ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW 2015/Sessions...Session Description / Objectives...

IASA 87TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW

Avoiding the Trough: Harnessing Big Data to Drive BI Maturity

Session 572


Introductions

John Johansen • SVP, Global Lead BI, Data & Analytics

• Majesco Mike Ferber

• CIO and Director of HR • ICAT Managers

Tony Simpkins • Manager, Enterprise Data Warehousing

• Grange Insurance Jason Lichtenthal, CIO

• Pure Underwriters


Session Description / Objectives

Big Data is everywhere. Do you know what you can and should you do with it? How mature is your BI strategy? How well aligned is your company’s functional areas to the various BI performance stages? Do you know how to best leverage technology to get the most out of your Big Data? There are so many different aspects to consider, as well as internal factors like understanding your company culture and how they embrace new technologies that it can be overwhelming. In order to understand “what Big Data can do for you”, it is important gauge your BI analytics capabilities and the impact of Big Data within your organization. During this session, an overview of the BI Maturity Landscape and BI Performance Stages will be discussed to help you assess your company’s maturity, as well as your company’s ability to quickly respond to the ever-changing market demands for data


Session Description / Objectives

• Highlight the role analytics can play in your organization and the characteristics of the BI Maturity Landscape.

• Describe the different stages/levels of BI Performance and how to align the levels across different functional areas.

• Discuss why we are at a unique point in time for big data solutions.


Wait, what trough?

Source: Gartner


BI Maturity Models: TDWI


BI Maturity Models: Edgewater


BI Maturity Models: Gartner


Our Favorite Model: MIT / IBM


Killer Applications across Functional Areas


Organizations need to focus on Key Skills


Why now?

The board is talking about it Pragmatism and good expectation setting will break the fall

Offerings are maturing

Ecosystems are built out

It doesn’t cost a lot to put a toe in the water


What Functional Applications?

Ingestion Actuarial

Large less-structured data store



Mike Ferber, ICAT


Introduction to ICAT Executive Summary

ICAT is a leading underwriting manager of U.S. SME property cat risks (hurricane and earthquake) at Lloyd’s. ICAT presently delivers ~$220M of profitable, cost-efficient, highly segmented and well controlled SME

property business to Lloyd’s and other underwriters. ICAT’s Syndicate 4242 has one of the best records at Lloyd’s over the past six years.

ICAT can produce a greater volume of well-selected, properly priced premium than can be absorbed by s.4242 within its geographic concentration limits.

ICAT has the production capacity to expand its strategic partnerships and deliver a superior portfolio of risks to selected underwriters with compatible risk-taking appetites.

ICAT provides integrated, real-time analytics, underwriting and financial information to MGA clients with risk appetites that are complementary to those of s.4242

ICAT provides each MGA client with hands-on control of a custom-designed portfolio that fits its overall aggregate requirements and risk management controls

ICAT OVERVIEW ICAT Overview


Business Units

Segmented business units provide targeted underwriting approach based on account size.

PBU MMBU: M1 MMBU: M2

Insured values generally below $10m. Full Limits. Average TIV $1.7m;

Average premium $6k.

Insured values $10m-$100m. Target full limits, offer primaries also.

Average TIV $21m; Average total premium $43k.

Insured values generally above $100m. Target primary layers.

ICAT’s core business that has the greatest benefit of insulation from market rating cycles.

This segment is more exposed to rating cycles (due to the larger

insured values) and requires ICAT to selectively identify market

opportunities.

Direct to retail and wholesale brokers. US based wholesalers only.

Highly automated underwriting and business process. Underwriting

applications are completed on-line. An automated underwriting box

defines acceptable risk parameters. Eligible risks run through rating

engine to deliver real time quotes. TIV flags prompt referrals to

underwriters for exposure management and price control.

Not automated, all accounts are underwritten by ICAT personnel. Data is collected via mandated data entry forms. All risks are modeled pre-binding. Narrowly

defined and controlled “underwriting box”: all business conducted within intranet system for transparency and control.

Product

Underwriting Philosophy

Distribution

Platform

HBU

Homeowners product, full limits. Admitted or non-admitted depending

upon market.

This segment has very high renewal retention and is less impacted by

market conditions.

US based retailers only.

Highly automated underwriting and business process. Underwriting

applications are completed on-line. An automated underwriting box

defines acceptable risk parameters. Eligible risks run through rating

engine to deliver real time quotes. TIV flags prompt referrals to

underwriters for exposure management and price control.

Underwriting Fundamentals


Underwriting Fundamentals

ICAT is built around five core underwriting fundamentals Risk Selection and Pricing

Aggregate (Exposure) Management

Catastrophe Modeling

Risk Inspection

Claims Management


Risk Selection and Pricing

Collection of comprehensive information to permit robust underwriting. A complete and comprehensive set of underwriting data is required from agents and brokers to receive a quote from ICAT.

Lower limit exposures (generally ≤$10m) are underwritten via a fully automated platform and larger exposures are underwritten by ICAT’s personnel using market expertise and similarly detailed pricing metrics.

Catastrophe and Fire / AOP components are rated separately to maintain integrity of underwriting

Risk factors used to determine risk eligibility and pricing include but are not limited to:

• Occupancy

• Age

• Construction type

• Distance to water

• Roof shape and age

• Structural integrity and parking type

• Soil type and liquefaction

• Fire protection and security

• Loss history

Underwriting Criteria

Catastrophe exposures are priced to achieve targeted expected loss ratios based on modeled annual average loss.

Each MMBU policy is modeled individually prior to quoting including COPE information, coverage, and secondary characteristics for each building.

Catastrophe

Fire/AOP exposures are priced to achieve a target expected loss ratio based on ISO loss costs and other industry data sources, modified for other risk characteristics of the account (fire protection, security rating, loss experience and deductibles, etc.).

Fire/AOP


Aggregate Exposure Management

Aggregate management is ICAT’s principle risk management tool. ICAT manages aggregate catastrophe exposures at both the regional and local level on a real-time, daily basis.

• Blueprints segment the US into 121 microzones that isolate homogenous risk and market environment characteristics.

• Business written is measured against Nationwide and

Regional Blueprints to ensure sound spread of risk and to mitigate concentrations.

• Each microzone has a target and maximum insured

limit. As limits are approached, ICAT’s systems provide warning flags and access can be restricted on an almost real-time basis.

Spread of risk: “Blueprints”

• ICAT controls risk concentration at the neighborhood level through the use of its Local Concentration of Risk tool. This helps to mitigate loss “black spots” resulting from a catastrophe event, e.g. localized exceptional quake losses or tornados spawned from hurricanes.

• As each building insured is assigned a limit and all

buildings are geocoded, ICAT is able to monitor local risk concentrations and maintain aggregate caps for 0.25 mile and 1.0 mile radius circles throughout the country.

Spread of risk: local concentrations


Data and Reporting At ICAT

ICAT has a massive infrastructure of Policy Admin systems, claims, inspections, risk modeling, rating systems and other potential sources of data

ICAT’s data infrastructure has grown organically, but seeks to compress data from multiple sources into centralized data repositories

ICAT leverages: • Cognos • Informatica • iPartners • Sales Force • Custom SQL Reporting • Excel, Excel, Excel….


Big Data and Predictive Analytics At ICAT

The HYPE: Everyone has heard about Big Data and the promise that it will show you new correlations about your data and your business • Predictive Analytics: Have yet to point out a new correlation between Risk Quality and already

known Risk Characteristics (e.g., Distance to Coast, Roof Shape, Construction etc…) • Data Architecture: Big Data analytics projects try to be something different from your normal

requirements, architecture, build, deploy reporting projects. Our experience so far, is there isn’t a big difference between Big Data projects and legacy Data Architecture projects.

The WIN: ICAT has been able to utilize it’s own experience in claims and CAT activity to develop our own proprietary ‘overlay’ on the stock RMS models. • ICAT believes that our data can be mined effectively to show correlation between our risk

experience and expected damageability. ICAT has developed it’s own model called IMAM (ICAT Model Adjustment Methodology)

• IMAM has allowed ICAT to buy more economical re-insurance and to set Blueprints with Lloyds that allow us to write more aggregate.

The FUTURE: ICAT is hopeful that tools will become easier to deploy, leverage able by business users and provide richer correlations than those that currently exist.



Tony Simpkins, Grange Insurance


Tony Simpkins Bio

Started Enterprise Data Warehouse and BI Program in 2002 Oversees day-to-day activities of team to support data

needs of enterprise Key member of Buckeye DAMA chapter board Lives in Columbus, OH Bachelor’s degree in Business Administration Associate’s degree in Computer Science


Grange Overview

$1.3 billion annual premium volume, $2 billion assets Personal, Commercial, Life Distribute through independent agents only 13 States (Mainly Midwest and some southern states) Headquarters is located in Columbus, Ohio


Where my Data Journey Started

Started development of the Enterprise DW in 2002 – Premium and Loss Data Marts sourced from mainframe. Goals:

• Replace individual line of business extracts with single version of the truth

• Gain greater insights by having transaction level data. • Make data available to non-power users through enterprise reporting

tool

Team: • 4 person internal team • Occasional external help as funding was available • Team still supported other applications


The Road

Enterprise Premium & Loss released December 2002 Continue to add new data to warehouse as time permitted. Roadmap #1 – (2008)

• “Solid Design – Infrastructure needs improved” • “Not enough business involvement” • “Resource Limitations inhibit teams delivery” • “Governance & Self-service concerns”

Reorganization #1– (2011-2012) • Business Began taking more ownership • Separated Data Integration from Reporting • Centralized Reporting • Developed BI/DW Mission, Strategy and Guiding Principals


The Road Continues

Roadmap #2 – (2013) • Shore Up Data Foundation • Implement Federated Reporting

Team • Expand Self-Service BI • Develop Enterprise Data

Governance • Invest in people (Proposed 15 new

FTE across the enterprise)

Current State • Completing V2 of Premium & Loss • Creating Enterprise Data Mart for

Quote Analysis • Implemented Federated Reporting

Team with Help of Business • Hired Enterprise Data Governance

Manager & Governance Analyst • Hired additional ETL, DBA, QA, BI

and BA resources (7 total) • Developing New 5 year roadmap


Future Roadmap Strategies

Complete Committed projects (V2 Premium, Quote, Others) Ensure Adequate Support for Existing Data Assets Better Leverage Existing Data Assets Support of Transformation Projects Establish Big Data Platform

• Telematics • Customer Sentiment • Call Center Analytics)

Over 80% of Executives interviewed thought we needed a Big Data Platform



Jason Lichtenthal, PURE


About PURE Insurance

Presenter

Presentation Notes

The fastest growing personal-lines specialist insurer dedicated to serving HNW individuals and families Member-owned model emphasizes transparency and alignment of interests Serving over 42k members serviced by 600 independent agents Based out of White Plains, NY, with 8 offices countrywide Member-obsessed culture Forward-thinking management team - determine processes, services, and systems which will allow PURE to scale efficiently and effectively


My Prior Data Experience

Presenter

Presentation Notes

Experience at National Financial Partners (NFP)a Company founded in 1999 Started in 2001 as database architect, eventually VP of IS Needed core systems, let alone reporting/analytics Reached a level of maturity that allowed data to thrive as a asset by 2006 Implementation of Microsoft suite for data warehousing, IBM Cognos for reporting and analytics Mostly tactical – month end financial closing in 1/3 the time Single version of the truth


PURE in 2009 (~3 Years Old)

Presenter

Presentation Notes

What did PURE look like in 2009? No data scientists 0 actuaries – outsourced pricing/risk profiles/reinsurance analysis Innate (insane?) knowledge of barrier reefs and inlets from storm surge Not enough meaningful data points regarding our niche Running the business on prior experience and by the gut Microsoft Access-based reporting


Can IT can assist with the level of data maturity within an organization?

IT Maturity

Presenter

Presentation Notes

Companies with IT functions performing only as operator / stewards are at a significant disadvantage to competitors who have higher performing IT functions. Where does PURE sit? And what are we doing to be the Strategist/Catalyst?


Data Maturity

What does it take to be “mature”?


Using Data as an Asset and for Competitive Advantage

Data as an Asset Where we have invested our time Eagle Eye Analytics “Buckets” for segmentation and categorization Using data to help with subjective analysis Acceptable risk parameters PURE Toolset SQL Server/SSIS IBM Cognos Salesforce

Eagle Eye Analytics Access/Excel Custom SQL reporting

Presenter

Presentation Notes

What it took to convince others that using data as an asset was a competitive advantage Holistic, concerted effort of many initiatives to “move the needle” Where have we invested our time Competitive Differentiation Products and Services Automation Ease of Doing Business


Barriers to Exceptional Analytics

We're facing a talent gap Resources capable of understanding and testing data Business users that understand the structure of data

We spend more time gathering data than analyzing it Need appropriate tools in place to allow for self-service

We don't (yet) see the value and importance of data visualization We have really smart executives that don't (yet) see that

others aren't as quick at understanding numbers

Presenter

Presentation Notes

Barriers standing between us and the future of analytics Show current report going out 100 columns


Possibilities for the Future

Build new, better, and more profitable products

Support for better decision making

Provide exceptional service experience to members

Improve productivity/efficiency

Presenter

Presentation Notes

Where are we going next? What have we not tackled? Growing with your data Data mining for patterns Aggregate management Along with our 40% growth rate comes an even larger increase in quotes, binds, renewals, and endorsements. Looking at how we are trending for 2015 – we’re looking at about 285k quotes created, 75k new policies, and 385k transactions. Lets say, for argument’s sake, that one in every 10, 100, or 1000 of those 385k transactions is deeply problematic. How do you find them? How do you stop them? The answer is not to screen every quote and every transaction. We need to focus on those transactions that are worthy of our time to scrutinize. We now have a ton of information at our disposal to use to figure out what we want to take a closer look at, and what we don’t. We can create profile segments to figure out what has a high likelihood of a low loss ratio, and what doesn’t. We took a first step with our member grade – used to grade the profitability and the size of a member, and make sure we are improving the quality of the membership. We took another step with the pre-renewal underwriting rules, to help underwriters know what business needs to be reviewed so we can get off of “bad” business in a timely manner. We need to use advanced analytics to help the underwriting team evaluate prospective members The product team used Eagle Eye Analytics to find trends that helped determine rate changes – resulting in changes like Auto 3.0. Understanding the positive impact of having a collector car on a policy, the negative impact of leased vehicles, or the impact of insurance score on loss results. Decision support tools will help us get more insight into how we should evaluate prospective members.


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ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW 2015/Sessions...Session Description / Objectives...

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