Presentation is loading. Please wait.

Presentation is loading. Please wait.

Copyright © 2009 Accenture. All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Insurance Analytics High.

Similar presentations

Presentation on theme: "Copyright © 2009 Accenture. All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Insurance Analytics High."— Presentation transcript:

1 Copyright © 2009 Accenture. All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Insurance Analytics High Performance Underwriting Solutions June 2009

2 1 Copyright © 2009 Accenture All Rights Reserved. Compelling Opportunity: Fiduciary Necessity Conventional business intelligence has grown out of a history of compliance reporting requirements for regulatory bureaus and shareholders, and most MIS reflects this The legacy system environment and history of mergers & acquisitions inhibits data quality and timely access Claim coding is not aligned with premium/policy coding conventions Inconsistent reporting standards globally forces complex reconciliation Exposure measurement tools (e.g., PML models) are confined to single line of business and single peril thereby constraining insurers’ ability to develop integrated, enterprise views of accumulation Operational technologies (e.g., task management, sales/submission management, call center) are not yet broadly adopted thereby limiting insights into service metrics, sales & submission outcomes, SLA compliance and individual service performance Third party data (e.g., peer benchmarking, industry loss experience, customer demographics) is not readily integrated into the product development and actuarial processes to ensure profitable alignment with growth expectations While information is the primary currency of the business of insurance, many insurers lack adequate insight to operate as high performers.

3 2 Copyright © 2009 Accenture All Rights Reserved. Strategic Importance of Business Intelligence for Insurance Loss Cost Improvement Expense Savings Market share Growth Improved Customer Service Ability to more rapidly address pricing leakage issues or market leverage opportunities Ability to optimize capacity allocation in catastrophe exposure territories Improved insight into rating and pricing levers to optimize product filing strategies and underwriting practices Better alignment between premium coding and claims coding to achieve more granular and precise profitability insight Robust trending capabilities for improved loss development management More efficient and consistent data preparation facilitates data availability & reporting Dashboard, Reporting and What If Modeling tools empower business users Reduced exposure to regulatory fines due to incomplete or inaccurate data Improved product and geographic analytics enable more focused growth strategies Real-time insight into leading indicators of book movement, market penetration and cross-sell/up sell to proactively manage growth drivers Timely operational metrics to address service turnaround, SLAs and customer retention More granular distribution segmentation to optimize profitable customer segment growth Timely data access and robust analytical capabilities provide measurable, immediate value to the organization.

4 3 Copyright © 2009 Accenture All Rights Reserved. Why Now? Key Industry Trend Softening P&C Market Advanced Pricing Methods and Tools Focus on Improved Productivity Implication for Insurance Analytics Increasing pressure to preserve rating and pricing adequacy Need to leverage micro-segmentation to find profitable market segments Growth will be fueled by differentiated products and services which are dependent on unique insights Pricing methods such as “Predictive Modeling” are being widely used in Personal and Small Commercial Lines and are poised to be advanced through more sophisticated analytics Most large insurers are migrating to proprietary pricing models over purchasing third party model services External data is being increasingly leveraged to augment proprietary/internal data Increasing need for consistency and efficiency in the core operations of Underwriting and Claims, as highly skilled workforces age and retire, and exacerbated by M&A activity Growth imperatives are underpinned by cost optimization initiatives, thereby demanding greater capacity within current resources Increased M&A Activity Insurers must efficiently use vast amounts of disparate information from their acquired companies Core systems replacement driven by post-merger rationalization is bringing new front-end systems that capture significantly more data about risk characteristics and claimant details Changing Regulatory Environment AM Best rating now includes a category for exposure accumulation practices Spitzer allegations have resulted in a push for increased transparency in broker / underwriter interaction around the globe Sarbox regulations have implications for decision accountability and risk management practices

5 4 Copyright © 2009 Accenture All Rights Reserved. Competitive Advantage Most insurers have focused their analytics investments on reporting capabilities and point-based predictive modeling. Analytics What’s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Sophistication of Intelligence Access and Reporting Optimization Business Intelligence Maturity Curve Alerts Query/drill down Ad hoc reports Standard reports Predictive Modeling Forecasting/extrapolation Statistical analysis

6 5 Copyright © 2009 Accenture All Rights Reserved. Find a distinctive capability within your organization and develop analytics to support it. Manage analytics at an enterprise level, ensuring that data and analysis developed in one part of the company is shared throughout the organization. Commit to and invest in a change towards analytics at the highest executive levels of the company. Have large-scale ambitions of what analytics can do. Jeanne Harris, Co-Author of “Competing on Analytics” 2007. Competing on Analytics

7 6 Copyright © 2009 Accenture All Rights Reserved. Accenture believes the most powerful business intelligence is generated through the integration of insights across the insurance value chain Insurance Value Chain Data Analytics Benefits Product Development Marketing & Distribution Pricing & Underwriting Policy Processing Claims Performance Management Enhanced Enterprise and Core Capability Decision Making Gain insights for creating more tailored products Improve ability to deploy new products effectively (filing strategies, training, marketing) Enhance primary and secondary research focus Leverage product performance metrics to improve business rules and operational efficiency Identify potential sales more precisely by analyzing customer purchasing patterns Enhance agency recruiting capabilities Optimize distribution compensation models Improve prediction of product preferences and customer retention Speed risk selection and quoting with better alignment between risk quality and pricing (reduced leakage) Provide timely insight into price tiering models (eg., predictive models) to adjust models for more targeted marketing Identify non-core underwriting operational activities that are candidates for automation, elimination or delegation Improve utilization of underwriting services (premium audit, loss control) Provide more accurate, timely and detailed understanding of exposure accumulation Increase ability to enhance servicing for the most valuable customers and distributors Lower costs of processing with enhanced insight into performance of operational staffing models Gain insight into process bottlenecks and detailed transactions to identify improvements in policy automation Improve timeliness and accuracy of fraud detection Enhance resource allocation and prioritization with better insight and predictability into claim complexity and settlement potential Improve subrogation predictions Enhance reserving practices Gain operational insight to increase levels of automation for simpler claims Improve consistency in metric management for the organization Improve alignment between internal and external data quality and reporting standards Achieve greater granularity and timeliness in reporting for improved insight and regulatory compliance Increase user sophistication in performance analytics through improved data management and analytic technologies

8 7 Copyright © 2009 Accenture All Rights Reserved. For Underwriting, sophisticated analytics have become table stakes… Marketing Market Penetration Analysis Strategic Demographic Analysis Competitor Analysis Agency/ Distribution Analysis and Performance Measurement Campaign Effectiveness (Affinity, Advertising, Lead Dissemination, etc.) Account Rounding Analysis Customer Value Analysis Sales Agent Performance/ Profitability Analysis NB & Quote Flow Hit and Yield Analysis Cancellation Analysis Retention Analysis in-Force Strategy Agent/Distribution Performance Analysis Channel/Access Method Analysis Agency Management Analysis Sales Tool Efficiency Acquisition Cost Analysis Compensation Analysis Relationship Referral Analysis New Business Sourcing Analysis Cross Sell / Up Sell Analysis Lead Management Product Product Research Market Territory Analysis Market Segment Analysis Competitor Analysis Distributor Analysis Product Design/ Performance Historical Product Analysis Rate Making Analysis Pricing Analysis UW Rule Analysis Loss Experience Analysis Contract/ Forms Analysis Product Launch ROI Analysis Product Launch Analysis Impact/ Disruption Analysis What/If Analysis Regulatory/ Trend Analysis DOI Relationship Analysis Pricing Performance including Tool Usage Predictive Model inputs/outputs/final Predictive Model deviations from Baseline Predictive Model and UW Rule Integrated Analysis Rate Adequacy Analysis Marketplace Analysis Profitability Analysis Loss Ratio Analysis Loss Development and Trending Rate Development and Trending Residual Market Loads (WC) Involuntary Book Analysis Off-Balance Analysis Reserve Analysis Expense Analysis Underwriting UW Productivity UW Expense Analysis SLA Mgmt Appetite Analysis Segmentation Analysis Book Mix Analysis Hit and Yield Analysis Referral Rates Authority Analysis Rule Analysis Price/Credit Analysis Agent Performance by Account/Book UW Service Utilization Market Comparison Analysis Competitor Analysis Quality/Audit Analysis Exposure Management CAT Management Loss Experience Analysis Reinsurance Analysis (Treaty, Fac) Servicing Service Channel Analysis & Optimization Service Channel Segmentation Knowledge Management Content Management Workflow Analysis SLA Mgmt Contact Mgmt Turnaround Times Cycle Times Straight-through processing volume Escalation Analysis Reassignment Analysis Customer Complaint Analysis Policy Error Analysis Span of Control Analysis Self-service Inquiry Analysis Corporate Performance Planning/ Budgeting Growth Analysis Book Mix and Portfolio Analysis Profitability Analysis Marketplace Analysis Competitor Analysis M&A Analysis Performance Management Resource Attrition Investment Analysis Expense Analysis ISO and Bureau Reporting DOI/Statutory Reporting Taxes, Boards & Bureaus Analysis Voluntary /Involuntary Market Analysis Pricing/ Actuarial Claims Claims Assignment and Routing Fraud Detection Formula Based Reserving Reserve Analysis Claims Handling Effectiveness Claims Processing Efficiency Subrogation Analysis

9 8 Copyright © 2009 Accenture All Rights Reserved. Accenture Insurance Analytics Solutions: Predictive Modeling Accenture's initial solutions for Insurance Analytics are focused on Predictive Modeling to help clients leapfrog current approaches with impactful results in the areas of Distribution, Underwriting and Claims. Product Development Marketing & Distribution Pricing & Underwriting Policy Processing Claims Performance Management Enhanced Enterprise and Core Capability Decision Making Pricing Insight & Optimization Distribution Insight & Optimization Predictive Claims Processing Common Deployment Solution

10 9 Copyright © 2009 Accenture All Rights Reserved. “Now, we've actually got predictive modeling tools that allow us to improve the overall quality of our underwriting decisions and improve the consistency of what we're doing from underwriting our book.“ JAMES LEWIS, PRESIDENT AND CEO, CNA PROPERTY & CASUALTY OPERATIONS “Our models categorize the best risks as five diamonds and the worst as one diamond. Early indications for BOP are that loss ratios for five-diamond accounts are three times better than those of one-diamond accounts, and we're writing significantly less of the one-diamond business.“ DALE THATCHER, EVP, CFO, SELECTIVE INS. GROUP, INC. “It's a data-base industry now, particularly in small face-value kind of business, and so we are very focused on innovation around product, particularly things like predictive modeling.” FRED EPPINGER, CEO, THE HANOVER INSURANCE GROUP, INC “We started with automated underwriting platform or multivariate predictive models with commercial business three - four years ago. Today we have over 80% of our business on an automated underwriting platform. We think we're one of the strongest underwriters in using predictive models here.” MIKE HUGHES, EVP - INSURANCE OPERATIONS, SAFECO CORPORATION “Generation 1” Predictive Models have enabled more precise product pricing at most insurers…

11 10 Copyright © 2009 Accenture All Rights Reserved. NEAR TERMMEDIUM TERMLONG TERM Invest in Modeling technology and resources Establish Internal competence Establish scaleable model deployment architecture Process Foundation Scaleable technology Management process Quality controls Transparency Internal competency Expand Model scope and scale Add LOB’s under model Grow % of premium/ claims under model Complete reference models Build credentialed resource pool across the enterprise Create “Speed to Market’ (reduce analytics cycle time) Create reusable process and technology Management and leadership maturity Quality maturity Pro forma Metrics Baseline Re baseline Metrics Process Optimization Proprietary models Enriched external data sourcing Robust solution integration Automated and integrated model and rule testing Automated model/rule deployment Externalized model/rule maintenance by business Market cycle tolerance Corporate Mission and Objectives Process Mastery Enterprise scope and scale Process and resource stability and alignment Payback on technology investment M a t u r i t y M e t r i c s a n d M i l e s t o n e s WAVE IWAVE IIWAVE III …But additional capabilities are needed to advance Predictive Modeling along the Maturity Curve for increased and durable benefits Off-Line Model Basic Integration Robust Integration

12 11 Copyright © 2009 Accenture All Rights Reserved. Generation 1 Predictive Model Challenges Use of external model vs. proprietary development (dependency on industry results, lack of model transparency) LOB vs. Account/Customer model orientation Lack of model granularity: location, agent/broker, Lack of model integration with other rules (underwriting, rating, forms) Lack of integrated book of business testing facility and “what if” scenario model impact analysis tools Lack of externalized model rule management capabilities in the hands of the business Lack of automated deployment/release management Limited performance insight across models and other rules

13 12 Copyright © 2009 Accenture All Rights Reserved. Accenture’s UW Predictive Model Solution enhances business benefits through effective lifecycle integration

14 13 Copyright © 2009 Accenture All Rights Reserved. This end-to-end UW Predictive Model Solution brings additional cost and growth benefits not achieved with Generation 1 solutions. Loss Ratio Reduction (4-5 pts) Premium Growth (varies by strategy) Expense Reduction (5-15%) Alignment of risk selection and existing book of business with overall risk profile objective Proactive management of non-renewals Risk selection improved through the identification and qualification of relevant predictors of loss Timely cross selling of products Positively influence channel production by improving the carrier/channel interface (easy to work with) Proactive targeting for new business Targeted renewal pricing and improved retention Consistent, error-free selection of individual risks High percentage of underwriting decisions untouched (manual underwriting on an exception basis only) Reduction of data acquisition costs Reduction of administration and selling expenses on a per policy basis UW & Pricing changes quickly brought to market Basic Integration Robust Integration Offline Model * Benefits are further enhanced as predictive modeling is integrated into other business functions Benefit Drivers:

15 14 Copyright © 2009 Accenture All Rights Reserved. Companies are Proactively Advancing their Predictive Modeling Implementations Accenture’s solution enables insurers to industrialize their predictive modeling capabilities throughout the organization. Model Integration Objectives: –Integrate predictive scores into business processing via rules engine configuration and portal/UW/Policy system integration –Develop business rules to provide further guidance & explanation of the scores –Test impact of models on book of business and with other rules –Deploy models/rules into run-time environment Model Development Objectives: –Identify data attributes that are predictive of the outcome being measured (e.g., Loss) –Derive the most reliable algorithm to calculate a predictive score, based on extensive analysis of historical data Model Refinement & Extension Objectives: –Establish timely operational data extracts for real-time performance insights –Establish flexible platform that externalizes model/rule maintenance to rapidly change rules, explanations, and predictive scores –Extend predictive score information into broader areas (servicing, sales)

16 15 Copyright © 2009 Accenture All Rights Reserved. Accenture’s Differentiated Answers Common Challenges Scoring Engine not flexible enough to adapt to new model structures 9 – 18 months lead time to integrate new model structures Results with Accenture Flexible Scoring Engine requires minimal or no new development to adapt to new model structures 2 – 6 months lead time to integrate new model structures ??? GO New Predictive Model Scoring Specifications Scoring Engine Wrapper (SOA) Automated Model Deployment Utilities Exceptions? Rules Process Rules & Process Yes No Rules Engine (Blaze,iLOG) Rules-based scoring engineRobust model deployment architectureConfigurable Scoring Process Architecture Accenture Assets: Solution Approach Options & Reference Architectures Onshore & Offshore Blaze Advisor & iLOG Scoring & Rules Engine Implementation Skills Blaze & iLOG Scoring Engine Software Accelerators Model Deployment Utilities SOA Scoring Engine Wrapper Software Accelerators Accenture Enables Speed to Integrate New Models

17 16 Copyright © 2009 Accenture All Rights Reserved. Common Challenges 3rd Party Data not cleanly matched to internal data Data refresh, scrubbing, matching, storage, and utilization options difficult to navigate Results with Accenture Cohesive data integration & utilization Automated matching with configurable accuracy/exception thresholds Thoughtful, guided decisions on all key points of consideration Accenture’s Differentiated Answers Integrated (real-time and batch) 3rd Party Data Sophisticated data matching Cohesive data utilization Accenture Assets: Data integration / utilization options & best practices Proprietary Data Matching Software Accelerators Onshore & Offshore 3 rd Party Data integration Skills Third Party Data Service Provider Alliances ??? GO Internal Data 3 rd Party Overlay 3 rd Party Extensions Customer Internal Data 3 rd Party Overlay 3 rd Party Extensions Policy 3rd Party Data Integration Batch Interface Real-Time Interface Data Utilization 3rd Party Data Source A 3rd Party Data Source A 3rd Party Data Source A 3rd Party Source A 3rd Party Source B 3rd Party Source C 3rd Party Source D 3rd Party Source E 3rd Party Source F Thresholds Data Matching T T T T T T Accenture Enables Efficient Use of 3rd Party Data

18 17 Copyright © 2009 Accenture All Rights Reserved. Common Challenges Rule requirements difficult to crystallize Rules architecture not well defined Rules quality/completeness not validated Rating & UW rules not well aligned to leverage Predictive Model Scoring Results with Accenture Clear, timely & consistent rule requirements Flexible & effective rules architecture established quickly Rules consistently managed to the highest quality throughout their lifecycle Accenture’s Differentiated Answers Proven rule requirements process & templates Robust rules architecture Comprehensive rules management framework Accenture Assets: Innovative rules architecture point of view Industry leading rules management framework Rule requirements templates & best practices Onshore rule requirements gathering skills ??? GO Accenture Enables Effectiveness of Business Rules

19 18 Copyright © 2009 Accenture All Rights Reserved. Common Challenges Business process & organization changes not aligned with either the model design or integration, and are difficult to agree upon Timely and appropriate communications & training are lacking Results with Accenture Business changes clearly aligned with both model design and integration via structured collaboration throughout implementation Leadership & key stakeholders are informed & aligned from early on Accenture’s Differentiated Answers Broad communications plan developed and vetted early with key stakeholders Process & organization changes, training and performance management developed collaboratively to align with industry best practices and unique solution aspects Accenture Assets: Industry leading Training & Performance Management Practice Process & organizational change reference models Communication plan reference models ??? GO Accenture Enables Adoption of Business Change

20 19 Copyright © 2009 Accenture All Rights Reserved. Accenture has a track record of successful solution implementation with predictive modeling Clear Vision Increased Process Automation Selective Integration Points Frequency and Severity-Based Models Analytics-Directed Appetite Proactive Underwriting Process Changes Coordinated Business Rules Management Keys to Effective Predictive Modeling Solutions Coordinated Delivery Clearly Defined Roll- Out Strategy Cross-Organization Dependency Mgmt. Cross-Organization Training Leadership Backing & Communications Plan Calculated Offshore Delivery Model Parallel Development & Testing Tracks Thoughtful Design Flexible, Rules- Based Solution Scalable SOA Framework Integrated Internal & 3 rd Party Data Robust Management Information Model Change Beta- Testing Platform Model Development Platform

21 20 Copyright © 2009 Accenture All Rights Reserved. Getting Started: Proof of Value Accenture Responsibility Proof of Value Business Case: Develop the business case, capability scope, and high level business and technical requirements –Technical Architecture Assessment –Data Services Survey –Vendor Services analysis Conceptual Design – pro forma design documents to integrate current or updated predictive models with production systems and testing/analytics solutions Client Responsibility Part time support (25% - 50%) from cross-functional team of IT, selected business/functional areas, analytics group and stakeholders Weekly status meetings with key stakeholders to evaluate deliverables and provide direction. Provide current and planned application architecture documentation Provide current and planned data services and model deployment architecture Accenture would work with you to conduct a short project to determine solution approach options and define potential scope and business value for the integrated solution.

22 21 Copyright © 2009 Accenture All Rights Reserved. Proof of Value Approach Model Development Evaluate current predictive models, modeling capabilities and model update cycle NA Integrated Architecture and Data Define business case for Proof of Value Define conceptual Design for Integrated Solution Develop Proof of Value – basic functionality for integrating rates, rules, and predictive models in a test environment Develop Proof of Value – basic functionality for better integration of predictive models with rules engine in a test environment Organizational Readiness Assess Analytics Management Processes and Resources NA Base Case Weeks 1-2Weeks 3-5+

23 22 Copyright © 2009 Accenture All Rights Reserved. Integrated financial data across disparate acquired companies to reduce financial close, consolidation and management reporting times as well as eliminate manual processes. Defined a global management reporting process and developed the EDW solution Developing Center of Excellence for Analytics & Modeling Commercial lines Business Intelligence solution, including data warehouse, product/pricing analytics (SAP/BO) for BOP, Auto, WC, predictive model analytics, operational analytics Enterprise Finance Data Mart Developed forecasting/planning desktop (MSFT BI) Enterprise Data Services including Master Data Management strategy/implementation Data Warehouse/Analytics Enablement of STP for Commercial Lines Installation of Accenture Underwriting and Claims assets Implementation of new Policy system for Personal Lines Data Warehouse implementation including ETL, data prep, and analytical tools Product Development solution leveraging best of breed components, including Blaze Advisor for underwriting and pricing rules and Skywire Insbridge for rating Sample Accenture Credentials Large National Insurance Company Specialty Lines predictive model configuration and integration to fuel lower touch growth model Integrated with policy system and FairIsaac/Blaze rules engine (including other UW rules) Provides guidance messages

24 23 Copyright © 2009 Accenture All Rights Reserved. APPENDIX

25 24 Copyright © 2009 Accenture All Rights Reserved. Insurance Analytics Capability Blueprint Users UW / Product Producer / Cust. / Vendor ClaimsActuary Sales / Service Executive Information Refinement Executive Info Actuarial Info Management UW / Prod / Claims Decision Support Sales / Mkt Decision Support CAT / ExposureGIS Data Access Drill Down Export (Excel / Access) StandardizedReal Time GIS / Mapping Interfacing Source Systems ClaimsPolicyFinancialsLegacy External / 3 rd Party Distribution Mgmt Enabling Capabilities Testing SimulationModeling Report Generation Alerts / Notifications Ad-hocSide-by-SideUser Tailored Batch KPI’s Forecasting Trending Common Data Definitions Analytics Data Layer Data Enrichment (3 rd Party) Scheduling Data Warehouse Data Marts Authority / Security

26 25 Copyright © 2009 Accenture All Rights Reserved. Operational Data Store Data Warehouse Reporting & Analytics Data Warehouse Staging Sources Operational Data Store (ODS) Data Subject Area Data Marts & Cubes Analytical Data Sources Unstructured information landing area Analytical Data Model Repository Master Data Repository (Producer, Product, Policy, Customer, Location) Business Dimensional Views ETL Model Mgt & Applications Data Miner Score/ Rules Engine Dashboards Data Archive Data Quality Metadata Management Metadata Dictionary & Repository MonitorStandardizeMergeMatch ETL Repository BusinessTechnical AuditBalanceControl Extract, Transform, Load (ETL) ETL Our solutions are supported by a target technology architecture that enables speed of predictive solution implementation while positioning the insurer with a strategic infrastructure for enterprise analytics. Producer Policy Claims Underwriting Proprietary & Public Core Transaction Applications/Data External Data Data Miners Under- writers Claims Mgt Product Managers Actuaries Marketing Operations Reporting Insurance Analytics Technology Blueprint

27 26 Copyright © 2009 Accenture All Rights Reserved. Accenture’s Insurance Analytics Offering: Pre-Built Architecture Assets Model Development Model Design Specification – reusable pro forma model designs based on known problem statements (e.g. fraud) Meta Data Dictionary – A reference data specification specific to a model type (e.g. subrogation) that identifies the individual data attributes to be used in Sample Set development ‘Raw Data’ ETL Procedures to populate the model development sample set from the transaction data ‘Raw Data’ data pre-processing routines to populate the model development sample set Derived Predictors – highly predictive features created from raw data inputs, applicable to all sample data sets for a common problem statement Insight Visualizations – typical visualizations for unsupervised/exploratory models that provide insights into unexpected patterns and data clusters Data Quality Reporting Templates – reusable reporting and metrics for data quality relative to usable features for modeling Validation Reporting Templates – reusable report template for validating predictive models (validation approach) Integrated Architecture and Data ‘Raw Data’ ETL Procedures to execute the model algorithm at prediction time with live transaction systems data ‘Raw Data’ data pre-processing routines to execute the model algorithm at prediction time with live transaction systems data Data Services (interfaces) – ACS interfaces to third party data (run-time model execution) Pre-loaded 3 rd Party Data Sources – preloaded tables for external data sources (free public data relevant to Model Design Specifications) Pre-designed 3 rd Party Data Interfaces – common external data interfaces to proprietary data that is typically used to improve the prediction accuracy of models Advanced Model Maintenance Architecture – interface and functionality to dynamically improve models from the transaction data flow Report Designs – business relevant report designs typical for management user needs User Interface Designs – sample user interfaces presenting a likely deployment of model output to the user in the transaction system Rule Designs – typical business rules to interpret and act on prediction outcomes for a model type Organizational Readiness Deployment Strategies, Change Management Approaches PHASES/ WORKSTREAMS DesignBuildTestDeployMaintain Plan Analyze

28 27 Copyright © 2009 Accenture All Rights Reserved. Accenture’s Insurance Analytics Offering: High Value Capabilities and Accelerators Our capabilities and accelerators can reduce the cost and risk of implementing predictive models and analytics across the enterprise for underwriting, claims and distribution operations. Methodology and Tools to accelerate the design, development and deployment of the analytical models Experienced Insurance Analytics and Actuarial services to develop, extend, and enhance the new analytical models Market leading analytics software suite to develop and maintain analytical models Technology expertise to install, configure, and integrate modeling software into the insurer’s current application environment Integration architecture to integrate Accenture’s Insurance assets (ICC, CCS, UWC) with the analytics software suite Robust metadata dictionary based on logical and physical insurance data models to rapidly define required data Data services to automate the connection to 3 rd party data Business solutions that address today’s business problems and also representative of predictive modeling capabilities that can be applied broadly across the enterprise

29 28 Copyright © 2009 Accenture All Rights Reserved. Centralized/Enterprise Services Decentralized/Line of Business Focused Line of Business/ Functional Representation and Demand Management Scope Methods/Standards Strategy Requirements Build Deploy Scope: Strategy Requirements Prioritization Assigning Resources Scope Line of Business and Functional Reporting and Analysis Operationalize analytics Enterprise Analytics Exposure Accumulation Intelligence Market and Competitor Intelligence Distribution Insight Analytics Technology and Data Governance Process Improvement Analytics Product Modeling Customer Insight & Experience Personal Insurance Analytics Business Insurance Analytics Corporate Analytics Claims and Service Analytics UW and Pricing Lines of Business / Functional Leadership Financial Support Functional Analytics (HR, Mrktg, Fin) Competency Centers (Others TBD) ILLUSTRATIVE Integrating analytics into the enterprise requires clear organizational accountability and responsibility.

30 29 Copyright © 2009 Accenture All Rights Reserved. Data – Current and New Claims Client Current New Current New Data Type Data Source

31 30 Copyright © 2009 Accenture All Rights Reserved. Data -- Current and New Carrier Location Current New Current New Data Type Data Source

32 31 Copyright © 2009 Accenture All Rights Reserved. Integration and modeling of this enriched data will enable enhanced statistical analysis and forecasting. Claims Client Carrier Location Current New Benchmark/TrendSource

33 32 Copyright © 2009 Accenture All Rights Reserved. More predictive and optimized analytics will position the insurer to create “game changing” innovations to grow its business. New Product & Service Offerings Enhanced Client Performance Diversified Revenues Increased Market Leverage Optimized Delivery Platforms

34 33 Copyright © 2009 Accenture All Rights Reserved. Big Ideas Differentiated Program Offering Target SICs, geographies, size Tailored coverage forms Proprietary rating/pricing methodology Streamlined application process Affinity/sponsor organization exclusive membership access Special risk engineering services Business Income consultative services (fee-based, sourced through accounting firm network) Preferred market arrangements with carriers, including pre-negotiated servicing terms Proprietary risk scoring tool to gauge loss propensity and risk management leakage Branded marketing material and advertising

35 34 Copyright © 2009 Accenture All Rights Reserved. Big Ideas Client Dynamic Pricing Proprietary client account scoring methodology --- lifetime value, total cost of risk, predictive risk model based on economic conditions as well as loss propensity Real-time benchmarking during proposal/quotation process Optimize profit margin based on trend in quote take-up by risk class, geography, product line and client profile

36 35 Copyright © 2009 Accenture All Rights Reserved. Big Ideas Location Predictive Model Develop proprietary location data warehouse Aggregate Aon client location data with U.S. business census and location/building data from third parties Develop proprietary location scoring methodology Develop proprietary EML/PML scores by LOB, SIC, geography Provide fee-based service for multi-product, multi- peril location modeling Develop new risk management, catastrophe management and business expansion consultative services Partner with third party providers for “hot site” and “cold site” contingent arrangements for clients

Download ppt "Copyright © 2009 Accenture. All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Insurance Analytics High."

Similar presentations

Ads by Google