Tag Archives: data analytics

The Key to ‘Augmented Intelligence’

As the insurance industry undergoes a massive digital disruption, it creates a sense of urgency and forces insurers to face risks and challenges, including the increasingly complex nature of processes and operations, the rapid evolution of technology and an increase in fraud. Concurrently, the data sets collected by insurers have practically exploded in terms of volume, speed, format, accuracy—and the value they can bring to those companies that know how to harvest it. 

Given the exponential pace of change, insurance leaders need to understand the implications of these trends, especially from a data and AI perspective, and consider carefully how they should respond. Augmented intelligence is changing the paradigm, helping insurance companies evolve processes, cut costs and improve customer experience with faster insights. 

The Age of ‘Augmented Insurance’

To keep pace with the disruptions, insurance organizations keep evolving their distribution strategies, explore new partnerships, alter their products and transform how they use technology to deliver upon their strategy–all based on data and analytics insights. Many insurance companies already use predictive analytics to anticipate possible future customer behavior (including risk of cancellation), identify fraud risks, triage claims, anticipate trends and predict prices. But all this has required significant investment in sophisticated tools, technologies, infrastructure and–most importantly–people. Fully automated processes may work to speed up operational activities, but strategic thinking has required insights that are curated, contextual and trustworthy. Augmented intelligence breaks this dependency on manual intervention for curating deep, advanced and contextual insights.

The principle behind augmented intelligence is to act as a force multiplier to human intelligence, autonomously managing complex data processing and analytical tasks, enabling businesses to make faster and smarter decisions. As a result, it allows data scientists and analysts to focus on solving blue sky queries and data science projects and removing the burden of ad-hoc insight and narrative generation.

The AI Imperative for Insurers

Insurers today are compelled by their existing and emerging competitors to deliver new offerings to better meet consumer needs and preferences. Recent advances in artificial intelligence, machine learning and augmented intelligence have vastly changed the analytic landscape by removing long-entrenched barriers and making advanced analytics platforms much more accessible to insurers. These new platforms have made it possible for key stakeholders such as underwriters, agents and claims adjudicators to get answers to complex business questions–like why did my claims revenue fall? or what will happen if I increase my underwriter margin by x%? and to make informed decisions based on the answers. 

Whether the goal is to maximize market share, increase profitability, optimize cost–or some combination of these–insurance stakeholders require a multipronged strategy and actionable insights to achieve their objectives. They should be able to:

  1. Analyze key signals and performance trends from various business divisions in real time.
  2. Perform root-cause analysis to arrive at key measures that affect performance and understand why and how performance can be improved. 
  3. Run multiple scenarios by changing the key inputs and impact on the targeted key performance indicator (KPI) and select the optimized strategy based on it.
  4. Design the next-best-move-based cognitive recommendations that take both internal and external factors into consideration.

Augmented intelligence uses machine learning algorithms to automate data and analytics processes, significantly reducing the time-consuming exploration, explanation, prediction and prescription analytics processes, as well as contextualizing the insights to user personas – we are talking about cutting down weeks of turnaround time across several decision support analysts, to near real time and no analyst intervention. Products that truly support advanced augmented analytics capabilities deliver on the promise of comprehensiveness and depth of insights across the value chain at the speed at which the business needs them; and because these are smart products they also overcome the challenges related to low adoption of analytics with a self-service enriched, personalized experience for the end user. 

See also: A New Burst for Augmented Reality

Solving for Various Personas

1. Maximize Productivity

2. Reduce Costs 

3. Optimize Business Processes  

Checklist for Augmented Intelligence Implementation

When implementing an augmented intelligence initiative, insurers must think in terms of the full scope and implications for the organization. A few caveats to consider before going full steam on augmented intelligence strategy are: 

1. Identify the relevant use cases to experiment — Augmented intelligence tools should ideally increase the breadth of analytics capabilities available to end users–which means use cases should be prioritized keeping this goal in mind. Additionally, rather than conducting use case discovery workshops with IT and business intelligence stakeholders alone, ensure the involvement of functional business leaders at the very onset to capture the specific business needs. This will result in smooth implementation processes as well as high adoption rates across functional roles.

2. Take stock of your use case data and infrastructure — While data is the common denominator for any successful artificial intelligence program, you also need to ensure your data has the relevant measures or drivers to run advanced analytics models. For example, if you do not account for drivers and causal factors in your claims data, the augmented analytics tool will not be able to explain the phenomena driving the changes. Additionally, augmented analytics projects require infrastructure that can support large data sets and run millions of queries and advanced machine learning models in seconds. Whether on premises or on cloud, always consider the data needs and infrastructure requirements. Ensure they are in line with the identified use cases so as not to compromise on the solution’s efficiency or speed of delivering insights.

3. Orchestrate with existing BI applications — As the name suggests, augmented intelligence “augments” the potential of your existing analytic and insights assets. Don’t consider it as a replacement to your existing dashboards or BI tools. Choose a solution that can seamlessly blend with their existing architecture and doesn’t require heavy architectural modifications. 

4. Select the right augmented intelligence partner — Your success with augmented intelligence depends on who you entrust it with to take it to the finish line. Having the relevant capabilities that can support the varied requirements as well as devise ways to overcome the common adoption hurdles associated with analytics tools is critical. Moreover, if the vendor doesn’t have a road map on how to further develop the product, or have a support team of domain experts that can help you design new use cases, chances are your experiment will meet a pre-mature death. 

See also: Untapped Potential of Artificial Intelligence


The ability to rapidly respond to an uncertain environment is expected to become a new core competency. Augmented analytics should be viewed as an always-on, immersive system that guides key stakeholders and provides visibility for lines of business, teams and locations. Insurers need to graduate employees from tedious manual processes, focusing their efforts on decision-making that adds business value instead. Insurers need to think about how augmented intelligence can become a key enabler of strategic choices, and not a barrier to success.

Better Analytics = Better Decisions

While data analytics has become increasingly important in the insurance industry in recent years, misclassification and missed opportunities continue to be major issues for many MGAs, brokers, carriers and vendors. Limited information is one of the most important factors, and more comprehensive access to better third-party data can improve decisions at all stages of an insurance policy lifecycle, from point of sale to policy maintenance and renewal.

It is important to identify some of the major roadblocks to securing valuable, accurate data. Antiquated data collection systems (with many systems built 30 years ago and not pertinent in today’s market) can have a significant negative impact. Other identified limitations of existing data analytics products include that “non-claims” are not differentiated from “not founds,” that policy and claims data are not linked, that carrier contributions are not vetted for accuracy (with up to 30% material errors or omissions) and that, perhaps most significantly, carriers and their agents often know little about the consumer at the initial point of contact.  

In addition, insurance professionals had to access multiple applications and field separate calls for each of the data sources and types of information, a time-consuming and cumbersome process.

See also: Achieving a ‘Logical Data Fabric’

But innovative technology can provide instantaneous access to better, more comprehensive data. It is now possible — as we are showing — to use application programming interfaces (APIs) to integrate data on a single platform and provide carriers, MGAs and brokers with immediate access to the prescriptive scores and comprehensive data they need, at the time needed, to allow for better decisions surrounding the sale, and growth, of policies. What we call a “single source” point of entry for all information, regardless of the source or type, provides a high-confidence hit rate to the prescriptive analytics and the pre-arranged knockout logic that facilitates rapid decisions based on extensive data into consumer behavior and insight.  

In addition, by harnessing the power of more accurate and complete information through prescriptive analytics earlier in the customer acquisition process and providing more opportunity for expansion and customer retention, today’s technology can help insurance industry professionals avoid the missed opportunities throughout the insurance lifecycle that have limited their potential.

Do You Know What You Don’t Know?

On March 12, 1928, William Mulholland, Harvey Van Norman and Tony Harnischfeger hiked down the dry side of the newly filled St. Francis Dam in California, inspecting worrisome leaks. Mullholland was the mastermind behind Los Angeles’ new aqueduct — a 233-mile series of man-made rivers and reservoirs that would eventually bring much-needed water to the San Fernando Valley. Before he left that day, Mulholland reassured Harnischfeger that, even though the leaks needed to be fixed, they didn’t represent a serious structural danger. That evening, the dam gave way, and 400 people living below the dam, including Tony Harnischfeger, were washed down the valley. It was a tragedy that could have been avoided.

We still have preventable tragedies, such as breadth of the pandemic and the Beirut explosion, but we also have advanced science that can give us quick vaccines, improvements in cyber security, fewer auto accidents through safer vehicles and greater access to relevant data from health and wellness to weather and water leaks.

Those of us in insurance technology know how knowledge can help us take advantage of opportunities and avoid risks. At Majesco, to help us look ahead, we gather insurer viewpoints and assess strategic priorities. This year’s survey report, Strategic Priories 2021: The Insurance Industry Shift Hits Hyper-Acceleration for Digital Business Models, the first of two reports, is especially crucial to an understanding of priority shifts: how COVID-19 affected insurer priorities and how those who are accelerating key strategies are leaping forward. 

In today’s blog, we’ll see that the COVID pandemic is not the only challenge insurers face. We’ll take a close look at what concerns insurers today.

Internal Challenges — Insurance Execs Share Their Views

Majesco surveyed insurance executives regarding these internal challenges:

  • Digital capabilities
  • Data security
  • Innovation
  • Legacy systems
  • Budget
  • Aligning IT and business strategies
  • Data and analytics capabilities
  • Talent (retention, availability)
  • Change management
  • Aging workforce/retirements
  • Post-COVID work environment

Of the 11 internal challenges in the survey, five show indications of becoming more the “norm” of doing business (Figure 1): Digital capabilities, Aligning IT and business strategies, Data and analytics capabilities, Data security and Talent (availability, retention). These challenges showed steady increases in concern over time but peaked in 2018-19 and have trended lower over the past two years. This trend likely suggests that companies have adapted to these norms through improved capabilities, adjusting expectations or increased confidence via the benefit of experience gained. 

However, the question remains … Do these perceptions match reality? A few examples:

  • Many insurers embraced portals, but most of these do not deliver a next-gen, holistic digital experience that customers, employees and distribution partners want and expect and that is part of a digital maturity that is needed.
  • Insurers have made progress getting their internal data sources in order but still struggle to realize the benefits of a fully integrated data environment consisting of internal and new external structured and unstructured sources, with AI/ML-generated insights. 
  • Given increasingly severe data breaches, data security may be more of an illusion than reality for many critical industries like insurance and why the need for cyber insurance is so critical.
  • The reliance on an aging IT workforce to keep old legacy systems running should shift insurer priorities to rapid legacy transformation, including no code/low code capabilities.

Figure 1: Declining levels of concern about internal challenges

The challenges regarding Budget and Legacy systems remain steady, with above average concern in every survey. Budgets will likely always be a universal source of discomfort, but as companies begin to establish new operating models that have new cost structures and shift IT expenses from capital expenditures to operating expenditures, companies open up the opportunity to reallocate resources to new initiatives. 

While many insurers have completed or begun legacy transformation projects, many of these are non-platform modern, on-premise implementations that are the “new legacy” systems and are now being replaced by next-gen, cloud-based digital insurance platforms. This next-gen platform has superior capabilities that meet the needs of today’s and tomorrow’s insurance customers, including no code/low code capabilities. 

See also: 5 Things to Know When Integrating AI

Interestingly, four new internal challenges in this year’s results all garnered lower levels of concern: Innovation and Change management, Digital and Data/Analytics capabilities and IT-Business alignment. Low levels of concern over Aging workforce/retirements and post-COVID work environment align with the downward trend in concern over Talent (availability, retention). These could be blind spots for many companies, which we’ll discuss in a moment.

Talent and Retention — IT Crisis or Minor Business Conundrum?

It is not surprising that IT executives are much more concerned about internal talent issues than their business counterparts, as seen in figure 2. Their 25% higher concern about the availability and retention of talent is driven by the fact that they face stiff competition with insurtech and Big Tech for employees with the skills needed to transform to digital-first insurers. Until they can achieve the transformation, however, IT needs to retain as much of its aging workforce as possible to maintain legacy systems and keep the current business running. This lack of alignment is problematic, meaning resources are allocated to business as usual rather than focused on creating the business needed for the future.

Figure 2: Levels of concern over talent issues, IT vs. Business

External Challenges — Insurance Execs Share Their Views

Majesco surveyed insurance executives regarding these external challenges:

  • Changing customer expectations
  • Emerging technologies
  • Regulatory requirements
  • COVID-19’s impact on target markets
  • Pace of change
  • Insurtech
  • Growing market availability of new/innovative insurance products
  • New competition from outside the insurance industry
  • New competition from inside the insurance industry
  • The rise of direct sales (B2C and B2B)
  • The rising cost of the agent channel
  • AM Best innovation score

This year, we see declining concern for several external challenges, including Changing customer expectations, Regulatory requirements, Pace of change and New competition from inside the insurance industry. It appears that insurers are feeling more confident in these areas, either through improved capabilities, adjusted expectations or experience (or a combination of these). 

However, as with the internal challenges, is there a risk of being too comfortable, confident and complacent, particularly coming out of COVID? Andreessen Horowitz says that e-commerce increased more in the last six months than in the entire decade beforehand! Add to this the emerging customer experiences from fintech and retailers such as Sofi and Amazon that are creating experiences, rather than transactions. Majesco research indicates that customers are willing to share their data if it leads to more personalized products and pricing that fit their changing lifestyles and risks, like on-demand and embedded insurance.

In addition, concerns have been nearly flat or declined slightly in concern over Emerging technologies, New competition from insurtech, startups, MGAs and New competition from tech giants from outside the insurance industry. This last challenge issue is a concerning blind spot, as experimentation and market entries by players from outside the industry accelerates. Consider a few recent examples:

  • Intuit launches QuickBooks insurance and 401K services — QuickBooks customers can now protect their businesses with comprehensive insurance coverage and offer their employees a 401(k) benefit, traditionally offered only by large companies. This integration will enable QuickBooks users to seamlessly obtain a customized quote and easily purchase general liability, professional liability and workers’ compensation coverage from Next Insurance with a few clicks of a button, directly from their QuickBooks account.
  • Petco launches insurance — In October, Petco announced the launch of Vital Care, a paid annual plan providing pet parents with a convenient, affordable way to meet their pets’ routine wellness needs.
  • Walmart offers pet insurance and health insurance — In November, Walmart announced it added pet insurance as animal adoptions soar during the pandemic. The company is offering insurance through Petplan and connecting people to pet sitters or dog walkers through Rover. In July 2020, it was announced that Walmart was launching a health insurance arm — dubbed Walmart Insurance Services — to sell plans to consumers. Walmart’s low prices and wide footprint could pose a threat to insurance startups — especially those firms breaking into the Medicare Advantage market.
  • Tesla insurance approved in Texas — In December 2020, the Texas Department of Insurance approved filings for insurance to be underwritten by a third-party Austin-based insurer and distributed by Tesla Insurance Services, which follows the company’s 2019 launch in California. Tesla’s Texas program uses driver behavior-based data collected by the vehicle as an input to determining at-fault collision rates. The program also covers cyber identity fraud expenses, electronic key replacement, loss or damage to the original wall charger and loss when the vehicle is being driven by autopilot.

As industry and market trends evolve, so do the topics covered by our annual survey. We added five new external challenges this year. Although these don’t have historical results for trending, COVID’s Impact on Your Target Markets had the fourth-highest overall ranking among all 14 external challenges, reflecting the real uncertainty on the pandemic’s effect on the economic health of consumers and businesses, and the impact for insurance. 

Growing market availability of new/innovative insurance products ranked sixth, tied with insurtech, ranking higher than the challenges from new competition from inside and outside the industry, suggesting that incumbent insurers more strongly associate insurtech with these new offerings – again, another potential blind spot on new competition.

The remaining three new issues, rise of direct sales, rising cost of the agent channel and AM Best innovation score, had considerably lower ratings than all other external challenges. (See Figure 3.) Again, are these blind spots, or do insurers have a good handle on these challenges? With e-commerce rising across all industries and demographic shifts in channel preference, we expected higher ratings on these two channel challenges. With the lackluster results of AM Best’s first Innovation Assessment mentioned earlier, it is somewhat surprising that this issue came in dead last.

Figure 3: New external challenges added to the survey

Key Differences Among Segments

Aggregating the ratings across all internal and external challenges highlights some key differences between direct written premium (DWP) tiers and line of business segments. The largest tier is much more concerned about internal challenges overall. This suggests that the complexity of the large organizations leads to a higher number of challenges that can be a hindrance to agility. Smaller companies should have an advantage here with less bureaucracy and complexity – but old systems could also be a hindrance negating this advantage. Replacing these systems with next-gen core platforms should now be of the utmost urgency.

See also: Claims Development for COVID (Part 1)

Companies with new products are much more concerned about the internal and external challenges, as highlighted by the aggregated rating comparisons. (See Figure 4.) The L&A/Group Only segment is a close second on five of these. This makes sense when you consider that these companies are challenging the status quo with non-traditional products and blazing trails, which can exacerbate existing challenges (e.g. IT, talent, etc.) as well as bringing up brand new challenges.

Figure 4: Differences in aggregate levels of concern over internal and external challenges by lines of business

How does your opinion rank within these challenges? Can knowledge open your organization up to the opportunities in our midst?

Do you know what you don’t know? 

Covering for a Gap in Workers Comp Data

What happens when a key data source becomes less available, reducing carriers’ ability to evaluate risk? This has happened during the pandemic in workers’ compensation.

In workers’ compensation, OSHA is one of the top data sources that underwriters use. In particular, underwriters will look at OSHA inspections and violations to measure some aspects of the risk. 

Here at Convr, our focus has been to help carriers with the right insights at the right time for better decision-making, and we found, using a vast data pool, that planned inspections dropped 48% in 2020.

One reason is fewer claims; as operational capacity was reduced or suspended for many industries in 2020, workers’ compensation claims dropped by over 20%. As accidents declined, inspections that normally would have followed weren’t needed. In addition, OSHA reduced the number of planned inspections for the safety of their inspectors.

The reduction in inspections has led to a lack of reliable information for workers’ compensation carriers to evaluate businesses — but this is where technology comes in. With the help of artificial intelligence and advanced analytics, carriers can still determine the risk of a business by looking at past patterns.

These past patterns include types of structured and unstructured information that data scientists refer to as “features” in machine learning models. Often, significant features are high-dimensional nonlinear combinations of company and property characteristics, such as the size of the business, the year it was established and prior violations. Other features include social media information and product and services data.

See also: 9 Months on: COVID and Workers’ Comp

Applying AI to our data lake, which is informed by over 2,000 data sources, Convr has determined that, in place of the normal volume of OSHA inspections, carriers can use a workplace safety model to accurately quantify risk. A workplace safety model consists of a machine learning model that predicts how safe a workplace will be based on OSHA data and the different data sources mentioned above.

Companies labeled as the riskiest 10% by Convr’s proprietary workplace safety risk scoring model observed three times as many future violations as those labeled as the median risk.

COVID-19 has proven that circumstances can change unexpectedly, and carriers have to become adaptable and flexible enough to implement alternative solutions to minimize the impact. Advanced AI models, like the one Convr has created to quantify workplace safety, hold tremendous value for carriers, enabling them to better understand risks even when traditional sources of information are limited or unavailable.

When armed with technological advancements such as these, carriers are equipped with the right tools for better decision-making and optimal underwriting results.

How Analytics Can Tame ‘Social Inflation’

“Social inflation” is considered one of the major emerging risks that the insurance industry must face. While people may misconstrue the term as relating to the rising impact of social media on online behavior of netizens, it has actually to do with increasingly hostile legal environment that insurance carriers are facing today. This manifests in the form of much larger verdicts, liberal treatment of claims by boards, more aggressive plaintiff bars, etc. This article explains the trend and describes measures that carriers can deploy to keep a check on increasing legal expenditure.

Here are some signs of the phenomenon:

  • A major P&C insurer anticipates $40 million in quarterly legal costs for property losses alone
  • There is a 94% increase in assignment of benefit (AOB) lawsuits in the state of Florida in the last five years
  • An increased probability of “nuclear verdicts” (> $10 million) is a real trend. In 2018 alone, the top 100 verdicts ranged from $22 million to $4.6 billion

Here are some factors driving the trend:

  • Litigation Funding: Propelled by easy capital availability, a new class of plaintiff attorney funding model has emerged in the last 10 years. Essentially, this model provides funding for legal expenses to plaintiff attorneys in exchange for a portion of the judgment or settlement. The model is good in the sense that it levels the playing field against large, well-funded corporations, but the unintended consequences (for insurance companies) is an exponential increase in attorney representation and pursuit of aggressive legal strategies. Approximately $9 billion has been committed to this “industry.”
  • Rising anger against big corporations: The very premise of the big corporation versus the individual scenario is driven by anger. The perception among the consumers and the jurors is that a corporation has only one goal: profit. Stagnation of incomes/wages is another contributing factor to this mindset. Big businesses in America like to talk about the good jobs they provide, but median salaries in the U.S. have been flat for decades. This is not because of a failure of workers to become more productive; there were gains in productivity, but they did not go to workers. Gains mostly flowed to the organizations and their shareholders, including executives who received sizable stock-based compensation. Hourly compensation for workers remained practically flat.
  • Large verdicts being driven by general social pessimism and jury sentiments: New and interesting patterns are being observed in jury behavior, especially in personal injury and liability claims. Emotion and trust play a big role in how a jury rules. Some of the key reasons behind these large verdicts are: jurors’ distrust in big corporations and their lawyers; jurors paying less attention to lengthy testimonies and complex explanations; impact of social media on how millennial jurors view the court system; impact of emotional stories on how a jury thinks; and the “what if it were me?” attitude influencing how jurors approach justice. 

Leaving aside the financial burden from social inflation, which is quite significant on its own, increased litigation also affects carriers in other ways. There can be inaccuracies in reserving, which is based on counsel’s estimates of litigation spending. There can be negative press and poor customer engagement/satisfaction. And claims can increase in complexity, delaying settlement. .

Many insurers are either in the early stages of dealing with social inflation or are not moving as fast as they’d like on the problem. The most common reasons include:

  • Claim handling practices are often inadequately data-driven
  • There is limited ability to foresee litigation
  • There is lack of trust in analytical models
  • The company takes a semi-reactive approach for claim settlement and negotiations
  • Assignments are inefficient, either to claim handlers or attorneys

What can insurers do to manage this growing challenge?

See also: The Data Journey Into the New Normal

Enter machine learning and artificial intelligence

Use of analytics from first notification of loss (FNOL) until the claim is paid is now a norm rather than a competitive edge. All large carriers invest heavily in use cases such as fraud detection, severity-based claim assignment, automatic loss estimation, recovery optimization, etc. However, given the complex nature of how litigated claims are handled, only a few top U.S. carriers are able to weave these capabilities effectively into business processes. Insurance carriers that successfully use analytics to drive business process change in claims litigation will stay ahead of this massive threat.

There are two unmistakable trends that carriers need to leverage:

  1. Aggressive use of information sitting in claims and policy systems (structured attributes, adjuster notes) to develop signals around plaintiff attorney behavior. These signals then need to be deployed within claims operations to encourage early case assessment and litigation prevention.
  2. Use of increasingly clean and comprehensive sources of external litigation information (from state courts, where most insurance litigation lies) to inform your litigation strategy. This includes past verdicts information by venues, judges, attorney firms, case types, etc. A thoughtful use of this information can help claims adjusters and defense attorneys devise the litigation strategy to avoid worst outcomes. There are multiple firms providing research tools that are based on these. Our recommendation, however, is for carriers to ingest the information, merge it with internal claims data and develop models and tools providing a comprehensive view

How carriers can leverage this trend

Carriers can use predictive and descriptive analytics solutions in claims management to mitigate the problem. At a high level, such solutions can lower ballooning legal costs by avoiding litigation and optimizing litigation strategy. Both these approaches call for incorporating advanced analytics processes and models at the key steps of the claim settlement process. This includes:

  • Advanced analytics and machine learning models to predict and avoid litigation and post-suit strategy formulation
  • Natural language processing (NLP) to leverage unstructured data and extract additional insights
  • External data ingestion to augment internal data and enhance model accuracy
  • Enhanced data management capabilities

Any insurance claims raised can result into any of the three possible outcomes: claims without litigation, claims that involve litigation without trial and claims that involve litigation with trial. With the help of the aforementioned technologies, carriers can engineer the following mechanisms in the claims management process (Figure 1) to optimize the legal expenditure for a given product:

  1. A – Prediction of litigation likelihood 
  2. B1 – Defense counsel selection
  3. B2 – Law firm benchmarking 
  4. C1 – Attorney insights 
  5. C2 – Prediction of settlement failure
  6. D – Legal activity duration, legal expense prediction
  7. E – Case-level insights 

See also: Growing Risks of Social Inflation


Claims data within insurance companies is being increasingly seen as a key asset, not a byproduct of the claims process. However, the path to using internal and external sources of data to drive business outcomes is long and arduous. It is becoming increasingly important for carriers to incorporate insurance analytics processes geared toward optimizing legal spending. To achieve this, insurers require a combination of capabilities to these engagements, i.e. ability to handle big data, ability to develop advanced analytics solutions and knowledge of “what, why and how to deploy” in claims business processes.