Tag Archives: analytics

3 Ways to Bar Fraud in Roadside Assistance

Unemployment is rising due to COVID-19, and some of the top risk management firms in the industry have indicated that fraud will also quickly increase. While claims fraud, inflated repair invoices and other common scams are probably the first that come to mind, roadside assistance fraud is another issue to which insurers should pay attention. It’s more common that one might think.

Especially as insurers increasingly offer ecosystem services such as roadside assistance to strengthen customer loyalty and generate additional revenue, it’s important that they ensure their roadside assistance partners are taking measures to protect against fraud, which can range from customers abusing the aid to get free gas, to tow operators sending fraudulent invoices. 

Here are some of the ways to protect against roadside assistance fraud:

Fast Payments Promote Trust

The adage, “An ounce of prevention is worth a pound of cure,” holds true in roadside assistance fraud. Instituting policies that reduce the incentive to commit fraud is far less painful than attempting to recover a loss, and one of these measures is to pay tow operators quickly. Especially in difficult times, those who issue payment within minutes instead of the standard Net-30 will foster loyalty that cuts down on fraud, especially while many companies are struggling to keep operations running. 

Tow operators balk at complex billing that deducts difficult-to-understand fees from their payment. The fees create unpleasant surprises that can make the difference between a profit and a loss. So, make sure that roadside assistance partners offer clear and transparent billing.

Transparency in Invoicing

With fraud on the rise, be on the lookout for roadside vendors that will attempt to bill you for “ghost” services. One way to mitigate this type of fraud is by asking your roadside vendor to provide transparency into completed services, ideally in real time. Require your roadside vendor to provide an unfiltered view into jobs, as well as customer confirmation that the job was completed. This kind of transparency makes it far less likely that you’ll be inaccurately billed or overcharged. Plus, this level of transparency gives you a better view into the customer experience. 

See also: 3 Ways AI, Telematics Revolutionize Claims  

Transparency in Operations

History tells us that, during times of high unemployment, we are likely to see more bad actors entering gig economy jobs. But fraud is often easily caught at the background check level, which can prevent bad actors from getting into the system in the first place. While it’s easy to provide a false name, it’s more difficult to provide a false Social Security number and matching drivers license. So, it’s important to have transparency in how tow operators are onboarded into your roadside assistance partner’s network and the methods they use to verify the identity of each driver and the person’s background. Ask about the types of checks they’re using to ensure identity verification, proper licensure and insurance compliance. Ideally, you want visibility all the way down to the driver level of who is servicing policyholders.

You need transparency because, while background checks have been an industry standard for years in roadside assistance, they may not be conducted at the appropriate level. For example, it’s common to accept a prior, third-party background check for a new contractor, a practice that leaves critical gaps in a contractor’s history and doesn’t necessarily report on charges or information relevant to the position. A “clear background check” usually does not tell you that the driver’s license is suspended, for instance. 

Background checks should be run annually at a minimum, but there are now next-generation background check services that will run in the background to provide live monitoring of arrest feeds, county reports and other proprietary information sites. This kind of continuous monitoring can flag events that could signal trouble, providing the opportunity to prevent fraud before it occurs.

The Importance of Analytics

Some policy holders may look to their roadside policy to help get what they see as “free fuel” as many times as possible. It’s a common scam, where drivers purposely avoid filling up and, when they run out of fuel, call the roadside assistance service to get some for free. 

This kind of fraud is most effectively detected through technology, specifically artificial intelligence, machine learning and analytics. Data analysis can identify previously overlooked trends to catch these kinds of issues and resolve them quickly. Insurers save money when machines and automation do the work instead of adding to headcount or finding problems only after the damage has been done. 

Even as fraud is anticipated to increase, roadside assistance many times has been overlooked. Don’t settle for passive fraud detection. Demand transparency and encourage the use of technology to mitigate risk, which will both reinforce your reputation and drive your bottom line.

2019 Trends for Customer Analytics

As we near the end of the first quarter of 2019, which trends are worth watching?

Data Visualization 3.0

To kick us off, let me highlight an area that is a regular topic on this blog, data visualization. Progress in this area is always a combination of skilled people as well as technology.

So, building on the positive examples from IIB Awards 2018, what is the trajectory for 2019? Well, I think this video from Elijah Meeks (senior data viz engineer at Netflix) highlights some important 2019 trends.

He not only summarizes the history of data viz tool development, but also the changing expectations of users. He may well be right about the 2019 theme of convergence — a third wave, not just of tool convergence but of developers and readers expecting one more flexible tool and communication medium.

Self-Serve Analytics Tries Again

There have been plenty of times when Gartner’s predictions of technology adoption have proven too ambitious, but they are always worth hearing. In a recent paper, Gartner predicted that by 2019 fully automated or semi-automated systems would be delivering more analytics than data scientists (or analysts).

Now, I am old enough to have seen at least two other waves of analytics “self-service,” with many predicting the democratization of analytics, only to later find that business leaders would prefer an analyst to do the work for them.

See also: 3 Skills Needed for Customer Insight  

However, with the rise of machine learning improving the intelligence and personalization of report/visualization delivery, this time may be different. This article from Dataversity does a good job of considering how this might happen for business intelligence (BI). However, I think it stretches the term BI too far and misses the difference between the advanced analytics and data science work, where data scientists should focus.

AI Applications Revitalize an Antiquated Trend

We shared several posts on the state of AI during 2018, focusing on financial services applications and even the issues of AI ethics. However, when worrying about potential threats to your career, it has become clear that many applications are hyped.

What we began to see in 2018 was a more mature production line to manage the delivery of AI products (including role of product manager). Several speakers at the Data Leaders Summit 2018 shared their practical experience in deploying AI models from lab to business lines.

So, I was interested to read this post on the reliable customer experience (CX) hub “Customer Think,“ from Vince Jeffs of Pegasystems. He provides a useful summary of how AI applications will evolve to better meet the CX demands for 2019, including familiar topics like empathy, human-machine collaboration, data protection and ethics.

Jeffs makes a good case for how AI applications will begin to demonstrate progress on all these fronts in 2019, an important milestone, if not yet the sci-fi destination of AI.

NOT the Year That Blockchain Transforms Businesses

This is a strange one for me to finish on, but I thought it worth including this (non) trend. Given that we have focused before on blockchain, and the outstanding questions if it is to help data science leaders, this caught my eye.

The title is almost clickbait, which is rare for a great site like Datafloq. However, the thoughts are worth reading. In this short post, Steve Jones helpfully summarizes both the progress in business adoption and the problem of still over-promising.

See also: Key Insurtech Trends to Watch  

I hope wise businesses will continue to adopt blockchain technology only where it is a more appropriate data solution. That could achieve its status as a data source that begins to matter to data leaders for analytics, too. But more likely it will be 2020 before we see serious use.

Which Waves Are You Preparing to Ride?

I hope those trends were useful to share with you and help inform your planning. There are many more topics I could have covered, including wider developments in data scienceIoT and virtual reality/augmented reality (VR/AR).

Which technology waves will you be riding in 2019? Are any essential to you achieving your 2019 goals? I’d love to hear your priorities or forecasts.

Don’t Neglect the Politics of Analytics

To complement recent advice from our guest bloggers, let’s consider the political dimension of applying analytics in business.

Annette and Peter have shared useful tips on applying analytics for both customer experience and marketing. However, unlike in the classroom, advancing such use of analytics in business always involves politics.

Over the years, as well as protecting technical stars from performance management systems, I have often had to mentor them on politics. Analysts can be idealistic and naive when it comes to the political dimension of office life.

So, in this post, I suggest a simple framework to bear in mind when interacting with your stakeholders. I hope it helps you navigate the political dimension, or to coach your team to do so.

A framework for the political dimension of stakeholders

I have shared before on the importance of mapping your stakeholders and segmenting them, so you can adjust your style to suit them. Beyond this, there is a need to be aware of the political dimension and to act accordingly.

Now, the precise details of political implications and game playing in each organization will vary. You will know better than I do what are long-term versus short-term priorities and how different stakeholders might be affected.

What I want to share is a framework for identifying where political considerations matter and for which stakeholders.

The 2×2 grid was developed by Simon Baddeley (Birmingham University) and Kim Turnball James (now at Cranfield). It is based on their research within the domain of local government, but their findings accord with my experience of working in large corporations.

Their analysis of research findings led them to segment people into one of the following four quadrants. These are defined by the twin dimensions of reading (political awareness – ability to read what is really going on) and carrying (political actions – for good or ill). Using those two axes, they identify four clear segments into which your stakeholders may fall:

  1. Innocent stakeholders (like naive sheep)
  2. Inept stakeholders (like inept asses)
  3. Clever but untrustworthy stakeholders (like cunning foxes)
  4. Wise stakeholders (like the proverbial owls)

Let’s consider each quadrant in turn and their implications for managing stakeholders when deploying analytics.

Quadrant 1: Naive Sheep

The first point to make using this framework is to avoid being a sheep. Like lambs led to the slaughter, people in this quadrant are politically unaware, but innocent of any ulterior motive in their work. While potentially trustworthy, they can also all too easily be manipulated by others or become cynical and resentful of the organization.

Sadly, I all too often discover that this is the quadrant occupied by analysts or data scientists. One of the reasons for that is actually principled. I have lost count of the times I have heard analysts or even their leaders describe themselves as not doing politics or sick of politics. As with an idealistic government-in-waiting, this is not a viable strategy.

See also: Sentiment Analytics Can Drive Growth  

Avoiding politics is impossible in any walk of life, but especially large organizations. As Aristotle said “man is by nature a political animal.“ However, it is possible to be political without selling your soul (as this useful article from Harvard Business Review puts it).

The challenge for those who identify themselves in this quadrant is to wake up and smell the coffee. Recognize all the evidence that decisions (even important ones) are made for irrationalemotional and social reasons. Listen and watch more. Become more astute at understanding others’ goals, concerns and where they might feel threatened. Build trust and collaborate where there is mutual benefit.

When working with stakeholders in this quadrant, it can be helpful to propose more collaboration or socializing of their ideas before acting alone. Reassure them by sharing their ideas, but also ensuring they get the credit, even when you have done the networking. You may well become a trusted adviser for getting things done.

Quadrant 2: Inept Asses

Please note I am speaking about donkeys, not derrieres! These are people who lack political awareness, every bit as much as sheep, but do not have benign motives. Instead, they seek to play political games or manipulate situations to their advantage, while being embarrassingly obvious.

They are the David Brent of real world offices. Believing themselves to be players they just make fools of themselves and usually undermine the reputation of their teams.

Now we can all make mistakes in life. Errors of judgment. But, if you honestly self-identify in this segment, then the good news is that you have woken up to it. Apologies may be in order, but the most important thing to change will be your options. Stop trying to get one over on people. Start keeping your word and sharing to help others.

When working with stakeholders in this quadrant, two things are worth considering. First, for any recommendations, lead with what is in it for them. Emphasize how they could benefit or advance their careers if they get on board. At the same time, be careful. You need to ensure you do not tarnish your reputation by being too closely aligned with their manipulation. You should also ensure it is not easy for them to pass off your work as their own.

Quadrant 3: Cunning Foxes

Here we reach the true Machiavellian manipulators within your business. At the worst, this is where you will find sociopaths and the few who actually do come to work to hurt others. Less extreme versions include those who have risen to a level of power or control and enjoy playing the system.

Although such operators can appear glamorized in TV dramas, they can also leave a trail of destruction in their wake. I have personally witnessed a CFO who clearly took pleasure in humiliating and thwarting the efforts of certain middle managers. In these days of greater awareness around mental health at work and the alarming level of suicide among men under 50, we should be very concerned with such behavior.

I have painted a picture of the stereotypical macho boss. But such character traits can also be found in women leaders and in those who do not appear senior. Anyone who has worked in large businesses will know that individuals can also exert control from positions of expertise or influence that are not obvious from org charts.

It would surprise me if many of the readers of this blog self-identified as being in this segment. However, I have seen embittered cynicism manifest in some of these tendencies. If you find yourself thinking how you can get your revenge on others at work through the skills you have or ability to sabotage their work – stop. Don’t just think about the consequences if they found out, take time to reflect on the kind of person you want to become.

Unfortunately, it is not rare to discover some of these characters at senior levels in large corporations. Being effective political operators, they are also often ruthlessly ambitious.

If you identify some of the stakeholders you need to work with as being in this quadrant, then proceed with caution. A few tips may help you. Where possible, brief them before public meetings so they are not caught unawares (you do not want to corner them). At times, it may also make more sense to approach them in a more public setting, after you have secured the support of others you can trust. You should also consider what benefits for them you can highlight and whether it would work best to share those directly or in public. A personal pitch that you know fits with their plans or creates an opportunity may help complement a more public recommendation “in the interest of customers.”

Quadrant 4: Wise Owls

As with all effective 2×2 matrices, this top right corner is where you want to be, having developed the ability to see the different political agendas and plans at work in your business and still being in touch with your soul and secure enough to act in the best interests of others. Those characteristics often distinguish those who are thinking more long-term.

Age does not necessarily make one wise, but it might be worth considering if some of those older leaders in your business have wisdom to share. You can spot an owl by reputation. These are the leaders who are known as those who get things done, and people really want to work with them. They may not appear to be shining brightly at present, but you will find their advice being sought by people at all levels.

If you have managed to develop both a strong ability to read the office politics and flexible tactics to get the right things done – please consider developing your team. Too few of today’s technically expert managers (across data, analytics, data science and research) possess such skills. Effective transformation of businesses to be data-led and ethical may well rely on your mentoring a generation of leaders to develop such skills.

See also: 3-Step Approach to Big Data Analytics  

If you have the pleasure of working with leaders whom you identify as being in this quadrant, consider asking them to be your mentor. This may be even more valuable for you if they are in a completely different part of your business. One of the most effective ways to develop increased awareness of office politics and the good judgment of deciding when and how to act is with the help of a mentor. At the least, it would help to consult with such leaders before widely sharing potentially controversial analysis. They may well be able to advise how to influence others.

Are you a Sheep, Ass, Fox or Owl?

I hope you found those thoughts on the political dimension of office life useful. Which segment did you identify with? Has anything I’ve said changed your view of use of politics at work?

Why not dig out your stakeholder map and seek to place each of your key players in one of the above quadrants?

At the least, I hope this post has prompted you to think about your ability to read political behavior and your motivations in any covert tactics of your own. A greater ability to operate wisely and ethically within the reality of political workplaces could really advance the influence and benefits of much analytics or insight.

The Future of AI and Work Life

The future of AI as a threat to our jobs is a popular topic. Here is a book to help you respond.

Unlike so much that is written on this topic, Tony Boobier‘s latest book focuses on a positive response. It also investigates the implications of AI at a deeper level than most analysis.

While many books have been written that focus on explaining AI, or focusing on the technology, this book focuses on jobs. Tony includes extensive research and careful analysis. He takes us through most sectors, to understand opportunities and threats.

Let me explain why Tony’s book, “Advanced Analytics & AI“ is worth reading, both for your role now and as future career advice.

Never mind understanding AI; do you understand work?

Tony is a man who has read widely. His polymath nature really shines through the start of this book. The subtitle hints at the breadth he explores, “Impact, Implementation & The Future of Work.“

Rather than just focusing on AI, Tony usefully starts by exploring the history of work. From slavery to the “flat white economy,“ he engagingly muses on both our need for work and how we recognize and value our abilities. This mindset guides his later exploration.

He goes on to provide some useful definitions of analytics and AI, helpfully calling out the lack of clarity and misuse of both terms that abounds. From business intelligence to advanced analytics and prescriptive analytics, plus rules-based systems and cognitive analytics, Tony manages to understand the purist distinctions and be pragmatic about what matters.

Going on to define AI, with particular reference to the Turing test, Tony briefly walks us through the history of AI development. As someone who worked in AI before the “AI winter,“ I recognize many of his examples and why the recent renaissance might be different.

Learning from AI in leading-edge industries

His list of leading-edge industries is always going to be controversial. But, given the investments that I have seen, I think there’s a good case for his selection. It includes financial services, automobiles, media/entertainment/telco and retail.

This chapter begins what is the heart of this book. For each sector (e.g. insurance), Tony outlines the relevance of AI and how it might replace some of the work currently done by humans.

His analysis is pragmatic. He points out challenges, difficulties and where either consumers may not accept technology or where the risks are too great. Tony does identify useful opportunities for AI innovation. He also suggests where those working in that sector can still add value.

The detail provided on so many diverse roles is particularly impressive. This is a well-researched book and a result of many years working in some of these sectors. Tony is also pragmatic in his identification of applications. From robo-advice to automated cars or supply chains, he calls out real progress and work still to be done.

AI progress within second-mover industries

Given a media focus on AI within leading-edge industries (e.g. autonomous vehicles), it is interesting to look elsewhere. Reading through the previous chapter, it became obvious that a number of AI innovations should be transferable.

In this chapter, Tony reviews those sectors that are beginning to apply pilots based on the above successes. From construction to utilities, public sector and agriculture, he challenges us to see how widespread adoption of AI could transform these jobs.

From smart homes to connected infrastructure, predictive policing to automated harvesting, Tony presents a picture of not just a threat to jobs, but a different way of working, that has subtly different challenges for each sector.

His challenges to previously accepted hierarchies and divisions between sectors are also important. With automated delivery of supply chains, previously separate industries could merge in new ways, one of many indications of the need for creative thinking by leaders.

The future of AI and its impact on professions

My own experience of seeking to get offshoring of analytics to work has taught me to be skeptical of hype. At the time, many commentators predicted the offshoring of white collar jobs. This proved limited, as the model ended up only working well for well-prescribed, repeatable processes.

We have also been this way before with AI. As Tony mentions, the hey day of expert systems in the 1990s predicted at least decision support for many professional roles, hopes that withered during the AI winter.

So, I read Tony’s chapter on the impact of AI on professional roles with some skepticism. That said, he does make a convincing case across a much wider range of professions than many would consider, not just to doctors, teachers and lawyers. Tony highlights the implications for AI delivering management, finance, engineering and even creative roles.

At the very least, Tony makes the case for opportunities and uncertainty – as a challenge for managers and entrepreneurs in this space. The role AI could play is still only starting to be defined for most professions, and planning would help.

Let’s stop pretending data science and AI are risk-free

Apart from extreme predictions of the end of humanity in robot wars, many articles suggest the rise of AI will be smooth. One might think it was risk-free and in line with the continual improvement of the human condition, even though so much that has gone wrong already should cause us to be circumspect.

Tony rightly includes a chapter summarizing these risks, from system failures to data privacy, employee error to reputational risk. Considering the role of the maturing regulation technology (regtech) sector, it is clear that does not provide all the answers.

Risk management, as for other sectors, needs a balance to be struck between automated efficiency and human judgment.

Prepare for your career in the future of AI

The most useful contribution of this book to society is the way that Tony ends it. In his final four chapters, Tony reviews:

That final chapter should be required reading for all those who will still be working in 20 years. Tony challenges readers to reflect on their motivations and needs, not just popular options.

As Tony encourages, now is not the time for humanity to fade away into a passive life of leisure. This is a time for careful consideration and design, planning how a powerful technology can serve humanity and avoid many pitfalls.

How are you preparing for the way AI will change your work?

I hope you found that book review useful, that it provokes your thinking about how AI will change your career.

From insurtech to jobs to avoid, Tony gives plenty of food for thought. It is also well worth checking his appendices, as resources for data. They cover implementation flowcharts, lists of jobs most affected by AI and professional bodies to advise you.

If you want to buy a copy of this book, you can get it here.

3 Insurtech Trends Accelerating in 2019

2018 was a breakout year for insurtech companies, as the insurance industry has been long overdue for innovation and disruption. The year attracted both talent and funding to the industry. FT Partners Research announced insurtech’s quarterly financing volume for Q3 2018 totaled $1.2 billion, which is up from $749 Million in Q2 2018. The excitement increasingly surrounding insurtech indicates that 2019 promises to be an even more meaningful and game-changing time for the insurtech space.

Here are three insurtech trends you should keep an eye out for in 2019 and beyond:

Sophisticated Analytics

Any successful insurtech startup is not only passionate about transforming the current insurance model to be more cost-effective and automated but is invested in exploring the role that data analytics plays at the core of this process. Intelligent and productive data aggregation, integration and analysis are crucial in achieving this.

When it comes to data analytics, the insurance industry’s antiquated business model has much room for improvement. Insurtech is modernizing insurance as we know it by implementing advanced big data analytics to optimize insurance products and services. And investors are taking notice. Significant investments are being made in data analytics and modeling techniques to improve nearly every part of the business. By embracing data analytics, your business can gain a competitive advantage by finding “new revenue opportunities, enhancing customer service, delivering more effective marketing and improving operational efficiency.” Over time, this rise in digital innovation is sure to bring significant opportunities for a more efficient, competitive and sustainable progress for insurtech as a whole.

See also: 10 Insurtech Trends at the Crossroads  


The vast and complex insurance industry has long awaited simplification. Insurers’ underwriting models have historically been a black box for consumers. Easy comparisons of complex data have been reserved for the experts. Transparency is critical to earning the trust of customers, especially in this digital age. People are now accustomed to online shopping, and they want procuring insurance plans to be less complicated — similar to shopping for and purchasing other high-ticket items such as homes and financial products. Consumers desire that their pricing and product information not only be transparent but comparable as “apples to apples” so they can make smarter choices. Users can access online marketplaces to compare prices and benefits of different plans side-by-side.

Partnerships between carriers and innovators

There is a deepening need for laser-focused investments and partnerships between carriers and innovators as insurtech has now matured into an everyday business. Insurance executive and insurtech dealmaker Stephen Goldstein argues that “the team is what is ultimately going to make an insurtech initiative a success,” meaning that incumbents and insurance leaders executing partnerships with insurtech companies are part of the recipe that is going to provide a positive ROI and make insurtech as an industry thrive. While 2018 was a year of exploring and experimentation for insurtech, 2019 will be the year of engaging and deepening those relationships.

At the start of 2018, insurance professionals predicted that the number of partnerships and collaborations between carriers and innovators would only gather momentum over the next year. And in June 2018, the Digital Insurer reported that partnerships remained a priority where insurtech was concerned. Insurtech companies are actively enabling new technologies that are used to provide increased efficiency and the ability to execute new tasks and analyses. These technologies are changing the industry on a fundamental level, all the while causing more incumbents to adopt these capabilities through investments or partnerships to compete effectively. The possibilities alone suggest that there will be expected growth in partnerships throughout the end of 2018 and well into 2019.

See also: Insurtech: Revolution, Evolution or Hype?  


2018 proved to be a massive year for insurtech, with a dramatic increase in funding from Q2 2018 to Q3 2018. There has been demand for skillfully acquired and implemented analytics, transparent experiences for consumers and mutually beneficial partnerships. All three trends are being successfully observed in 2018 and are believed to gather more momentum to lead us into 2019 and later.