5 Decisions impacting ‘Data on Cloud’ strategy for insurers

Insurance companies are doubling down on their data, analytics and A.I investments to be ready to compete in a ‘Post-Covid’ digital economy. In the last couple of years, multiple cloud-based technology platforms have emerged and Insurers are actively exploring ways to take advantage of this technology ecosystem. It is not an easy decision as technology is rapidly evolving and there are many strategic questions to ponder - · How do you ensure that you have an ability to pivot in future in response to evolving technology and business needs ? · Should you redefine your data model or continue with the same ? · What should be your data integration strategy? · What kind of A.I/ML capabilities you need to provide ? · How do you provide a self-service data visualization augmented by A.I ? I have explored these aspects in my ‘Point of View’ article - 5 Decisions impacting ‘Data on Cloud’ strategy for insurers - https://www.lntinfotech.com/wp-content/uploads/2020/10/POV-5-Decisions-Impacting-Data-on-Cloud-Strategy-of-Insurance-Enterprise.pdf?pdf=download

AI driven Knowledge Management

I have been working with a large insurance company to define its knowledge management strategy and solution architecture. As I dug deeper into business scenarios, I realized that ‘Knowledge Management’ is the key area where A.I technologies will have immediate and significant impact on insurer’s bottom line. .... click here for more -> https://www.linkedin.com/pulse/ai-driven-knowledge-management-amit-unde/

Applying AI in Insurance

Please check this out - https://www.lntinfotech.com/blogs/applying-ai-in-insurance/

Connected Insurance – How will it be in 10 Years from now?

Read it at - https://www.lntinfotech.com/Blogs/Lists/Posts/Post.aspx?ID=98

Coming down from the Ivory Tower of Enterprise Architecture

Read it at - https://www.linkedin.com/pulse/coming-down-from-ivory-tower-enterprise-architecture-amit-unde?trk=prof-post

End of Insurance as we know it

Click here to read the blog published on LinkedIn

Insurance: Regulations likely to bring back more focus on ‘Risk Management’ practices and global visibility

At recent G20 Summits, the G20 Leaders endorsed the implementation of an integrated set of policy measures to address the risks to the global financial system from systemically important financial institutions (G-SIFIs). Accordingly FSB (Financial Stability Board), in conjunction with IAIS (International Association of Insurance Supervisors) identified an initial list of Global Systemically Important Insurers (G-SIIs) consisting of 9 groups:

1) Allianz, 2) AIG, 3) Generali, 4) Aviva, 5) Axa, 6) MetLife, 7) Ping An, 8) Prudential Financial and 9) Prudential plc.

Basically, FSB is trying to solve 'Too big to fail' problem by hand picking large global institutes and subjecting them to a set of policy measures.


Although the initial focus is on these designated global SIFIs, it may eventually come down to all large, internationally active carriers. Many domestic regulators are likely to designate domestically important insurers and apply policy measures on the similar lines. In June 2013, U.S Department of Treasury FSOC designated AIG and Prudential Financial as SIFIs, and both will be subject to stricter regulatory standards and supervisory oversight under the 2010 Dodd-Frank Act.

The set of policy measures comprise:
• recovery and resolution planning requirements;
• enhanced group-wide supervision; and
• higher loss absorbency requirements ( including non-traditional non-insurance (NTNI) subsidiaries)

The impact of these regulations is still being worked out, however, at the minimum, it requires following from IT perspective -
• Integration of data sources for recovery/resolution and risk management across the group
• Identification of changes to Risk management metrics and bringing in visibility at group and legal entity level
• Identification of intra-group exposures
• Approach for ring fencing NTNIs and development of risk management plan & systems

In July 2014, Systematically important Reinsurers will be identified and they are likely to have similar impact. I will update this entry as we do more research on this.

New article : Big data Trends 2014

The recent article in Alsbridge Outsourcing Center - Big Data Trends 2014, includes some of my thoughts on how Insurance companies can effectively leverage the super abundance of data - http://www.outsourcing-center.com/2013-09-big-data-trends-2014-new-uses-new-challenges-new-solutions-58181.html
Here are some excerpts -

In this era of low interest rates, insurance companies need strong real-time analytics capabilities to achieve the elusive underwriting profit and sustained growth,” explained Amit Unde, chief architect and director of insurance solutions for L&T Infotech. “Going forward, the competitive battles will be played on the data turf. It’s the companies that leverage both external and internal Big Data, predictive analytics and adoptive underwriting models that will come out on top.

With Google Maps and location intelligence services, the underwriter can view a property from all angles and assess distance from a coastline, flood plain or other potential hazards. Online access to hundreds of different data sources—from videos to photos to loss trends and other documents— is now just a few clicks away,” Unde said. “But, without the right tools, mining this data is still a highly manual process.

I wouldn’t be surprised if, in the next five years, the next big player in the commercial insurance industry was a new company with a Big Data-driven automated policy issuance and claims payout model,” Unde said. “Automated decision-making has the potential to transform the industry, enabling small players to compete with large insurers, based on their technology.

In the insurance industry, companies should validate against a set of rules or cross-verify against multiple sources,” Unde said. “However, in most cases, it doesn’t make sense for insurers to boil the ocean to get 100 percent data accuracy. It makes better sense to apply the 80/20 rule to achieve the desired accuracy for the 80 percent of the dataset without having to invest intensive efforts—then asking ‘did you mean’ questions in the remaining 20 percent of cases.

Let me know what you think about the article.

Webinar : Aggregate, Visualize and Manage: The Fundamentals for Gaining A Single View of Risk

The ability to aggregate, visualize, understand and manage risk is fundamental to the profitability of insurance carriers and reinsurance companies. In some cases, it’s fundamental to legal compliance, overall solvency and long-term viability.
Yet it has been difficult to date for most insurers to gain a single, operational view of risk across their organizations. Until now…
Carriers are beginning to leverage technology solutions for a comprehensive risk management solution – one that helps carriers overcome siloed operational structures, IT systems and data sources to integrate information across the enterprise, ensure its quality, enrich it with third-party data – and then present a single, map-based view of operational risk in near-real-time.
View this one-hour webinar ( conducted on Wednesday, July 31, at 2:00 PM)as Rich Ward, Business Solution Architect with Pitney Bowes Software and Amit Unde, Chief Architect, Insurance Solutions with L&T Infotech, discuss new trends in location intelligence technologies and how near-real-time geospatial analytics are drastically changing catastrophe modeling, underwriting and risk management practices.

6 Strategies for NextGen Insurance Underwriting

6 Strategies for NextGen Insurance Underwriting

Big Data: Are you still a fence sitter?

Get more value from Big Data technologies – Use it for Small Data Analytics

[ Note : I have published this blog originally at L&T Infotech blogsite - www.lntinfotechblogs.com/Lists/Posts/Post.aspx?ID=38 ].

Big data is often defined by three Vs. – Volume, Variety and Velocity. While this definition captures the essence of Big data, it is limiting when used to define technologies that support Big data. These technologies can do much more than just handling ‘Big data.’ In fact, most enterprises can derive more value by using these for ‘small data’ analytics.
Besides handling large variety of data, these technologies provide new analytical capabilities, including natural language processing, pattern recognition, machine learning and much more. You can use these capabilities effectively for small (or ‘not so large’ data) in non-traditional ways and get more value out of this data.
Here is how –
1. Create ‘Data labs’ rather than just a data warehouse
Big data technologies provide advanced analytical environment. The focus is on analyzing the data, rather than structuring and storing the data. Such environment gives a perfect sandbox for experts to ‘experiment’ with data and derive intelligence out of it. For example, Insurance actuaries can derive specific patterns out of claims history data by linking external factors with loss events and define rules for pricing and loss predictions.
2. Don’t just predict, but adapt continuously to changing realities
Big data technologies provide machine learning capabilities that allow calibrating predictive models continuously by comparing actual outcomes with predictions.
3. Change ‘Forecasting’ to ‘Now-casting’
Big data technologies can help in analyzing large stream of data at real-time, without hampering performance. This capability can be used effectively to provide ‘real-time’ analytics. For example, Insurers can define new products that charge premiums based on real-time risk data emitted by sensors or telematics instruments, rather than traditional approach of calculating premiums based on forecasting of risks.
4. Don’t get constrained by a Data model
Have you ever undergone the pain of living with a data model that no longer supports business requirements? Well, don’t worry anymore. Most Big data technologies support ‘Open format’ and dynamic changes to data records to suit analytical needs.
5. Forget Massive data movements
In big data platforms, the data is co-located with analytical processing involving minimal data movements. Forget about those large, multi-year ETL programs.
6. Save cost with low-cost commodity hardware
Large data warehousing and MDM programs often require expensive enterprise hardware and licensing to support desired level of performance. This expense can be as large as 50% of your total cost of ownership (TCO). The big data platforms are designed to work with low-cost commodity hardware (including bursting on cloud), and most are open-sourced. This can help you slash the hardware/licensing costs significantly.
So the moral of the story is – Big data technologies provide many capabilities that make them an attractive choice for ‘small data’ analytics as well. Be innovative in leveraging these capabilities to complement your current analytics world.

Big Data - A solution in search of a problem !

What does Big Data solve in Insurance, that cannot be really solved by traditional technologies? This one seemingly simple question generates a good deal of brainstorming. Let’s keep Health insurance aside (that’s easy) and think about P&C and Life insurance space.

Where is the big data ?

Number Uno is Social data, ever growing and less contextual, but BIG it is.
Then we have policy data over years. We, of course, have a loss history of several years.
We have external risk data sources.
Few companies may also have real-time data streams from Cars (PAYD or commercial fleets), factories etc..

What can we do with Big Data technologies?

Sure we have many problems.
First, we need to know customers better.
How many times we tell them that you can save $400 by switching and then when they ask for a quote, we provide a quote more than their current outgo.
Do we congratulate them when they have a new baby arrival at home?
Do we know that they are looking to buy a car?
With big data, we will be able to co-relate all the seemingly unrelated data sources and link them to derive the actionable intelligence.

Another application is Fraud detection. Some people are out of bars, just because we cannot practically spend time and energy to figure out their fraud. Big Data technology makes is possible and simpler.

What about Risk Analysis? Sure! More the data you have, more you know about your customers, you are likely to predict the risk better.

The real advantage
To some extent, we are doing all this with current traditional technologies as well. More the data, Merrier it is, so big data technologies will definitely help, but is that all?
In my opinion, the real advantage of Big Data is to find the problem (or opportunity) that you do not even know about. When a data scientist dives deep into data and finds patterns and co-relates, there will be an Eureka moment, that will provide you the real ‘intelligence’ hidden in this data. Indeed, Big Data is a solution in search of a problem!

An ounce of knowledge is worth a ton of data

As my colleagues return from Insurance CIO summit and other conferences, I am getting bombarded with questions and suggestions of leveraging the BIG social data.. There are many ideas floating - targeted marketing, risk evaluation etc..
Well, I adopted words of Dr. Fayyad (Yahoo’s ex chief data officer) to reply back - An ounce of knowledge is worth a ton of data!

Notwithstanding the legal and moral issues, the availability of this data does not mean 'availability of knowledge'.

The models put on this data are sometimes completely inadequate to generate any useful insight for insurers. Even if there are any, it is hard to tie back these insights to specific customers or prospects, thereby, making those completely non-actionable.

I think, the insurers will gain more, if they focus on generating 'insights' from the already available data, before looking at acquiring more data. There is a plenty of structured and un-structured data available within the premises of the organization, across several touch points.
How much that is being utilized? Do we have models to analyze the data and generate insights and predictions? Is this intelligence already integrated with the business processes - from customer acquisition, to underwriting and claims processing?
I think, we need to WALK before we RUN.

Translating Business Strategy to Enterprise Architecture

I recently concluded a consulting assignment to define Future (after M&A) enterprise Architecture for a health insurance company, who acquired another company with considerable overlap in business.

While it was ‘relatively’ easier to come up future IT architecture by analyzing future needs and system overlap, it was quite challenging to present to executive board (completely non-technical with attention span of max 5 mins) and explain how exactly it maps to their business strategy. We had generated loads of detailed EA artifacts, however, challenge was to put all this together in just couple of slides and create a strong business case to move forward.

I found TOGAF’s Content Metamodel very useful in creating this linkage. I identified strategy business themes and for each business theme, and developed a view similar to content metamodel to link business strategy to required business services first, and then further to changes required in business process & IT systems.

A quick glance at artifacts is as shown -

Keys for success in using a Global Delivery Model

My presentation at South New England PMI conference.
I shared thoughts and my experience of creating customized process framework by adopting best practices from traditional as well as agile methodologies that are appropriate for your project and your organization.

Keys for success in using a Global Delivery Model - snec pmi april 29 2011

View more presentations from Amit Unde.

What if two Turkeys make an eagle?

If you follow ‘Mobile world’ news like I do, you might have already heard about partnership between Nokia and Microsoft, and Google’s trash-talk in response – “Two Turkeys do not make an Eagle”.
Cut to one year back – when android itself was a Turkey, however, they pretty much turned themselves into an Eagle. If you trust in Gartner’s figures, Android market share has risen from 3.5% in 2009 to 17% in 2010 and it is on its way to 22% this year. See - http://www.gartner.com/it/page.jsp?id=1434613 .
Nokia’s Symbian has highest share so far and having seen a world (e.g. India) completely dominated by Nokia phones, I believe they will put up a pretty serious fight for Apple and Google.
What does this mean to us in Insurance industry, who are developing mobile business apps? I think, these developments make a clear case for ‘cross-platform’ development. Rather than, making an application specifically for iPhone or Android, it’s time to seriously consider cross platform development platforms. A hybrid approach with combination of common ‘Portable’ code and some sexier ‘Native’ features appears as a good balance that provides functionality with oomph.

Naturally Yours,

The stars are getting aligned for Natural User Interface (NUI) technologies, with widespread adoptions of touch phones and pads, and recent advancements in consumer technologies such as Xbox Kinect. Consumers have progressed from ‘liking’ to ‘expecting’ natural multi-touch interface. Many technologies are already creative waves.  In L&T Infotech, our Tech office experimented with Microsoft Surface and I was amazed to see the possibilities. Touch IT

I suspect that we will see explosion of application of these technologies everywhere in coming years…!  Consumer electronics industry such as Gaming, Phones, and computers will naturally be far ahead, however, I wonder where we will use this in Insurance industry.

I bet it will be for marketing splash and customer acquisition. I can see consumers leaning on the interactive tables and playing with Geco to understand different parts of policies OR interacting with cute Progressive lady to name their price. Who says buying insurance cannot be fun?

- Amit Unde

Is it End of Zachman ?

It's very sad the way things turned out for John Zachman's associations. Just came across an excellent post by Gartner's Philip Allega on this topic - http://blogs.gartner.com/philip-allega/2010/09/01/john-zachman-is-dead-long-live-john-zachman/

One would have expected further research from such elites, rather than just attempts of commercialization of the framework. Really, not much has been added to the framework, since, it is published in 1980s... Though there is lot of value in the thoughts that are behind the framework, IMHO, the use of framework itself is quite hyped.
In comparison, other established EA frameworks ( and methodologies) like TOGAF and even the new upcoming like EACOE are quite useful. May be they will continue Zachman's legacy.. but without his name !
Looks like he wants it that way.

Agile adaptation of Architecture Evaluation Methodologies

I discussed some of the architecture evaluation methods in my previous post.  In short, the idea is - Rather than just using a checklist for technical evaluation, we should test drive each functional scenario against architectural decisions and judge the impact on quality attributes (utility tree) and evaluate the architecture. Such a review will be more specific to the project and hence, more beneficial.
Many accept the advantages of these methods, however, often criticize these methods for their overhead. I think, these methodologies can certainly be adapted for agile use. We do not have to stick to the the elaborate process that SEI has recommended, but instead, we should adapt it for our use by sticking only to its principles. In fact, in my experience, these methodologies are more beneficial if we use them in conjunction with agile development methodologies.

Why Architecture evaluation is important for Agile methodologies?
In Agile world, the application functionality is to be built in agile way, with constant visibility to business, and frequent changes to the features. However, the same is not the case for its architecture. If the architecture is allowed to evolve with changing requirements, it causes frequent rework, constant re-factoring and in fact, it counters the ‘Agile’ response. It makes sense to spend upfront time on architecture evaluation and ensure that it is flexible to accommodate the future changes.

What are the steps in evaluation?

I assume that you have background of standard evaluation methods such as ATAM / CBAM. If not, refer my last post. Though SEI has defined a very elaborate process, I think, only the following are critical steps –

Step 1 – Prioritize functional scenarios along with all stakeholders and identify architectural approaches and alternatives

Step 2 – Generate Quality Attribute Utility Tree and specify Stimuli – Response for each scenario

Step 3 – Analyze architecture approaches and identify all possible

a)      Risks,

b)      Non-Risks,

c)       Sensitivity Points ( interdependencies) and

d)      Trade-Off Points.

Step 4 – Quantify the Benefits of different architectural strategies and corresponding Cost and Schedule implications

Step 5 – Calculate desirability (benefit divided by cost) and Rank the alternatives.

Step 6 – Make decisions and document

What about the overhead of evaluation? How can it be done in Agile Way?

To make these methods more agile, use following techniques -

1. Evaluate on Sampling basis –Short-list only the unique and critical scenarios that are likely to change.
2. Create the artifacts such as Utility Tree as part of Architecture development process and not just for evaluation. The act of creating the utility tree will improve the thought process while defining the architecture.
3. Evaluate everyday as soon as you discuss the scenarios in the war room. All the stakeholders are present in the war room. This avoids extensive planning, presentations, and elaborate evaluation exercises.
4. During the evaluation, apply the utility tree to each scenario and evaluate the Sensitivity Points, Risks /Non-risks points, and Trade-off points. In case of alternative solutions, estimate Cost and perform Cost-Benefit analysis.
5. Evaluate not only for the defined scenarios, but also for the possible changes. Get the ‘change’ scenarios from the business to evaluate the impact of changes. The architecture should be flexible to accommodate such changes.

- Amit Unde