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Cognitive Automation and Its Importance for Enterprises

One of Japan’s leading insurance firms — Fukoku Mutual Life Insurance claims to have replaced 34 human tasks with IBM Watson (AI technology).

Cognitive automation is a subset of artificial intelligence that uses advanced technologies like natural language processing, emotion recognition, data mining, and cognitive reasoning to emulate human intelligence. In simple words, cognitive automation uses technology to solve problems with human intelligence.

Cognitive automation vs Robotic Process Automation

The main pillars of cognitive automation

Consider an automated home security system programmed to function based on millions of decisions. It may still encounter situations when it does not know what to do. Machines can make logical decisions in many unforeseen situations using cognitive neuroscience. 

The technologies to make cognition-based decisions possible include natural language processing, text analytics, data mining, machine learning, semantic analytics, and more. The following table gives an overview of the technologies used in cognitive automation.

Machine LearningIt involves improving a system’s performance by learning through real-time interactions and without the need for explicitly programmed instructions.
Data MiningIt is the process of finding meaningful correlations, patterns, and trends from data warehouses/repositories using statistical and mathematical techniques.
Natural Language ProcessingNLP is a computer’s ability to communicate with humans in native languages. 
Cognitive ReasoningIt is the process of imitating human reasoning by engaging in complex content and natural dialogues with people.
Voice RecognitionIt is transcribing human voice and speech and translating it into text or commands.
Optical Character RecognitionIt uses pattern matching to convert scanned documents into corresponding computer text in real-time.
Emotion RecognitionIt is the understanding of a person’s emotional state during voice and text-based interactions.
Recommendation EngineIt is a framework for providing insights/recommendations based on different data components and analytics. For instance, Amazon was one of the first sites to use recommendation engines to make suggestions based on past browsing history and purchases.

Why is cognitive process automation important for enterprises?

Cognitive automation improves the efficiency and quality of computer-generated responses. In fact, cognitive processes are overtaking nearly 20% of service desk interactions. The following factors make cognitive automation next big enhancement for enterprise-level operations –

  1. Cost-effective: Cognitive automation can help companies to save up to 50% of their total spending for FTE, and other related costs.
  2. Operational Efficiency: Automation can enhance employee productivity, leading to better operational efficiency.
  3. Increased accuracy: Such systems are able to derive meaningful predictions from a vast repository of structured and unstructured data with impeccable accuracy. 
  4. Facts-based decision making: Strategic business decisions drill down to facts and experiences. Combining both, cognitive systems offer next level competencies for strategic decision making.
4 benefits of cognitive automation for enterprises

Also read – Cognitive approach vs digital approach in Insurance

Applications of cognitive automation

End-to-end customer service

Enterprises can understand their customer journey and identify the interactions where automation can help. For example, Religare — a leading health insurance company incorporated NLP-powered chatbot into their operations and automated their customer-support and achieved almost 80% FTE savings. Processes like policy renewal, customer query ticket management, handling general customer queries at scale, etc. are possible for the company through chatbots.

Processing transactions

Reconciliation is a tedious yet crucial transaction process. Banking and financial institutions spend enormous time and resources on the process. Paper-based transactions, different time zones, etc. add to the complicacy of settling transactions. With human-like decision-making capabilities, cognitive automation holds a huge prospect of simplifying the transaction-related processes.

Claims processing

In insurance, claims settlement is a huge challenge as it involves reviewing policy documents, coverage, the validity of insured components, fraud analytics, and more. Cognitive systems allow making automated decisions in seconds by analyzing all the claims parameters in real-time.

Also read – How intelligent systems can settle claims in less than 5 minutes


Deloitte’s report on how robotics and cognitive automation will transform the insurance industry states that soon, automation will replace 22.7 million jobs and create 13.6 million new jobs. However, not all operations can be automated. The following are the requirements for successfully automating processes.

  1. Input sources: The input sources should be machine-readable, or needs to be converted into one. Also, there’s a limitation to the number of sources that the system can process for decision making. For instance, in an email management process, you cannot automate the resolution of every individual email. 
  2. Availability of the technology: Cognitive automation combines several technologies like machine learning, natural language processing, analytics, etc. Thus, all the technologies should be available to make automated processes functional. 
  3. Data availability: For the cognitive system to make accurate decisions, there should be sufficient data for modeling purposes.
  4. Risk factor: Processes like underwriting and data reconciliation are good prospects of cognitive automation. However, based on the risk value and practical aspects, human intervention may be required to make the final decision.
  5. Transparency & control: Cognitive automation is still in a nascent stage and humans may overturn machine-made decisions. Therefore, the system design needs to adhere to transparency and control guidelines.

Wrapping up

Cognitive systems are great for deriving meaningful conclusions from unstructured data. Many back and front office operations can be automated for improving efficiency, especially in consumer-facing functions to understand requirements and feedback. For instance, cognitive automation comes with powerful emotion recognition capabilities. It can help with making sense of customer tweets, social updates, through face recognition and analyzing texts. 

Since cognitive automation solutions help enterprises to adapt quickly and respond to new information and insights, it is becoming crucial for customer-centric businesses. The following graph shows how important cognitive technology adoption is for businesses that focus on consumer centricity.

Customer centricity and cognitive technology adoption
Source: Deloitte

Also read – 5 Front office operations you can improve with AI


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Retention playbook for Insurance firms in the backdrop of financial crises

4 minutes read

Belonging to one of the oldest industries in the world, Insurance companies have weathered multiple calamities over the years and have proven themselves to be resilient entities that can truly stand the test of time. Today, however, the industry faces some of its toughest trials yet. Technology has fundamentally changed what it means to be an insurer and the cumulative effects of the pandemic coupled with a weak global economic output have impacted the industry in ways both good and bad.

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Source: Deloitte Services LP Economic Analysis

For instance, the U.S market recorded a sharp dip in GDP in the wake of the pandemic and it was expected that the economy would bounce back bringing with it a resurgent demand for all products (including insurance) across the board. It must be noted that the outlook toward insurance products changed as a result of the pandemic. Life insurance products were no longer an afterthought, although profitability in this segment declined over the years. Property-and-Casualty (P&C) insurance, especially motor insurance, continued to be a strong driver, while health insurance proved to be the fastest-growing segment with robust demand from different geographies

Simultaneously, the insurance industry finds itself on the cusp of an industry-wide shift as technology is starting to play a greater role in core operations. In particular, technologies such as AI, AR, and VR are being deployed extensively to retain customers amidst this technological and economic upheaval.

Double down on digital

For insurance firms, IT budgets were almost exclusively dedicated to maintaining legacy systems, but with the rise of InsurTech, it is imperative that firms start dedicating more of their budgets towards developing advanced capabilities such as predictive analytics, AI-driven offerings, etc. Insurance has long been an industry that makes extensive use of complex statistical and mathematical models to guide pricing and product development strategies. By incorporating the latest technological advances with the rich data they have accumulated over the years, insurance firms are poised to emerge stronger and more competitive than ever.

Using AI to curate a bespoke customer experience

Insurance has always been a low-margin affair and success in the business is primarily a function of selling the right products to the right people and reducing churn as much as possible. This is particularly important as customer retention is normally conceived as an afterthought in most industries, as evidenced in the following chart.

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        Source: econconusltancy.com

AI-powered tools (even with narrow capabilities) can do wonders for the insurance industry at large. When architected in the right manner, they can be used to automate a bulk of the standardized and automated processes that insurance companies have. AI can be used to automate and accelerate claims, assess homeowner policies via drones, and facilitate richer customer experiences through sophisticated chatbots. Such advances have a domino effect of increasing CSAT scores, boosting retention rates, reducing CACs, and ultimately improving profitability by as much as 95%.

Crafting immersive products through AR/VR

Customer retention is largely a function of how good a product is, and how effective it is in solving the customers’ pain points. In the face of increasing commodification, insurance companies that go the extra mile to make the buying process more immersive and engaging can gain a definite edge over competitors.

Globally, companies are flocking to implement AR/VR into their customer engagement strategies as it allows them to better several aspects of the customer journey in one fell swoop. Relationship building, product visualization, and highly personalized products are some of the benefits that AR/VR confers to its wielders.  

By honoring the customer sentiments of today and applying a slick AR/VR-powered veneer over its existing product layer, insurance companies can cater to a younger audience (Gen Z) by educating them about insurance products and tailoring digital delivery experiences. This could pay off in the long run by building a large customer base that could be retained and served for a much longer period.

The way forward

The Insurance industry is undergoing a shift of tectonic proportions as an older generation makes way for a new and younger one that has little to no perceptions about the industry. By investing in next-generation technologies such as AR/VR, firms can build new products to capture this new market and catapult themselves to leadership positions simply by way of keeping up with the times.

We have already seen how AR is a potential game-changer for the insurance industry. It is only a matter of time before it becomes commonplace.


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