KEY TAKEAWAYS

--AI and advanced analytics can help to identify claims that may be at risk for litigation or other escalation, before they get out of control.

--Where adjusters in traditional claims management systems revisit cases from time to time, AI is at work 24x7.

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Inflation is very much in the news these days, as the cost of virtually everything seems to be on an upward trajectory.

From an insurance industry perspective, though, inflation is a multifaceted problem; in parallel with broader economic trends, social inflation is driving up the cost of claims, pushing premiums higher, making it more difficult to accurately price risk and leading to higher costs for virtually everything we buy.

Consider the trucking industry. In 2021, jurors in Florida awarded a record-setting $1 billion to the family of an 18-year-old tragically killed in a multistage accident involving two trucking companies. Needless to say, that verdict made headlines, but it's not an isolated incident.

Between 2010 and 2018, the number of so-called nuclear verdicts or megaclaims increased by an alarming 235%. During that same period, the average seven-figure verdict in the trucking industry grew from $2.3 million to over $22 million, a nearly tenfold increase in just eight years. Megaclaims in the trucking industry are getting a lot higher, and there are a lot more of them.

This level of inflation is unsustainable. It's driving up premiums, and the costs must inevitably be absorbed into the prices that trucking companies charge their customers to transport products. Virtually every product we buy at the local supermarket or big box store is going up in price, partly because sellers must absorb the cost of higher premiums. For consumers, it's a hidden cost, but we're all paying for it.

What's Driving Social Inflation?

This trend can be attributed to four root causes: a high-stakes litigation environment, shifting public opinion fueled by increasing divisiveness, medical cost inflation and newly emerging risks such as public health emergencies and geopolitical strife.

High-stakes litigation has played the most prominent role. Plaintiffs' attorneys have stepped up their game, using every tool at their disposal to aggressively push for higher verdicts. Plaintiffs' attorneys are also investing heavily in tools like advanced analytics, doing everything they can to ferret out intelligence that might tip the scales in their favor.

Courtroom strategy has changed, as well. Plaintiffs' attorneys have shifted away from trying to win the sympathy of jurors, finding that they can often be more successful by appealing to a jury's inherent anger and distrust of large corporations. Fear appeal is undoubtedly playing a role in nuclear verdicts.

Another factor is the industrialization of claims litigation. In recent years, a new class of businesses has emerged, offering advance payments to plaintiffs in exchange for rights to recovery. That raises the stakes considerably, combining a powerful profit motive with a highly organized class of professional investors, attorneys and case managers. According to Bloomberg, the litigation funding industry is currently managing $12.4 billion of investments. Well-organized attorneys are industrializing the litigation process, and smart money is expecting a return on those investments.

Medical cost inflation is also a significant factor. New drugs and advances in medical treatment are pushing prices higher, while the costs of malpractice and liability insurance are exerting upward pressure, as well. Recently, a shortage of skilled labor has also contributed to medical inflation.

See also: What to Do About Rising Inflation?

What Can Insurance Carriers Do About Social Inflation?

Let's take a look at what carriers can do about this. For starters, they can step up their game with respect to intel. Artificial intelligence (AI) and advanced analytics can help to identify claims that may be at risk for litigation or other escalation.

Every claim is a moving target, of course, evolving as a claimant's medical condition changes, as attorneys get involved and even as public opinion shifts. Where traditional claims management methods require adjusters to revisit each case periodically, digesting new material as it becomes available, technology offers a more agile (and far less tedious) alternative. AI is at work 24x7, monitoring incoming claim data in real time, alerting case managers promptly and increasing the likelihood of resolving high-risk claims without ever going to court.

AI technology also offers a way to help prevent claims from escalating in the first place. Matching injured workers with the best health care provider for their specific case, for example, yields a win-win-win situation. Workers achieve better medical outcomes, employers save money and get valued people back to work faster and carriers resolve claims at a lower cost.

Public policy may have a role to play in stemming the tide of social inflation, but it takes considerable time and effort to produce results through legislation. In the absence of widespread public awareness and pressure, political solutions to the problem seem unlikely.

That leaves individual carriers to repeat the mantra, "work smarter, not harder." Innovation offers a path to dampen the impact of social inflation, and AI technology has delivered very impressive results to date.

As first published in WorkCompCentral.

KEY TAKEAWAYS:

--Today, resources are misallocated -- 90% of innovation money goes toward maintaining existing systems, thus only 10% goes toward innovation.

--Insurers need to start piloting an innovation-forward industry, using tools such as modular systems, automated underwriting, advanced analytics and API integration with third-party systems.

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For every innovation, product release or software update, consumers and businesses become that much more digitally enabled… and digitally demanding. It’s a trend that may cause client expectations to eventually outpace the range of products and services offered by insurance providers – that is, if they don’t do anything to accommodate.

Although some insurers may be hesitant to throw all their chips in with digital strategies, leveraging technological innovations can indeed modernize the insurance industry. Nuanced digital tools have already begun filling the gaps created by skills shortages and are gradually becoming more mainstream. Yet many insurers still prefer to maintain their legacy systems.

Now is the time for traditional insurers to overcome their risk-averse legacy practices and scale up the industry’s gallery of startup solutions. Those that wish to retain their clientele within the rapidly changing market would do well to explore the options.

Reluctance to Innovate 

Although some insurers have adopted open digital insurance practices, such as many of those operating in Nordic countries, many others in the U.S., for example, are unsure which tech-based solutions to invest in – the array of choices is vast: centralized management systems, sales automation tools, self-service portals and mobile apps, among others. Even though the desire to change may be there, the rate of digital technology adoption among independent insurance agencies (44%) is still not where it might be. 

Part of the problem is a misallocation of resources. According to Deloitte, insurers direct 90% of innovation resources toward updating legacy systems – only 10% are allocated toward more transformative projects like product development or novel business models. 

Insurers are wary of the risks of innovation rather than eagerly anticipating the pain points that could be alleviated. But considering that market disruption is already on the horizon – not to mention the growing customer expectations for more cloud-native products and digitized services – this resistance may be incredibly counterproductive. 

The second part of the problem can be traced to insurers’ uncertainty around the compatibility of transformative new-age tech with their existing insurance structures. Many innovative ventures have failed due to the siloed nature in which they released their solutions, which were dislocated from other teams and systems that, in reality, they should have been integrated with.  

See also: Insurance 2030: Implications for Today

Tech Upgrades

What’s the answer? Insurers need to start piloting an innovation-forward industry – one that clearly demonstrates the high levels of usability and connectivity already available across other modern industries. Innovative methods and tools like modular systems, automated underwriting, advanced analytics and application programming interface (API) integration with third-party systems will propel insurers forward and sharpen their competitive edge to thrive in this rapidly evolving digital economy. 

Insurers should consider adopting the following measures so that they may future-proof themselves from getting squeezed out of the market:

1. Take on a modular system encompassing role-based front ends and workflow, with near-real-time connections to data from clients and products.
2. Automate rules-based underwriting and risk triage.
3. Upgrade analytics processes to link claims, policies and risk engineering. 
4. Administer cost-effective approaches when building digital products while remaining agile vis-a-vis connectivity with existing insurance structures. 
5. Leverage plug-and-play APIs and integrations with third-party systems. 
6. Differentiate software between back-end and front-end systems – the former should be robust, static and scalable with no room for compromises, and the latter should be customer-centric, omnichannel and data- and AI-driven. 
7. Align capabilities with respect to attracting, acquiring, retaining and rewarding customers across B2C and B2B sectors, while providing real-time data for insurance portfolios and the various types of insurance providers. 
8. Develop channels to interact with clients (e.g., receiving product feedback).  

Risk It for the Biscuit

The longer insurers wait to digitize their systems, the more they will frustrate customers, and the more they will hurt their bottom lines. 

The insurance industry can address many of its challenges by encouraging insurers to accept greater risk in adopting innovative technologies and properly integrating them into active systems. After all, risks can be mitigated by assigning each new onboarded solution with a specific goal or deliverable outcome that addresses insurers’ critical needs, aspirations or pain points. 

As digitization expands into the next decade, technology will serve as the industry’s best insurance policy.

Back in the 1980s, a boss of mine was sloppy about paying his bills and had developed a devious way of dealing with the dunning notices he'd receive. He'd let them pile up until the company seemed to truly be ready to refer him to a collections agency, then would send a check for all but, say, 63 cents of what he owed. The company would duly follow up, demanding its 63 cents -- at a cost that Marty figured at several dollars per letter. After letting several of those notices pile up, Marty would send a check for something like $1.50, knowing that the company would eventually refund his tiny overpayment and chuckling at the expense the company would incur.

Now, there aren't a lot of Martys in the world -- at least, I hope there aren't, for the sake of corporate efficiency -- but businesses trip themselves up in all sorts of ways that are even more damaging than hounding him about 63 cents, rather than treating the pennies as rounding error. There are the forms that really don't need to be filled out (by annoyed customers as well as annoyed employees), the meetings that don't need to be held, the processes that don't actually accomplish anything. 

Yet we focus much more on the new -- what meetings, forms, processes, etc. should be added? -- and too seldom take time to think about what we should stop doing. 

With the economic outlook uncertain, companies are searching for efficiencies wherever they can find them -- and a productivity push is a lot more palatable than layoffs. Companies also are in the middle of a once-in-a-generation opportunity to redefine the contours of work as they establish some sort of hybrid of work from home and a return to the office. 

So, now would be a great time to take a hard look and decide: What should we stop doing?

The topic pops to mind because of a recent piece in the Wall Street Journal that carried the subhed: "The time-consuming and pointless tasks companies can eliminate to boost our productivity… and save money." The main example is AT&T, which is in the middle of a multi-year program to cut $6 billion in costs. Part of the effort is Project Raindrops, "an initiative to save money and time by simplifying and eliminating business processes—from expense reporting to email communications to manager approvals—that slow down workers’ daily flow." An AT&T executive is quoted as saying, "'One mundane process may feel like a single raindrop, but when you have a multitude of that within the employee ecosystem, it creates a flood of extra work.'”

Among many other things, AT&T realized it was silly to make expense reports on retirement parties list each and every attendee. AT&T apparently throws lots of retirement parties: It figures the change in reporting saves 28,500 hours a year. The piece adds:

"AT&T has automated employee requests for access to some physical office spaces (savings: 23,333 hours), removed one of the two clicks necessary to connect to AT&T’s corporate computer network (savings: 300 million clicks a year) and simplified or eliminated the dozens of emails that go out every month to its fleet of drivers (savings: 41,310 hours)."

Frankly, the tabulation of clicks and the remarkably precise count of hours saved suggests that AT&T hasn't entirely shed the formidable bureaucracy I encountered when I covered the technology world from the WSJ's New York bureau in the 1980s and 1990s, but I applaud the intent.

Shopify took even a more direct route than AT&T. The provider of e-commerce tools for retailers simply deleted 12,000 events from the calendars of its 11,600 employees, which it figures frees up 322,000 hours this year.

On the other end of the spectrum, the article describes subtle, cultural changes that will take time and continual communication. The article talks about "'productivity theater,' or activities workers engage in primarily to look busy or be visible to managers and colleagues. Around 43% of workers say they spend more than 10 hours a week trying to look productive rather than on valuable tasks, according to a February survey of 1,000 full-time workers by the workplace analytics company Visier Inc.

"The firm found that the most common performative tasks include responding to emails from colleagues as quickly as possible even when a prompt answer isn’t required; attending meetings where one’s presence is superfluous; and completing unnecessary extra research for projects."

Those efforts sound worthwhile, too, even if they aren't quick fixes. One of the best things Lou Gerstner did when he took over as CEO at a flailing IBM in 1993 was to ban "foils" -- the term IBMers used for the sheets of cellophane prepared for use on overhead projectors. He knew IBMers spent months preparing for presentations to senior executives. The rule of thumb was that, for every foil you were going to display, you needed 10 backup foils, just in case someone asked a question. And for every backup foil, you needed 10 backups. Executives presenting to Gerstner actually didn't initially believe him when he said he'd banned foils. When he'd ask a question, they'd say, "Well, I have a foil here that shows the answer." But he wouldn't let the foil be displayed -- and the whole company eventually adapted to a new form of communication based on conversation and argument, not presentations, and on the radical idea that it was actually okay to say, "Let me get back to you with the answer." 

We all know that the insurance industry has no shortage of forms and procedures that could be tightened up or even eliminated, and what I think of as "meeting creep" affects every organization. Intel's meeting rooms used to have posters on the walls that instructed the person holding the meeting to calculate what the meeting cost, based on the time spent and the salaries of each person in the room, and to cancel the meeting if it wouldn't drive at least that amount of revenue, but that was in the Andy Grove days in the 1990s, and few CEOs have ever been as relentless. 

Some efficiencies will require time and money to produce. In particular, there is a frightful amount of rekeying of data, which not only soaks up employees' time but inevitably introduces errors. Eliminating that rekeying requires connecting one database to another, which is typically a messy process. 

But there are loads of quicker fixes, and they're easy to find. All you have to do is ask. 

Employees are more than happy to tell you what drives them nuts, while not producing any benefit for the company. So are customers -- or, at least, employees who deal with customers all the time. 

Maybe 10 years ago, a fellow approached me about buying his consulting practice. He was pretty much a solo practitioner but had all kinds of great references about the productivity improvements he'd generated at clients, almost immediately after they engaged him.

His M.O.? He just surveyed employees to ask what the company should stop doing.

And it worked.

I'm now providing this same advice to you -- and I'm not even charging you for it. Ask employees and customers what hoops you're making them jump through that don't provide any value for them or for you. 

If you can stop doing useless stuff, everybody wins.

Cheers,

Paul

KEY TAKEAWAYS:

--AI allows, for instance, for exoskeletons that can let workers with spinal cord injuries walk again.

--AI also has the capability to quickly analyze imaging scans to diagnose injuries, potentially speeding up the recovery process.

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Artificial intelligence (AI) is not just an emerging form of technology. AI has already proved valuable to just about every industry, including the medical sector. AI can often analyze complex medical data and come to more informed decisions than humans, leading to improved accuracy of assessments and more effective treatment plans.

“The advances in medical research and technology are robust. We are seeing exponential growth of new research every day,” said Lisa Haug, assistant vice president of medical management at Safety National. “One example is the utilization of artificial intelligence and robotics to help with nerve stimulation for spinal cord injuries, which recent studies are showing has the potential to help [patients] regain movement. While these advances do not necessarily eliminate the need for 24/7 care, they are promoting more independence for the injured worker.”

How Is AI Used in Recovery?

The following are just a few examples of how AI can be used to aid in an injured worker’s recovery:

• Exoskeleton: This is a wearable robotic for patients recovering from spinal cord injury. It allows the injured worker to regain balance, coordination and strength – enabling many to learn how to walk again.
• Rehabilitation: AI can analyze historical data to make predictions about the best course of action for treating an injured worker and guide medical staff in developing effective rehabilitation plans.
• Digital Health: AI aids in early screening, detection and intervention, resulting in early treatment. An example includes home screening diagnostics where an individual uses a test kit at home to detect diabetes, flu, colon cancer, etc. The kits are usually tied to an app, enabling users to monitor results on their devices.
• Prosthetics: There are now computer-aided design and 3D printing of prosthetic parts, along with embedded AI systems that can interpret an injured worker’s movements to provide adaptive tissue and muscle assistance, leading to improved patient mobility.

See also: AI in a Post-Pandemic Future

The Benefits of Using AI

The benefits of using artificial intelligence in an injured worker’s recovery plan are vast, including:

• Quicker Diagnosis: AI has the capability to quickly analyze imaging scans to diagnose injuries, potentially speeding up the recovery process.
• Individualized Treatment Plans: AI can draw on vast databases of patient data to create individualized treatment plans that are tailored to a patient’s specific needs.
• Improved Accuracy of Medical Assessments: AI technology can analyze complex medical information and gain deeper insights than a human doctor, leading to improved accuracy of medical assessments.
• Smart Rehabilitation Plans: AI can study a patient’s progress and conclude which rehabilitation techniques are most effective, thus ensuring an optimal recovery plan.
• Real-Time Monitoring: AI can monitor a patient’s recovery progression in real time and provide immediate feedback, allowing physicians to make necessary adjustments to improve outcomes.

All these advances will help serve injured workers, though organizations will need to remain cognizant of advances and the associated costs. AI will continue to provide advances that provide deeper insight into the patient’s condition, which could help provide an improved path for care and, in some cases, return injured workers to their jobs more rapidly.

KEY TAKEAWAYS:

--Generative AI can optimize underwriter workflow by automating routine tasks around new business pricing, renewals, endorsements and cancellations.

--It could automate much of the traditionally arduous claims processing workflow, reducing the need for human intervention and ultimately cutting down on hours. 

--Generative AI can analyze large volumes of data and identify patterns or anomalies that may indicate fraudulent activity.

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To bolster innovation, insurers are turning to a technology that, in its short lifetime, has already created massive changes in business and the world. Generative AI, a type of artificial intelligence that can create content, rather than simply analyzing existing data, has been at the core of experiments in trying to optimize insurer processes, predict risk and develop customized policies for individual customers.

There are three key areas in which generative AI could transform insurers: underwriter workflow, claims processing and fraud detection. 

1. Underwriter workflow 

Generative AI can optimize underwriter workflow by automating routine tasks around new business pricing, renewals, endorsements and cancellations. This can save time and improve efficiency, allowing underwriters to focus on underwriting. For example, on new business, generative AI can analyze past quotes to assist in risk triage, scoring and classifying policies to assist underwriters in risk selection. This is particularly useful for lines of business where there are significant policy volumes such as financial lines and general aviation. 

With the explosion of data, accurately assessing risk now can mean analyzing vast amounts of information. Generative AI can evaluate this data in real time, allowing insurers to identify emerging risks as they enter the picture and tailor their policies accordingly. With this information in hand, they can work to develop customized policies reflecting the specific risks faced by individual customers in different regions. Looking at other types of non-natural risks, generative AI can analyze social media data and other sources of information to identify and predict the likelihood of incidents related to cybercrime, fraud or other emerging threats. 

2. Claims Processing 

For as long as it has been an established practice, claims processing has been a labor-intensive and time-consuming process, involving extensive paperwork, manual verification and often lengthy delays. Generative AI could be the end of this traditionally arduous process, as it helps insurers automate much of the claims processing workflow, reducing the need for human intervention and ultimately cutting down on hours. 

For example, using natural language processing, generative AI tools can understand and analyze claim forms, quickly identifying discrepancies and pinpointing gaps in information. As a result, the claims process speeds up, and the likelihood of errors and inaccuracies drops drastically. On top of this already welcome improvement, generative AI can be used to automate claims verification, using machine learning algorithms to identify potential fraud or other irregularities, helping insurers reduce the risk of fraudulent claims and ultimately improving the overall efficiency. 

See also: Google's $100B Mistake--and How to Avoid It

3. Fraud Detection

Fraudulent claims cost the industry billions of dollars each year. Generative AI can be a powerful tool in the fight against fraud, allowing insurers to identify and prevent fraudulent activity, instead of scrambling to ameliorate its effects after the fact. 

Generative AI can analyze large volumes of data and identify patterns or anomalies that may indicate fraudulent activity. This analysis can result in surfacing patterns of behavior consistent with fraudulent claims, such as multiple claims filed within a short time, injuries claims inconsistent with the reported incident or those filed from locations known to be associated with fraud. By identifying these patterns early on, insurers can block payments to claims highlighted as fraudulent, reducing financial losses and protecting customers from potential harm. 

As the market continues to evolve and generative AI tools become more sophisticated and powerful, insurers that embrace these technological developments will gain a competitive advantage and thrive in the face of disruption. However, it is important to recognize that there are also challenges associated with the adoption of generative AI in the insurance industry, including data privacy concerns, regulatory compliance and the need for skilled data analysts and other professionals to manage and interpret the data generated by these systems.

KEY TAKEAWAYS:

--Technology has made an unprecedented amount of data on natural disasters available to anyone who needs it.

--MGAs and technology companies will start building better disaster-related insurance products.

--They still need more and better tools so their efforts can scale.

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We’re four months into 2023, and already California has been hit with multiple flash floods, skiers were left snowless in the French mountains and earthquakes shattered cities in Turkey, Pakistan and the U.K. Natural disasters used to happen once every few years -- some were considered “once in a lifetime.” Today, they have become a part of our daily news agenda. 

Technological advancements have finally democratized access to vast amounts of data on these natural disasters. We’ve never been better prepared to understand what could happen tomorrow. 

Bridging the data gap with new tech 

Tech has a deflationary and democratizing effect. In the past, hurricane data and modeling was beholden to a supercomputer that certain specific people, governments and organizations were given access to. Today, three people in a startup can access this data through the cloud and generate high-performance complex calculations at scale. 

However, you can give a person paints and brushes, but they do not automatically become Picasso. Data is only as valuable as the way you use it. On one hand, the more relevant data you access, the better your risk modeling should become. On the other, if you still rely on legacy technology and processes like Excel spreadsheets, this cornucopia of data will instead create unyielding data pools that don’t produce improved insights. Pricing tomorrow’s risk cannot be done with yesterday’s tools. Only with modern tools and technology can businesses generate actionable insights that feed into complex issues like climate change. 

Data transparency and consent essential 

The sheer amount of natural disasters the world is enduring means that, whether for a traditional insurance behemoth or a scrappy insurtech startup, data is available to better predict the risk of these disasters happening tomorrow. But data has a bad reputation -- for too long, businesses have hidden data mining details in the small print of privacy agreements most never bother to read. Do you know how and why a business is storing your data? 

Data should be used to create better, fairer policies in insurance and to explain when policies change. You can’t argue against an increase in car insurance if there’s data showing you driving recklessly, for example. Data can deliver clarity and causality; it can explain risks and costs, but only when explicit consent is given to access such data. 

See also: Improving Communication During Disasters

Modeling for tomorrow 

We’re seeing a huge amount of investment in products like parametric insurance and other related modern forms of coverage to do with climate-related risks. When you look at the challenges faced by some of the biggest insurance companies in the world, you see that a lot of them are exposed to natural perils, like earthquakes and hurricanes, which means the providers of technology in that space are incredibly motivated to make things better. If clients are facing losses that your models are not allowing for, there is a huge disconnect, which will push incumbents to innovate. 

Of course, there’s always more to be done. It’s the nature of the innovation game. As we see more advancement in data science and analytical technology, we’ll start to see MGAs and other technology companies emerge that can actually build dedicated products for businesses around parametric and disaster-related insurance. In fact, we’re already seeing new entrants like Descartes do exactly that. These are companies that you wouldn’t have been able to build just 10 years ago, but advancements in tech and an increase in skilled analytical professionals have made it possible. A 100-person-strong data science team just wasn’t possible in the past. 

The democratization of data means insurers and reinsurers have the data to make better decisions, but not necessarily the tools to do so at pace and scale. It's incredibly important that catastrophe insurance evolves to support businesses -- it can’t be so expensive no one can afford it. That’s not a useful form of protection, yet catastrophe insurance is going to be critical in certain areas for companies to survive. That means as an industry, we need to leverage all the advances in technology to make more of the world insurable.

KEY TAKEAWAYS:

--The 2022 deals show a growing percentage of Tier 4 insurers and MGAs purchasing core systems, indicating how important digital transformation has become to compete in the P&C market.

--Nearly all deals were deployed in the cloud versus on-premise, signaling a need for modern technology to support core deployments. 

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It would be difficult to dispute how critical core systems are in the insurance business – they are the enablers of significant in-and-out interactions and help drive efficiency and profitability for an insurance company. Today, many insurers and MGAs are accelerating their transformational journeys by upgrading their policy, billing and claims systems. And new research suggests that demand for new core implementations is not slowing down. 

A new research report from SMA examines core systems deals completed in 2022 from 18 of the top solution providers in the market today. The transactions span insurers and MGAs and support personal, commercial and combined personal/commercial lines business. What is clear from the research is that the core systems buying is consistent, which, along with continued deployments across all segments over the past few years, indicates a strong market.  

However, a few trends are emerging. The 2022 deals show a growing percentage of Tier 4 insurers and MGAs purchasing core systems, indicating how important digital transformation has become to compete in the P&C market, with more smaller insurers deploying new capabilities. In addition, nearly all deals were deployed in the cloud versus on-premise, signaling a need for modern technology to support core deployments.  

As digital transformation continues to drive innovation, we will continue to see investments in core systems. But it is important for buyers to be mindful of business objectives when choosing solutions – the wrong fit can obstruct goals, but the right solution can enable a digital insurance enterprise to reach new heights.   

For more information on core systems deals and buying trends in 2022, read SMA's recently published research report, "2022 P&C Core Systems Purchasing Trends: Insurance Market Dynamics Shift Foundational Technology Needs.” 

KEY TAKEAWAYS:

--Automation applied to an inefficient operation can simply magnify the inefficiency. 

--You will surely be asked which customers will be affected most by automation, so be prepared.

--Key changes will be cultural, so understand at the outset how much change will be needed -- and tolerated.

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Streamlining and automation often get talked about in the same breath, but there’s a big difference.

Streamlining is essentially simplifying an existing process. That is typically done by removing some unnecessary workflows from the larger effort.

By comparison, automation is “cost cutting by tightening the corners and not cutting them,” as Haresh Sippy, chief founder of Tema India, has put it. In the context of insurance pricing, automation typically means connecting disparate systems and data flows more seamlessly. Rather than just simplifying existing processes, these connections bring some overall structure and governance to the workflow, enable scheduling and triggering of activity and allow for reports to monitor progress. 

Automation shouldn’t just be a matter of saving time – important as that often is – it should bring new sources of value to pricing.

Automation done responsibly

Automation allows for doing more with less -- but automation applied to an inefficient operation can simply magnify the inefficiency. 

Traditional machine learning models, for example, have to effectively "fail" to learn. But will they learn fast enough for certain pricing applications? Automation has to be appropriate to the pricing circumstances for which it is intended.

When looking at how to apply automation responsibly, the six standards recommended by Microsoft are a good starting point: accountability; transparency; fairness; reliability and safety; privacy and security; and inclusiveness.

Improvement must be relative to something relevant

Insurers approach the pricing cycle of Analyze – Decide – Deploy in a multitude of ways, so no two automation projects are going to be the same. 

For example, companies working with traditional, generalized, linear models could make significant improvements (up to 40% resource savings in our experience) by automating the simplifying, grouping and curve-fitting factors that could lead to more competitive or segmented pricing. A next step could be the automated tuning of factor parameters and interactions, leading to applications that assist the interpretation of results.

The key is to identify where automation can improve your pricing process and deliver the most value.

Automation may do more than just replace what previously would have been done manually. Machines may reveal pricing insights that wouldn’t typically have been uncovered. Often, automation can serve to triage the value of making rating updates, as we have seen recently with some companies automating the tracking of potential inflation effects on their books of business.

See also: Insurers Turn to Automation

Which customers will be most affected?

In just about every pricing automation project we’ve worked on where companies are, for example, using technology to integrate and update data from multiple systems to adjust their pricing and are aiming to get new pricing to market quicker, the question arises: “Which customers are going to be most affected, and by how much?” 

In the fairly safe knowledge that the question is coming, automate the response, particularly as impact analysis can be extremely time-consuming if done manually.

Another reason for being ready for the question is increasing interest from regulators in understanding how machine learning and automation are driving pricing decisions. 

Key challenges are often cultural

Automation doesn’t necessarily always sit easily with established pricing practices. It pays to determine what those most involved are prepared to let go and the acceptable levels of scrutiny and review of automated processes at the outset. 

There is likely to be a need to introduce new working practices, because breaks or barriers in an automation-enhanced workflow can limit the benefits of automation. For example, a company that aspires to automated delivery of pricing updates can face real problems if the hand-off from pricing/product teams to IT/rate deployment teams is overly manual and complex.

Check out Majesco’s latest research report to better understand the strategic priorities and investments needed to adapt to today’s market challenges and focus areas for new products, value-added services, channels and digital expectations.

Read Now

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