machine learning. And it’s no wonder: just as AI has been used to automate thousands of manual tasks, ML has the potential to automate far more sophisticated functions that rely on data analysis used by business, government, medicine and more. The benefit is expected to be counted in trillions of dollars of increased productivity, improved customer service, creation of new products and marketing strategies, and millions of lives saved and made more enjoyable.

Machine learning applications have already started to seep into our daily lives. We have smart appliances and smart homes, voice-interactive digital assistants that can operate other devices and look up information for us, like restaurants with food we like or where our kinds of movies are playing. More and more service companies are using “chatbots” to interact with customers online, ML-powered digital assistants that respond to keyboard messages.

Behind the scenes, Uber and Lyft rely on ML applications to match drivers and riders and support dynamic pricing, rates that change based on supply and demand for rides by location and time of day. Google and other search engines use ML to improve each user’s search results employing back-end algorithms that watch how far down in the results users click on search items.

ML is also making headway in two industries with which I’m familiar: insurance and fleet.

  • User-based insurance. Drivers shopping for car insurance with Progressive can opt to install a company-provided telematics device that measures how much risk for an accident they pose, based on how often they speed and engage in harsh braking (a sign of distraction or tailgating). Safe drivers are offered premium discounts of up to $150.
  • Real-time repair estimates. Many insurance carriers are experimenting with ML-based technologies to help drivers who’ve had an accident receive real-time repair estimates by taking photos of their vehicle damage with a smartphone camera. The app is being built based on thousands of images of damaged vehicles and their repair costs.
  • Accident reduction. Fleets are beginning to turn an ML-assisted way of identifying high-risk drivers called predictive or prescriptive analytics. These algorithms use records of driver behavior and various demographic and industry data to predict the probability that they’ll be involved in a collision over the following 12 months. Once those that pose the greatest risk are identified, fleets can remediate their poor driving behavior with additional training and coaching.
  • Predictive maintenance. Based on historical data, Programs have been developed that can predict when auto parts or systems are likely to fail, and recommend replacements before those failures occur.

There’s more coming, in every industry. It’s part of what historians are calling the Fourth Industrial Revolution, in which disruptive technologies and trends such as the Internet of Things, robotics, virtual reality and artificial intelligence are changing the way we live and work. As with the previous three revolutions, many jobs will become obsolete and disappear, but – and I’m confident of this – many more will be created, and mankind will be much better off.

Competition in the business world will depend more and more on the extent to which companies harness machine learning and the related digital tools of the Fourth Industrial Revolution. The survivors will be those who master them strategically and master them first.