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In recent times, start-ups and disruptive technologies/innovations have set a mandate for all the industries to have a hawk-eye on the market trends and its impact. The veteran and the notable Fortune 500 groups are given tough times by the mushrooming tech giants. Companies can no longer take a back seat and relax.
It is good to be wise and be proud of the experiences & achievements in the past, but most importantly be vigilant of the present and have a defined vision of the future to sustain! and have existence
The technology adoption and re-incarnation of business strategy/priorities to serve the customers will definitely play a crucial role in the Insurance Company’s journey in making it stand tall and strong in the crowd (as a differentiator).
Following are a few highlights of the “Disruptive Trends” the Insurance Industry is trying to adopt for improved Customer Experience[Cx] and reducing Operational Expenditure[OpEx]:
- Rise of InsurTech
- Similar to FinTech organizations, there has been a rise of InsurTech start-ups [leveraging latest technology and innovation] trying to replace or enhance the usage of insurance services of incumbent companies.
- InsurTech solutions bring in an incremental or radical / disruptive innovation development of applications, processes, products or business models in the insurance services industry
- It gives the firms a mix of – Trust built over the years by Insurance Companies & Differentiated experience by newGen Start-ups
- Enables development of Faster Go-To-Market (GTM) solutions than developing age-old legacy systems and maintaining it over time (costly affair)
- Alliance accelerates change and transforms the way to think, develop and satisfy the needs of the customers – will foster more collaborations in the futue
- Making Big Data Actionable
- “Data” is a powerful currency in the race for fintech supremacy.
- In addition to gathering data, making sense and actionable items out of the data heap by leveraging contextual data analytics, intelligence, big data will make a difference for the customers and the financial institution as a whole.
- With enormous data in store, data lake kind of concepts can be adopted and harnessed to reap better benefits
- Machine learning capabilities can be topped-up with the real-time analytics to form the icing on the cake and help in making human-like decisions
- Virtual Assistants – Chat Bots or Robo Advisors:
- It is crucial to understand the customer and suggest products for cross sell/up-sell – based on adaptive(real-time) & predictive(historical) analytics on the transaction/similar transactions for prospective customers
- It is like providing the right product, to the right customer at the right point in time
- g.: similar to Apple’s Siri; Google Now; Amazon’s Echo
- Robotics Process Automation[RPA] / Smart Process Automation[SPA]
- Leveraging RPA / SPA solutions to build Workforce Productivity Solutions [ around areas where there a monotonous, mundane and manual intensive tasks are involved]
- Typical scenarios are: data entry; head-down worker activities; reconciliation process; on-boarding new customers; sending notification/intimation
- This helps in reducing the operational expenditure of the organization and improve the workforce productivity
- Blockchain Technology
- Blockchain technology provides a shared, trusted and secure public ledger to record financial transactions / digital assets and ‘smart contracts’.
- These are recorded independently – distributed across a multitude of computers around the world.
- The records are condensed (into blocks) and interlinked (to form chains), using complex cryptographic algorithms.
- There is no third party required for secured transaction
- The verification of data for primary applicant or reinsurance can be validated by leveraging block chain technology model – this will reduce the complexity and tedious ledger process
- Automated smart contracts can help in quick validation of identity and limited bureaucracy
- Peer-to-Peer Insurance models or Smart Policies can be envisaged[ based on data feed for climatic conditions by Meteorological Dept. pay-outs can be done or initiated for crop/agricultural insurance]
- Key areas where it can be leveraged in Insurance sector:
- Smart Contracts
- Customer Identity Validation / Verification
- Regulatory and Compliance Benefits [Audit]
- Smart Underwriting
- Fraud Management
- Digital Data as a Service [Microservice based model – functional segregation of business]
- Leveraging the Microservices ways of working / implementation to expose some common functionalities and processess as a service
- Few services for example: getting insurance details, capturing policy details, policy payment etc.
- A standard API layer with a SOAP/REST protocol can be can be leveraged to access the functionality from multiple devices / external or other legacy systems
- With this approach – the concentration and focus can only be on the Business Functionality – than building the same functionality for multiple applications / customizing or maintaining it.
- This will help in avoiding development of monolithic application
- It will also give rise to new breed of providers offering data on a subscription model
- Mobility or omni channel experience has already been adopted by some of the major insurance companies
- More personalized and advanced features with a good customer experience will make a difference
- Mobile Health Apps to gather data from the customer and monitor the health – thereby reducing the spending on healthcare and importantly enable insurance companies to understand the customer’s risk profile
- Omni-Channel & Opti-Channel Delivery
- An ‘opti-channel’ experience delivers solutions using the best (optimum) channel based on the customer’s need and preferred channel
- This delivery model is beyond multichannel (delivery on multiple platforms), or Omni channel (delivery through all channels similarly)
- The proliferation of channels will begin to consolidate and improve in efficiencies
- Designing of “Digital Persona” is a key component
- This helps in targeting right customer, at the right time, with the right offer via the right/preferred channel
- Driverless Car [defining new policies]
- With the driverless cars hitting the road – definitely the insurance policy model needs to be revisited [as there will be no human driving it – to claim whose fault]
- Variation based on regional regulatory and compliance will add other dimensions
- For eg: things like black-box in the car similar to ones used in aircrafts can make a difference
- There will be incremental changes to the policies based on the adoption and faith the human race gains over machines
- IoT [eg: Wearables, Devices]
- The Internet of Things’ is becoming a buzzword and the technology trend with repercussions across the business spectrum
- It opens up a new world of opportunities by connecting the internet with billions of devices – wearable gadgets, fancy vehicles, fitness gadgets or industrial equipment
- Sensors, Beacons, Wearables and Devices etc generate humongous amount of data which can be leveraged, aggregated and analyzed by financial institutions to improve operational performance, customer experience, and product pricing
- The dump of data gathered from these devices on a daily basis can be instrumental in defining new services, improve efficiency or achieve other health and safety benefits
- We hear a lot about Smart Home, Fitness Trackers etc. IoT kind of models can be leveraged to optimize and provide customer specific service [house insurance, vehicle insurance or personal health insurance]
- And importantly, with data in hand the Insurance Providers can either help or challenge the customers [eg: in healthcare industry with fitness gadgets]
- With advantages that can be reaped from the data are also associated security, privacy & risk concerns areas
- Leveraging Advanced Machine Learning & Artificial Intelligence
- Machine-based learning is described as the science of getting computers to act without rules-based programming
- Cognitive computing capabilities and development of algorithms that team themselves [self-healing processes] are an integral part leveraging Machine Learning
- The data gathered via IoT [structured + unstructured] can be put to use by crafting ML[Machine Learning] algorithms
- Natural Language Processing – NLP can also be leveraged for providing a more customized / personalized experience
- The Data & Algorithms can help in defining the Digital Persona of the customer
- New Payment Models
- With the ease of regulations, we are back in the era of Money Virtualization
- Every section of the industry food, travel, ecommerce etc. have come up with the Mobile Wallet features – making life easy for the customer to move without carrying cash/coins/cards
- Customers demand powerful services to be delivered at the swipe of a smartphone screen, wherever and whenever they happen to be. As it is delivered in the more efficient way – it is getting easier for them to step over the inertia of the traditional banks
- As many players are entering this space – there is a race for dominance. Insurance Companies can’t walk solo with their offerings, collaborative partnership with mushrooming mobile wallets is the key
- This will help them to : enter competitive market space; save cost; ease of operation; customer demand
- Contact Centre Modernization
- Customer Experience
- Customers are dissatisfied with their customer service experiences
- Better Service
- Providers struggle with critical service and support capability gaps (e.g.: Mobility, outbound emails, personalization etc.)
- Futuristic Platform
- A new generation of “social” customers is setting the agenda for contact centers of the future.
- Competitive Market
- Organizations are taking the first steps toward building next-generation customer management platforms
- Customer Experience
- Usage based Insurance Models [PAYD, PHYD]
- Auto insurers are shifting towards usage-based insurance models that will help them to enhance claims handling capabilities and perform better customer segmentation
- The insurance premium will be calculated based on usage and/or behaviour
- Vehicle Telematics [in car installed devices for sending data at real time] will be leveraged to estimate the usage, driving pattern and driving behaviour
- Some of the models can be;
- PAYD – Pay as You Drive – Insurance premium is calculated based on the number of kilometres/miles coved by the vehicle
- PHYD – Pay how You Drive – Insurance premium is defined based on the driving pattern/style
- Games are a great way to engage the customers across all age groups
- Insurance companies are adopting gamification to simplify the complex and tedious processes – this in turn enhances the Customer Experience, training and adoption
- Gamification uses strategies like points, challenges, leaders board, incentives and different levels of engagement
- It is an integral part of the insurer marketing campaign
- Use of Aerial & Digital Imagery
- Insurers have started using aerial and digital imagery for accessing the property and other relevant details
- This helps in reducing the claims processing time
- Eg.: for a house insurance – aerial and digital imagery can help in getting the exact dimension details, location, proximity to identified risks [if any] – e.g.: seismic zone for earth quakes
- Very helpful for remote locations and primarily for Agriculture based Insurance [for farmers] – Unmanned Aerial Vehicles – UAV [Drones] can be leveraged
- There may be regulatory and compliance challenges which needs to be addressed in case of Drones [to avoid infringement related issues]
- Patient Adherence Apps or Patient Care Monitor
- Non adherence to routine medicine intake or timely check-up might lead to increased healthcare spending for the Customer and at times also hits back to the insurance companies
- With Apps for Care Monitor the patient’s adherence towards medication will improve
- These apps regularly follow-up with the patients for medication, treatment and check-up via games and reminders
- The objective of these apps is to: remind; follow-up; educate; simplify; counsel; reinforce
- The data gathered will be helpful to provide incentives to patients who follow medication regularly and on a timely basis.
- Establishment of “Insurance Innovation Labs”
- The marriage between the traditional insurance and new age start-ups give rise to insurtech “innovation labs” or “digital factory”
- Innovation Labs envision an evolutionary process that re-examine the original purpose of the business (institution) before altering the DNA to redefine the purpose to go beyond the original intent.
- This establishment creates an open space to research, develop and showcase creativity and connect with the customers in enhancing the experience
- This develops a curiosity factor and an experimental approach with the vision of embarking on the digital journey
- The labs can be used to define new opportunities and to test the hypothesis
- It also creates a platform to engage customers with technology / business related discussions, different from the routine transaction related activities
- Insurance Innovation Labs can help in defining:
- Strategic / business thinking – with a focus on business and brand
- Design thinking – product or service creation
- Agile – technological design
- Human / Customer centred design – interface, environment, product design
- Virtual & Augmented Reality[AR]
- The year 2016 was remarkable in terms of the excitement, craze and the impact Virtual & Augmented Reality can bring for any business model [How can we forget Pokemon Go]
- Augmented Reality is a technology that generates sound, video or graphics over real world environment and thereby enhancing the people’s experience.
- Customers are tired of the same marketing models and customer service [ via Contact center call or printed/scanned flyers and brochures]
- VR/AR will be a new space to excite the customers and help them understand the policies/products better
- Customers no longer have to browse through multiple links; flip through multiple pages of the attachments; presume something or spend time in calculating the premium and selecting the right product
- Augmented reality has the potential to bring a paradigm shift in the insurance sector and many of the advanced-thinkers had already started adopting the technology as a major marketing tool.
- Others Trends to watch-out and Adopt
- Leverage Telematics in vehicles to gather data and communicate at real-time
- Connected Health – Stitching the data from wearable and other fitness devices to analyse and recommend the best policy for an individual
- Providing Next-Gen Customer Experience
- Developing Customer Centric Applications (primarily self service enabled – eg: Kiosk)
- Welcome & Leverage Open Source Stacks (OSS)
- Crowd Sourcing Initiatives
- Improvise efficiency and reduce operational expenditure by adopting DevOps
- Original Image Source: Insurance Tech Periodic Table
- Original Image Source : Image
Interesting Read Articles & References:
- 5 Insurance Companies that found a novel way for marketing [Virtual & Augmented Reality]
- Block Chain in insurance – Opportunity or Threat [McKinsey]
- Some use-cases of Blockchain in Insurance
- Blockchain in Insurance Sector
- McKinsey Report Weighs Blockchain Impact on Insurance Industry
- Trends in Insurance Tech
- Top trends in Insurance – 2016
- Insight Insurance Tech Vision
- Insurance Industry Trends
- BPM software spend to hit 2.7bn
- ‘You won’t need insurance for self-driving cars’
- Wearable Technology data to aid premium calculation
- Dashboard footage now accepted insurance disputes in UK
- InsurTek Insights
- Insurers use drones for crop and agricultural insurance
- 5 ways IoT will transform the Insurance Industry
- Insur Tech – Startups
- 16 Insur Tech Start-ups to watch-out
- Disruption in the insurance industry: Displacement or innovation?
- How Technology is quickly disrupting the Insurance Industry
- Big Bang Digital Disruption: Is the insurance industry in Asia next?
Keep Reading, Keep Researching & Keep Blogging!!
Please do share your thoughts and views to enrich it!
Happy Reading! 🙂
Disclaimer: Views my OWN not my Employer’s
[The intent of using the images was to pictorially share the thoughts and share the learning. Happy to share Credits for the photos and images]
Related posts: [old blogs]
- The Banking & the Financial services industry is the widely and wildly conquered sector where digitization and automation has been enabled to the max by IT.
- But in recent times, with the advancement of technology and innovations, it is important for the Banking industry to explore, pace up, transform, innovate, re-think and live in the present.
- The technology adoption and re-incarnation of business strategy/priorities to serve the customers will definitely play a crucial role in the Bank’s journey in making it stand tall and strong in the crowd (as a differentiator).
Keep Reading, Keep Researching & Keep Blogging!!
Happy Learning!! 🙂
The Banking & the Financial services industry is the widely and wildly conquered sector where digitization and automation has been enabled to the max by IT. But in recent times, with the advancement of technology and innovations, it is important for the Banking industry to explore, pace up, transform, innovate, re-think and live in the present. The technology adoption and re-incarnation of business strategy/priorities to serve the customers will definitely play a crucial role in the Bank’s journey in making it stand tall n strong in the crowd (as a differentiator).
A nice read article on “Top Retail Banking Trends & Predictions“.
Key Highlights :
- The ‘Platformification’ of Banking
- Removing Friction from the Customer Journey
- Making Big Data Actionable
- Introduction of ‘Optichannel’ Delivery
- Expansion of Digital Payments
- Executing on Innovation
- Exploring Advanced Technologies
- Emergence of a New Breeds of Banks
- Mining New Talent
- Responding to Regulatory and Rate Changes
few additions …
- Next-Gen Customer Experience by including
- wearable device adoption
- analysis driven Next Best Actions
- ..and many more
- Developing Customer Centric Applications (primarily self service enabled)
- Welcome & Leverage Open Source Stacks (OSS)
- Crowd-Sourcing Initiatives (loan, mortgage, risk management etc)
- Blockchain technology making the financial system more decentralized
- Emergence of Banking Market Apps
- Robot Advisers that stop you from making unsound financial choices, in real time – Customer Assist
- Establishment of Banking Innovation Labs
- Beacon Technology Adoption
- Heat-Map Technology – Identifying right customer for the right product at the right time
- Automated Appointment/Scheduling/Token Management – helps avoiding unexpected crowd and long queues
- Battling with mushrooming Mobile Wallets
- DevOps driven CI/CD in IT – for streamlining Release Management
- Streamlining Operations in IT with Robotic Automation / Machine Learning techniques
Interesting Read Articles:
- 10 Branch Banking Innovation Strategies for 2016
- The Uberization of Banking
- What Makes a Great Mobile Banking App
- Digital Disruption Forces Financial Institutions to Rethink Priorities
- Selfies Transforming Mobile Banking
- With Digital Banking, Consumers Must Come First
- Internet of Things: Opportunity for Financial Services?
- Banks Need to Make Mobile Apps An Experience, Not An Add-On
- Article : The derivative effect: How financial services can make IoT technology pay off (link : pdf)
- 7 Habits Of A Highly Successful Digital Bank
- Differentiation in Banking Requires Better Data Insights
- New Customer Onboarding Goes Beyond Slick Marketing (link: pdf)
- Mobile Banking Satisfaction Drops As Digital Expectations Rise
- Banks Expand Innovation Investments to Battle Fintech
- How Banking Can Survive Digital Disruption
- Three Barriers to Banking Innovation
- Digital Thinking for Bank 1.0
- Becoming a Smarter Bank
What are your top trends & predictions in the retail banking space?
Happy Learning!! 🙂
The power to Think, Decide and Act based on situation, emotion and person is something that makes Human a very unique species in the ecosystem.
Machine Learning is like building the Human type of behavior in a Non-liven Object/Machine/System based on some highly rich and complex algorithms and techniques.
Machine Learning helps me what I should take an fits my taste before I myself realize and ask for the same.
Before deep-diving into the details and granularity of the Machine Learning features. Let’s get a feel and heads up – where in our day-to-day real life Machine Learning is important and makes sense :
- Banking / Retail / Telecommunication
- Prospective Customers and Partners
- Satisfactory index of the Customer (based on relationship, transaction, marketing campaign etc.)
- Fraud, Waste and Abuse of Claims
- Forecasted Credit Risk and credibility of the customer
- Effectiveness of a Marketing Campaign
- Eg: How many accepted the offer and how many rejected it. Any decisive factors leading to acceptance.
- Cross Selling and Recommendations
- Eg: Ecommerce sites : People who purchased this product also purchased this
- Contact Center (helps the CSR to engage the customer during the call with the relevant data)
- Eg: We have seen that you have ordered the cheque books to a separate address (different from the registered address) – would you like to change your Address Details
- Healthcare & Life sciences
- Scanning & Screening – Biometrics
- Drug Discovery based on the component mix
- Diagnosis and remediation based on the Symptoms, Patient Record and Lab Reports
- AECP – Adverse Event Case Processing Scenarios based on drug, patient, geo, climatic conditions, past history, food intake etc.
- Handwriting to Text or Speech (Identification & Learning Graphology Techniques)
- Debugging, Troubleshooting and Solution Wizard
- Email filtering based on Spam
- Text & Mail categorization/recommendation
- Support Issues and enriching KeDBs (Knowledge Error Databases)
- Friends and Colleagues Recommendation – via Facebook, LinkedIn, Twitter etc.
- Self-Driving Cars – by building artificial intelligence and algorithms
- Image Processing
- Handwriting / Signature / Fingerprint / Iris / Retina Verification
- Face Recognition
- DNA Pattern Matching
(Feel free to suggest and help me enrich this list based your experience and the real time scenarios you have witnessed)
- Building something with a machine/non-living object which is a replica of human brain and that caters to meet the brains of millions and billions of users – is NOT an EASY job.
- A Rich Volume, Quality Data and Flawless Algorithm is very critical for building/training a Machine Learning Model to think/decide/act like humans do.
- With millions/billions of non-stop data processing – human mind can get fatigue/tired. At times the Human factor also creates a “Dependency“. This is where Machine Learning Algorithms play a crucial role. Build once and automate it.
- In simple words : “Big Data + Machine Learning = Deadly Duo“
Stay Tuned and Watchout for this space for Detailed Blogs/Notes/Links on Deep Diving Machine Learning. Till then Thanks for Stopping By.
Happy Learning!! 🙂
With referenece to my earlier post on “BigData & Hadoop newbies – Bookmarks!!“. Here are a few additional links for a complete Training plan for Hadoop. This looks impressive. Have started going through them and will keep this space updated with any useful links and comments.
Please do share if you come across any helpful site for learning haddop – for newbies
- Tutorial 1: Hello World – An Overview of Hadoop with Hive and Pig
- Tutorial 2: How To Process Data with Apache Pig
- Tutorial 3: How to Process Data with Apache Hive
- Tutorial 4: How to Use HCatalog, Pig & Hive Commands
- Tutorial 5: How to Use Basic Pig Commands
- Tutorial 6: How to Load Data for Hadoop into the Hortonworks Sandbox
- Tutorial 7: How to Install and Configure the Hortonworks ODBC driver on Windows 7
- Tutorial 8: How to Use Excel 2013 to Access Hadoop Data
- Tutorial 9: How to Use Excel 2013 to Analyze Hadoop Data
- Tutorial 10: How to Visualize Website Clickstream Data
- Tutorial 11: How to Install and Configure the Hortonworks ODBC driver on Mac OS X
- Tutorial 12: How to Refine and Visualize Server Log Data
- Tutorial 13: How To Refine and Visualize Sentiment Data
- Tutorial 14: How To Analyze Machine and Sensor Data
Reference links :
Happy Learning!! 🙂