Tag Archives: analytics

Framework for Litigation Spending

The U.S. P&C industry has significantly lagged behind other U.S. and global industries in reducing unit costs over the last 15 years, and spending on managing claims litigation or contingent liabilities is a major reason. Most large P&C carriers spend 5% to 8% of gross written premium under various categories like external counsel, expert fees and internal attorney costs. This spending is considered a necessary evil so carriers can manage the right settlement or trial outcomes as well as protect their reputation. However, our experience with some of the largest U.S. P&C carriers demonstrates that there is a general lack of strategic insight in managing this large spending bucket and consequently a missed opportunity to reduce expenses.

Social inflation is an accelerating trend in the last decade, and COVID-19-related litigation is likely to complicate the situation for commercial insurers significantly. The systematic increase in litigation funding and rising wealth inequalities have added significant fuel. Many CEOs have publicly raised social inflation as a continuous challenge to profitability.

Sudden economic changes brought by the pandemic have created both risks and opportunities for P&C carriers. On the one hand, extended closure of courts and delays in litigation are likely to drive more plaintiffs to look for faster settlements. On the other hand, there is a high degree of uncertainty because of potential legislation regarding coverage exclusions in business interruption policies. Carriers need to respond to events as they occur, state by state, with great agility, empathy and data-based objectivity.

It is important for insurance carriers to have a robust litigation management strategy. We have identified five key levers in managing litigation spending:

1. Being Data-Driven

Having a data-driven claims team is the first prerequisite for leveraging the power of analytics for litigation. Exploration of claims, litigation and financial data leads to surfacing the need for advanced analytics intervention. Extraction and processing of external court data is challenging and often expensive, but a few carriers have seen tremendous return on such investment.

2. Well-Defined Metrics

Most carriers struggle with a homogenous and widely accepted (internally) definition of litigation spending and its categories. Claims, finance, general counsel, internal trial division, procurement, legal ops – are all the departments that have slightly different ideas of what actually is a dollar spent on litigation, and consequently what and how that expense can be reduced. As a start, a strategic initiative to harmonize the definition and reconcile the differences in metrics (as they flow through multiple databases and reports) should be launched. Such an initiative has very high return on investment as it tends to bring into focus the opportunity for the carrier.

3. Advanced Analytics Capabilities

A few carriers are building models to predict litigation propensity or even to predict outcome based on use of staff versus outside counsel. In addition to data science and analytics model deployment experience, prioritization of the advanced analytics resources toward litigation spending management is a key requirement.

See also: P&C Commercial Lines in 2021

4. Data Infrastructure

Quality and freshness of data flowing into the descriptive and predictive analytics workflows is a key determinant of the value of litigation analytics. Poorly built and broken data pipelines may cause delayed and incorrect execution of the analytical models and may not yield insights to act upon in spite of successful validation of early models. A robust data management strategy is important to ensure collection, cleansing and preparation of critical data elements for analytics execution.

5. Attitudes and Behavior

Perhaps the most important factor holding back P&C carriers is a lack of the right attitudes and behaviors. An economically optimal view needs to be developed for leadership to take an informed decision in every litigated claim (sue or settle) or even potential litigation. Serious adoption of insights by operational staff is usually the last and most critical point toward data-driven success. In our experience, a strategic approach to litigation management requires mixing experienced litigation adjusters skills with data science, engineering and process design experts.


There are no silver bullets in systematically reducing litigation spending. In our experience, the carriers using most, if not all, of the principles discussed here are way ahead. Their desire to manage litigation spending better made them methodical and data-driven. We can say with almost certainty that, as the situation with COVID-19 accelerates, changes in claims litigation combined with the effects of social inflation mean that these carriers are better prepared to face the future.

Do Health Plans Have the Right Data?

Today, health plans (also referred to as payers) are busier than ever. They strive to deliver efficiency, great customer and stakeholder experiences and improve care outcomes. To do this, they need to use more data, and they have much data at their disposal. But what are they missing?

They can be missing key patient information; that is, they don’t see the whole picture. The insurance industry is no stranger to data gathering, coding, tagging, analyzing. In addition to a long history with data, in the recent decade or two the industry has seriously upped its game in terms of converting legacy data to newer usable forms and has upgraded systems to establish true master data management infrastructure (whether in-sourced or in/out-sourced). We have even seen a flurry of third-party data use and the occasional implementation of new ways to digitize unstructured data. All of this is here to stay, which is good for data purveyors, health plan analysts and application developers and for the business of cost management and reimbursement. Providers and patients alike will benefit.

But the challenge remains: Health plans can’t keep up with everything all the time. And they cannot use all of the data as thoroughly as they want to. 

I advise health plans to take stock of their needs and assess whether current data sources will get you where you need to go. If not, additional patient level data — identified or de-identified — from a new outside source could very well be in order. 

Let’s review some of the cases where a health plan could tap into some of the available ecosystems to solve key challenges.

Data Efforts Are Getting Budget Dollars

In 2016/17, it was estimated that the life and health insurance industry spent over $3.5 billion on marketing and advertising activities. (Estimate is compiled from more than one source and may include some commission payments). 

On top of this, according to Novarica, Gartner and other watchers, the industry plans to spend hundreds of millions of dollars a year through the 2020s on data and analytics talent, technical infrastructure that supports AI and machine learning, advancements in digital capabilities and modeling, as well as improving content and communication management systems. Throughout, every functional department of a health plan will seek data-driven understanding and confidence. 

Sample Payer buckets illustrated below: 

Large Payers (Top 10): In-house data management leaders and large-scale analytics teams at large payers often can be funded to focus on select hot topics of the day, and sometimes they build things themselves. The opportunity for larger payers is to acquire an assortment of sample datasets in the size and with necessary permissions so they can focus on what they are good at: the analytics, evaluation of new product designs, improvement to sales plans, sales enablement and sales effectiveness, negotiating network contracts (think value-based care) and delivery of care.

Next 100 Payers: While these payers have smaller in-house tech and analytics teams, they can still have resources in terms of staff and dollars to spend on services or data. They might find it very useful for a fixed amount of time to engage third-party data sources so they don’t have to commit to hire permanent staff. I have seen many engage analytics expertise to go with it, or buy/license just the data.

The rest of the market: As we move to smaller, regional or independent health plans, often I see that they have small in-house teams dedicated to data management and small teams dedicated to analytics. (A team might be as small as two or three persons.) It is not uncommon that they outsource part of their data management and data analytics capabilities. They tend to have more updated technology platforms and can easily acquire third-party data on demand, plugging it in quickly, to complement their curated internal data sets.

See also: Overcoming Human Biases via Data

Use Cases Abound

In general, richer patient-level data can help health plans address needs in two big categories: market segmentation and risk management. Below are market trends that drive needs in these categories, with an opinion as to where the opportunity lies for helping plans address them. Please share your thoughts with me in the comments section below.

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.