Why Data Science is Integral to an Insurance Business Today?

Did you realize that whether you are riding the Uber, instructing Alexa, wearing fitness trackers or using the smartphone, a vast amount of data is being generated? Interestingly, an estimate by experts reveals that by 2025, the volume of digital data will increase to 163 zettabytes. Today, this data acts as a treasure trove for most companies who consider evaluating it to generate actionable insights. This trend is sector-agnostic, and across domains, we see a similar pattern – be it in manufacturing, life science and healthcare, energy, retail, utilities or BFSI.  The banking and financial services industry is one domain where data generated and handled is significantly enormous. As the electronic records only grow in number the financial services industry is turning to big data analytics to store data, generate actionable insights, and boost scalability. The insurers are not lagging behind, where they have access to rich sources of data and have figured out the right blend of what, why, and how to use the data to thrive in a highly competitive business environment.

A quick look at the challenges which plague today’s insurance companies tells us why harnessing customer data is more than important now. Growing risks, high operational costs, a volatile market, customer renewal, and churn remain major concerns. Data analytics, a significant part of data science helps address these challenges and facilitates claims processing, pricing strategies, and fraud management. Let’s look at some of these use cases in detail:

Risk assessment

Identifying risks and taking steps to mitigate them is a critical responsibility of any insurer, which becomes even more important when underwriting policies. There are cases where policies are written for storefronts and warehouses. In such cases, fixed data sets which include political, geographic, and economic data are analyzed. Furthermore, personal data of customers often prove to be an asset which when efficiently used helps analyze both types of risk— pure and speculative. The matrix model needs mention here which supports accurate risk assessment and facilitates meaningful risk discussions. In this 3×3 model, the event consequences lie along one axis and the event frequency on the other. Each group in the matrix represents risks at some level. The model is dependent on algorithms that detect and combine the data revolving around individual risks which vary in nature, effect, and character. This helps list each risk, rate its likelihood and severity and identify the highest-priority risks which may require immediate remediation.

Claims management

Apart from risk management, claims assessment is another important area where analytics plays a critical role. It is a given that faster and better insights empower insurers to ascertain and interpret what is going on in the claims process. However, data needs to be combined with analytics expertise to not only improve the speed of identifying hidden correlations but also respond to potential challenges timely. Analytics, in particular, helps prioritize claims, settle straightforward claims, tag the complex claims for further inquiry, and reduce the cycle time. Let’s take a case in point. A particular insurer’s data revealed that the claim cycle is abnormally high only in a specific geography. When data was dissected further, it was discovered that claim time for a particular claim in that geography was almost twice, and this was the case in bodily injury claims. This led to the understanding that by addressing the root cause of the cycle time, the insurer could decrease the average cycle time. The use of analytics led the insurer to specific questions such as the number of adjusters available to address bodily injury claims, idle time among adjusters, and so on which when solved expedited the claim cycle time.

Fraud Management

While we already know by now how analytics contributes to risk assessment and claims management, it also helps detect frauds. Handling frauds manually have always proved costly for insurers, more so when high-value frauds go undetected. Three innovative analytics methods in which frauds are detected include:

  • Social Network Analysis (SNA)

In the case of SNA, both structured and unstructured data are fed into the ETL tool (Extract, Transform and Load tool). The analytics team then uses information across sources and calculates the likelihood of fraud based on several factors. Technologies that are integrated into the predictive modelling process for fraud management include text mining, content categorization, sentiment analysis along with social network analysis. 

  • Predictive analytics

The second method is predictive analytics, where text analytics and sentiment analysis are used to look at Big Data for fraud detection. Often the claim reports span across multiple pages leaving very little room for insurers to detect scams. Here analytics comes to use which not only helps sift through unstructured data but also detect possible frauds. Once the fraudulent claims are spotted, it accelerates the payment process of the legitimate claims leading to a higher number of satisfied customers. 

  • Social Customer Relationship Management (SCRM)

Social CRM or social customer relationship management is the third process in which the insurers link social media to the CRM. Insurers Invest in sophisticated analytics tools to verify the validity of claims and detect fraudulent crimes. This enables greater transparency with genuine customers and increases the faith they show in the insurance organization. Introducing social customer relationship management leads to a shift to a customer-centric ecosystem which is again beneficial to the business in the long run.

Final thoughts

As we reach the cusp of a massive data science revolution, the near future will witness insurance companies only increasingly using data science analytics. And why not when it helps optimize marketing strategies, enhance income, improve business and reduce costs. It will not be surprising if the use of data science in insurance makes huge leaps in the near future, and insurance businesses leverage it to the fullest. We may rather believe in this optimistic trend and wait and see what awaits us at the end of the road.

How Artificial Intelligence is Fast Transforming Aviation?

According to forecasts, AI investments in the global
aviation market are expected to reach a  $2,222.5 million by 2025, demonstrating a CAGR
of 46.65% in the
period between 2018 and 2025. This trend is fast disrupting how the players
approach their data streams, operations, and customer-centricity goals. Interestingly,
we can already witness certain AI-specific use cases in aviation which include
baggage-check in, client inquiries, plane fuel enhancement, and facial
recognition. You may wonder why the players are taking resort to AI for facial
recognition — for faster boarding. Kiosks for facial recognition cut down on
boarding time, resulting in greater convenience and a higher number of
satisfied travelers.

Why do airlines need
to board the AI bandwagon?

However, achieving high customer satisfaction
levels may become more challenging as airline traffic doubles up to almost
eight billion in
the next two decades. Most airlines may struggle to keep pace with consumer
demand. Reasons for the robust demand have been identified as strong economic
growth, a growing middle class, and increased spending on services. Here too AI
has a crucial role to play.

With the high proliferation of data, as the
airlines collect a vast amount of customer preference info, scheduling history,
and employee information, developing practical AI solutions becomes easier.
Airlines use the data sets and advanced AI-algorithms to offer superior personalized
services to a larger consumer base and improve employee incentives, ensuring
that services do not deteriorate despite rising consumer demand.

Changes we can witness
already

While the traffic is all set to
increase, the competition too will heighten. In such a scenario, AI is being
leveraged by the aviation industry for autopilot systems, which assists the
pilot in controlling the plane better. They reduce repetitive tasks of the
pilots, manage turbulence and help in weather forecasts determining whether it
is safe to fly. In case of adverse weather, AI engines suggest alternate
routes.

Although auto-pilot remains the most
successful use case of AI, the use of the technology stretches beyond
auto-pilot in multiple ways:

  • AI disentangles the procedure related to
    baggage screening in a few airports. Osaka Airport has introduced an AI
    platform—Syntech One 200, which screens the baggage for multiple passenger
    lanes saving on resources and time. This AI software can also detect knives,
    threats, and weapons both ensuring and bolstering security.
  • Also, AI-based virtual assistants are being
    used to improve client services. These assistants answer basic queries allowing
    the customer service personnel adequate time to take care of more pressing issues.
    A leading airline is already using Alexa to answer common customer queries such
    as the status of a trip, check-in request, and whether wi-fi is accessible on a
    new flight.
  • AI helps detect booking-related problems. It
    analyses past flyer information, historical data and monitors the weather to
    predict which passengers will not show up or may swap to another flight. It
    provides the ground staff with up-to-date information on the number of people
    likely to board the flight.

Use
of AI in fleet and operations management

Besides the above use cases, AI also
finds use in enhancing the fleet and operations management. The Alibaba Group
recently announced that it has provided AI-based solutions to the Beijing
airport where they are using it to help pilots find parking easily. This
AI-system gauges congestion, facilitates flight route optimization, and
analyses real-time data to detect delays.

It would surprise you that besides
flight route optimization, fraud detection in aviation is another area where AI
finds use. Advanced algorithms analyze a customer’s flight and purchase pattern
to detect fraudulent credit card transactions, which saves airlines millions of
dollars, eventually.

Enhancing customer
service for retention

While fleet and operations management
is being well tackled, AI engines also contribute majorly to customer service. It
is for this reason that 52% of
airlines have thought of implementing AI initiatives in their
customer service operations in the next five years. The AI engines adopt
behavior tracking techniques and keep track of purchase history to make
personalized offerings. Intelligent systems carry out sentiment analysis,
evaluating real-time customer reactions to provide actionable insights on how
to improve pre-flight, in-flight, and post-flight service.

Monitoring
the “health” of aircraft

Another interesting
area where AI finds use is predictive maintenance. Airlines are fast embracing
AI to monitor the “health” of aircraft. AI systems predict when a part requires
maintenance, ensuring repairs without delay. Data is fast analyzed, which
enables timely preventive actions. They use AI systems on valves, generators and
brakes which extend the lives of the parts and minimize disruptions.

In case you are
wondering how predictive maintenance is carried out—AI systems make use of Natural
Language Processing (NLP) to scan maintenance logs, predict failure, and
recommend fixes. This saves the maintenance costs of airlines and also keeps
passengers safe. Interestingly, this upgrade to AI has led to a significant
reduction in flight delays owing to malfunctions.

Making the skies friendlier in future

Till now, we
discussed how AI is bringing about changes, but what lies in store for the
future? AI promises to ensure a reduction in the number of bags lost in a
unique way. Using AI, intelligent machines will enable bags to be autonomously
managed from the moment a passenger checks-in to when it arrives at the
destination — all without human intervention. 

Besides, interesting
research is underway in using Fitbit biometric data inflight to offer better
customer care during the journey based on body temperature and heart rate. And, what
more could we ask for, convenience, comfort, and now inflight care based on
biometrics.

As airlines embrace AI, it is us the
end customers who will undeniably stand to benefit the most.  

How Machine Learning is a Game Changer in Manufacturing?

In the prevailing times, the manufacturing process for companies can be cost-intensive and time-consuming if they are not equipped with suitable technologies. One critical and emerging technology which is all set to revolutionize manufacturing is machine learning (ML). From improving productivity to bringing about efficiency gains, ML algorithms have been seen to transform each step of manufacturing. These algorithms reduce the length and cost of manufacturing processes and harness the potential of varied datasets to make smart manufacturing a reality. They collect data from the manufacturing environment and use sophisticated and advanced computing power to arrive at actionable insights.

According to the predictions of a leading market intelligence provider, the smart manufacturing market will be worth $320 billion by 2020 and grow at a projected CAGR of 12.5%. Now, what is smart manufacturing? As most of us are aware that Industry 4.0 is underway, there is a shift from traditional manufacturing to smart manufacturing where machines are trained to understand processes and robots assemble a product with high levels of precision. This primer provides us with insights into how ML finds significant use in modern-day manufacturing.  

ML methods used in manufacturing

The three types of ML methods used in manufacturing include supervised, unsupervised and reinforcement learning. Let’s understand each of the three with use cases:

Supervised learning

Supervised learning is an ML technique where large training data sets are applied to the systems. The system is trained with data that has already been categorized into one or more groups, and the groups here are referred to as labels. The system makes use of the algorithms to understand the data structure and then classifies the output data under the right category. Let’s take the Statistical Learning Theory (SLT) as a case in point. Bayesian Networks are the most well-known application of SLTs. An American multinational information technology company in Corvallis, Oregon manufactures numerous precision products at immensely high speed. One essential component in the production of these products was the alignment of the cap to the base. Hence, the positional accuracy of the cap was critical. The company developed a prototype to monitor the performance of the alignment process in real-time. For the monitoring model, they used Bayesian networks which led to positive results. 

Unsupervised learning

In the case of unsupervised learning, the evaluation is not dependent on pre-classified labeled data. Instead, the algorithms detect patterns in the unclassified data and these groups of related observations are called clusters. The final objective of unsupervised learning is to decipher unknown but evident relationships between the clusters. Clustering is a common example of unsupervised learning which is being adopted by companies such as Acta-Mobilier which provides high quality lacquered furniture. The company was grappling with rework rates almost as high as 30%. This resulted in a significant gap between the increasing customer requirements and the speed of implementing new processes. The company implemented clustering algorithms to address this production logistic problem which eventually reduced the rework rates.

Reinforcement learning

In addition to supervised and unsupervised learning, manufacturers also adopt reinforcement learning. Take for instance, a world leader in industrial robotics which integrates deep reinforcement learning into its industrial robots. Towards the end of 2016, an American tech major collaborated with this leader in factory automation to use the latter’s AI chips in the Smart Factory for efficiency gains.

While the use of ML algorithms will only be on the rise, there are some key areas where the transformation is already evident: 

In supply chain management

It is interesting to note that according to a Gartner study, by 2020, 95% of supply chain vendors will depend on supervised and unsupervised learning. ML algorithms provide supply chain operators with significant insights into how to improve the supply chain performance, anticipate the anomalies in logistics, and identify areas that can be automated leading to scale advantages. ML is revolutionizing supply chain management in four key ways:

  1. Reducing logistics costs
  2. Detecting inconsistent supplier quality levels
  3. Reducing risk and potential for fraud and;
  4. Providing end-to-end supply chain visibility

In predictive maintenance

Predictive maintenance is another critical area in manufacturing which ML is fast transforming. One of the Big Four accounting firms anticipates that implementation of ML and analytics by manufacturers will rise by 38% in the next five years only to enhance predictive maintenance. 

ML specifically helps in:

  • Remaining useful life (RUL) prediction: In this case, the algorithms give insights into when a machine will fail so that maintenance can be scheduled well in advance. Regression is used to calculate the RUL of an asset.
  • Anomalous behavior: Anomalies are detected through time series analysis.
  • Failure diagnosis: ML algorithms are used to diagnose likely failures beforehand. The algorithms also recommend mitigation and maintenance actions after the failure.

The use of ML in predictive maintenance can be understood better with a real-life use case. A manufacturer of industrial equipment in the beverage industry integrated the existing machines with an ML-based monitoring and prediction system. This reduced problems of inefficiency and reactive customer service. It also helped optimize the equipment maintenance schedules which took place at defined time intervals previously and did not cater to maintenance needs in real-time. Eventually, the use of the ML-based prediction system led to business scalability and optimized the cost structure. 

In quality control

Lastly, quality control is another function where ML finds significant use. According to a prediction by Forbes quality testing done with machine learning can boost detection rates by 90%. Machine learning allows algorithms to inspect products and identify flaws. These ML-based algorithms make use of samples in the training set and create a library of the possible defects. Further, they learn from samples to distinguish the faultless from the flawed. 

As ML heralds a new age of predictive manufacturing, optimized supply chains, and enhanced quality control, it will also mean a quantum leap to make Industry 4.0 a reality. What’s more, machine learning being a self-learning system will only lead to better outcomes resulting in improved production throughput at superior quality levels. 

The Fast Evolution of Artificial Intelligence—how will you win?

Artificial Intelligence (AI) is fast making a foray into our personal lives. Take for instance the portrait mode effect while we capture images through smartphones, the social media feeds in our timeline, (it is all curated by AI) when we use Google Maps for navigating or call an Uber. In the case of Uber rides, it is AI that decides the price and car that match our request. Without a doubt, AI is the most transformative technology today. 

Enterprises and AI

As any new technology is meant to improve productivity, businesses especially cannot afford to overlook the potential of AI. Gartner predicts that AI will rank among the top five investments for more than 30% of CIOs by 2020. Going by the McKinsey Global Institute Insight, by 2030, 70% of companies would have adopted at least one type of AI technology. To realize the true potential of AI business will need to take several actions sooner rather than later. By leveraging the first-mover advantage they can take productivity and efficiency to new heights and outpace their rivals. 

In the real scenario, while developments in AI are business-ready there is a strong disconnect between the enthusiasm around AI and its actual deployment in applications. The majority of the AI projects lie in the pilot stage. To catapult to the launch phase companies should look at initiating an assessment of their AI-readiness and identify quick-win opportunities which lead to prompt financial benefits, justifying the deployment costs of AI. 

The proliferation of AI across sectors

To succeed in AI efforts businesses need to unify their AI efforts with the greater business transformation efforts. In the insurance sector, a major health insurance provider is already using AI methods to customize benefit design which has led to a 180% growth in their new member acquisition rate. What lies ahead in aviation? AI-based virtual assistants are helping pilots to increase their efficiency and productivity. A major American airline is using Amazon Alexa to answer common passenger questions. By using AI in predictive maintenance analytics aviation players are trying to predict how and by when aircraft maintenance schedules ought to be completed. 

The healthcare and life sciences sector is also not behind when it comes to the adoption of AI. Healthcare is one sector that is ripe for disruption through AI. Very soon healthcare players will use the power of a popular AI-platform to empower clinicians helping them decide on the most appropriate therapy and also provide decision support with evidence-based data sets. Interestingly, AI is all set to become the cornerstone of precision diagnosis. Without replacing doctors, it will only complement them, ensuring they stay on the top of their game.

Returns from deploying AI

Deploying AI technologies alone is not sufficient, it is equally critical that organizations demand ROI. AI unlocks new revenue streams increasing the top-line figures, it also brings about efficiencies in operations improving the bottom-line. We can use AI to cut down on repetitive tasks, develop new products and bypass the invention process ourselves. AI intervention will only improve customer experience initiatives and marketing and sales functions leading to top-line growth. 

While AI has the potential to boost profitability by an average of 38% by 2035 across 16 industries, it has its limitations. Despite the strong potential, AI systems need to be continually trained and recalibrated. We have reached a stage that AI will not be restricted to performing tasks. Instead, we need to raise it to act as a responsible member of society. According to experts in the next few years, AI will work along with humans in collaboration as their co-workers. We can no longer treat AI as a mere software but rather embrace the concept of citizen AI. 

Need for responsible AI

With great power comes responsibility, and so is the case with AI. Most of the fears and concerns related to AI revolve around loss of privacy, significant biases in decision-making, and gradually losing control over automated systems. Responsible AI ensures transparent, ethical, and accountable usage of AI and guards critical decisions against biased algorithms. The need of the hour is transparent AI which allows one to check whether AI models are well-tested. In organizations, one could ascertain whether the AI models are aligned to the core principles for larger business goals. 

While AI proliferates across most sectors, the skepticism around it will decrease with the right approach and timely decision-making. 

About TCG Digital

TCG Digital accelerates enterprise digital transformation with hyper-contemporary technologies, advanced analytics, data strategy, application development, mobility, RPA, cybersecurity, and IoT to deliver Velocity to Value . 
We are the flagship technology consulting and solutions company of “The Chatterjee Group”, a multi-billion dollar portfolio of corporations. This affiliation empowers us with access to global talent, subject matter expertise, and a ‘1,000 digital minds’. 
Our clients range from major global brands and large government entities, to small and mid-market companies; with a recognizable roster of enterprise logos. We differentiate through deep systems and sectoral knowledge, acute agility, impeccable quality, and ready ease-of-doing business.
Our mantra is simple: Velocity to Value – transformation acceleration for the digital enterprise to deliver rapid, measurable ROI through relentless innovation. Whether you are setting strategy, ready for implementation, or encountering technical drag, TCG Digital brings to bear talent, solutions, and highly performant platforms to catapult your business to successful, sustainable disruptions.

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